nanomaterials Review VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials Milena Tomić 1,2 , Milena Šetka 3 , Lukaš Vojkůvka 4 and Stella Vallejos 1,3, * 1 Institute of Microelectronics of Barcelona (IMB-CNM, CSIC), Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, Spain; milena.tomic@imb-cnm.csic.es 2 Department of Electronic Engineering, Autonomous University of Barcelona (UAB), Campus UAB, 08193 Cerdanyola del Vallès, Barcelona, Spain 3 CEITEC—Central European Institute of Technology, Brno University of Technology, 61200 Brno, Czech Republic; milena.setka@ceitec.vutbr.cz 4 Silicon Austria Labs, Microsystem Technologies, High Tech Campus Villach, Europastraβe 12, A-9524 Villach, Austria; Lukas.Vojkuvka@silicon-austria.com * Correspondence: stella.vallejos@imb-cnm.csic.es or vargas@vutbr.cz; Tel.: +34-935947700; Fax: +34-935800267 Abstract: This review summarizes the recent research efforts and developments in nanomaterials for sensing volatile organic compounds (VOCs). The discussion focuses on key materials such as metal oxides (e.g., ZnO, SnO2 , TiO2 WO3 ), conductive polymers (e.g., polypyrrole, polythiophene, poly(3,4- ethylenedioxythiophene)), and carbon-based materials (e.g., graphene, graphene oxide, carbon nanotubes), and their mutual combination due to their representativeness in VOCs sensing. Moreover, it delves into the main characteristics and tuning of these materials to achieve enhanced functionality  (sensitivity, selectivity, speed of response, and stability). The usual synthesis methods and their  advantages towards their integration with microsystems for practical applications are also remarked Citation: Tomić, M.; Šetka, M.; on. The literature survey shows the most successful systems include structured morphologies, Vojkůvka, L.; Vallejos, S. VOCs particularly hierarchical structures at the nanometric scale, with intentionally introduced tunable Sensing by Metal Oxides, Conductive “decorative impurities” or well-defined interfaces forming bilayer structures. These groups of Polymers, and Carbon-Based modified or functionalized structures, in which metal oxides are still the main protagonists either as Materials. Nanomaterials 2021, 11, 552. host or guest elements, have proved improvements in VOCs sensing. The work also identifies the https://doi.org/10.3390/ need to explore new hybrid material combinations, as well as the convenience of incorporating other nano11020552 transducing principles further than resistive that allow the exploitation of mixed output concepts Academic Editor: (e.g., electric, optic, mechanic). Giuseppe Cappelletti Keywords: volatile organic compounds; gas sensors; nanomaterials Received: 23 December 2020 Accepted: 7 February 2021 Published: 22 February 2021 1. Introduction Publisher’s Note: MDPI stays neutral Nanoscaled materials, with sizes within the Debye length of the surface (typically on with regard to jurisdictional claims in the order of 2–100 nm), are used as highly sensitive (receptor) elements in gas/vapor sen- published maps and institutional affil- sors due to their large surface-to-volume-ratio and their proven better chemical, optical, and iations. electrical properties as compared to non-nanoscaled materials. These gas/vapor sensitive materials include usually unmodified or modified metal oxides (MOXs), polymers (POMs), and/or carbon-based materials (CbMs) with varied morphological shapes, integrated into conventional transducers (e.g., resistive, capacitive, gravimetric, or optical). The sensing Copyright: © 2021 by the authors. properties of these materials generally depend on the chemisorption of negatively charged Licensee MDPI, Basel, Switzerland. oxygen adsorbates (O−2 , O− , and O2− ) in air, which, due to charge transfer between the This article is an open access article material and the analytes, change the electron density on the material surface. They may distributed under the terms and also depend on the chemical reaction between the sensitive material and the analyte, or the conditions of the Creative Commons diffusion of species into the bulk of the material [1–10]. Attribution (CC BY) license (https:// Most studies of gas/vapor sensitive materials are focused on the detection of low creativecommons.org/licenses/by/ concentrations of CO, NO2 , O3 , H2 , NH3 , or H2 S due to their toxicity, their relation with 4.0/). Nanomaterials 2021, 11, 552. https://doi.org/10.3390/nano11020552 https://www.mdpi.com/journal/nanomaterials Nanomaterials 2021, 11, 552 2 of 33 atmospheric composition, or the fact that these gases can be found at high levels in certain environments [11–18]. However, in recent years, the application of gas/vapor sensitive materials has been extended to the detection of volatile organic compounds (VOCs), not only because their presence is significant in the industry and domestic sector, but also because of their relevance as markers for (indoor/outdoor) air [19] and food quality [20], and early diagnosis of several diseases [21]. VOCs are organic chemicals that possess a high vapor pressure at room tempera- ture [22]. These compounds are numerous and varied, commonly classified according to their functional group into aliphatic hydrocarbons, simple oxygenated hydrocarbons, halogenated hydrocarbons, carbonyl compounds, aromatic hydrocarbons, etc. [23]. Most applications, in which one or more of these compounds need to be monitored, typically involve a broad background of other vapors and/or gases. For instance, thousands of VOCs have been found in the environment, food products, and/or exhaled breath. There- fore, the selectivity of sensitive materials, or its approximation by means of reducing the cross-sensitivity, is imperative. Table 1 exemplifies the VOCs profile in the field of air quality and breath analysis (note that these profiles contain only the most representative VOCs usually reported in the literature). Table 1. Summary of the volatile organic compounds (VOCs) profiles in air-quality due to anthropogenic emissions in the atmosphere [24] and exhaled breath due to cancer (e.g., lung, breast, gastroesophageal, colorectal, oral cavity, head and neck, etc.) [21]. The presented threshold limit values (TLV) are based on the documentation of the American Conference of Governmental Industrial Hygienists (ACGIH) from 2019 [25]. The concentration range in the exhaled breath (CREB) for the selected gases/vapors, which include the minimum and maximum levels found within healthy and unhealthy subjects, were adopted from the references [26–30]; these references are also summarized in reference [21]. VOCs Monitoring Air Quality Breath Analysis VOCs Representative Representative Profile Vapor Analytes TLV (ppm) VOCs Profile Vapor Analytes CREB (ppb) Methanol 200 TWA Alcohols Ethanol 1000 TWA Methanol 157–344 1-Propanol 200 TWA Alcohols Ethanol 96–2848 Ethers Furan N/A 1-Propanol 4–13 Formaldehyde 0.1 TWA Pentanal 2–7 Aldehydes Acetaldehyde 25 C Aldehydes Heptanal 2–7 Propenal 0.1 C Nonanal 2–107 Ketones Acetone 250 TWA Acetone 2-Butanone 200 TWA 35–1000 Ketones 2-Butanone 0.002–3 Esters Ethylacetate 400 TWA 3-Hydroxy-2- 0.002–0.05 Carboxylic acid Acetic acid 10 TWA Butanone Ethane N/A Anthropogenic VOCs emission Propane N/A Butane 1000 TWA Pentane 1000 TWA Hexane 50 TWA Pentane 2–18 Alkanes Heptane 400 TWA Alkanes 4-Methyloctane 16–19 300 TWA Cancer Octane Nonane 200 TWA Decane N/A Undecane N/A Dodecane N/A Ethylene 200 TWA Alkenes Propylene 500 TWA Butylene 250 TWA Alkenes Isoprene 41–109 1,3-Butadiene 2 TWA Alkynes Acetylene N/A Benzene 0.5 TWA Ethylbenzene 20 TWA Aromatics Styrene 10TWA Ethylbenzene Toluene 20 TWA Aromatics 1–18 Xylene 100 TWA Toluene 1–37 Chloroform 10 TWA Halohydrocarbons Dichloromethane 50 TWA Dichloroethane 100 TWA Chlorobenzene 10 TWA Acetonitrile Other Dimethyl disulfide 20 TWA Limonene 0.5 TWA N/A 8-h time weighted averages (TWA)—the average values of exposure over the course of an 8-h work shift; ceiling values (C)—the exposure limits that should not be exceeded at any time; N/A—data not available. Nanomaterials 2021, 11, 552 3 of 33 The present review focuses on the materials utilized to improve VOCs sensing, paying special attention to properties such as the sensitivity, the response time, and particularly selectivity from a cross-sensitivity point of view. The key materials taken into account in this review are MOXs, POMs, and CbMs and their modifications, with a special effort dedicated to MOXs due to their large presence in the literature, even after several years of research in this field. This can be noticed in Figure 1a, which displays an example of the weight of reports for these materials in the recent five years, as well as the most common unmodified and modified MOXs (Figure 1b). In considering the literature, we have compiled extensive tables with selected sensing data. A schematic view of the criteria used for such selection is depicted in Figure 2. The filtering stages have focused on finding reports in which the targeted materials were tested to various VOCs at the same concentrations and operating temperatures to compare their crossed response. This search sorting has significantly reduced the number of reports as compared to the comprehensive list of available literature, which certainly provides extraordinary examples of materials and their analysis, but often these publications do not cover in depth the test of analytes or in many cases, they present responses to incomparable analytes concentrations or operating temperatures. In summary, the tables provide to the reader a quick visualization of the best responses for a given material and the degree of cross-response recorded at the same operating temperature for similar concentrations of various VOCs. The tables showing functional parameters, although providing quantitative data, are recommended to be used only for qualitative comparison, as the reports do not share generalized protocols for the tests. Thus, the functional outputs are prone to not depend only on the material properties, but also on the conditions used to test the analytes. The review, in addition, provides the most common technological solutions reported in the literature, including material features, synthetic methods, sensing principles, and the key enabling systems for their practical use. We believe this work will allow for identifying at one glance the attributes of most common systems for VOCs sensing, among MOXs, POMs, and CbMs in terms of cross-response amongst selected VOCs; information generally not found in other reviews. Hence, here we present the recent research efforts and developments in materials for VOCs sensing. Figure 1. Materials for VOCs sensing summed up from the Web of Science (WOS) database. The data were collected using “gas sensors + VOCs” as keywords and only journal articles between 2015 and 2020 were taken into account. The materials were generalized as modified metal oxides (MOXs) (e.g., ZnO, SnO2 , WO3 ), carbon-based materials (CbMs) (e.g., graphene, graphene oxide, carbon nanotubes), and polymers (POMs) (e.g., conductive polymers and others such as Poly(methyl methacrylate)—PMMA). (a) Summary of MOXs, POMs, and CbMs, plus their mutual modifications (MOX + MOX, CbM + MOX, POM + MOX, POM + CbM, POM + POM) or metal modifications (M + MOX, M + POM, M + CbM), and other minority materials (e.g., zeolitic imidazolate framework (ZIF) materials, metal dichalcogenides, and perovskites) used for VOCs sensing. (b) List and presence in the literature of the most common VOC sensitive MOXs (pure) and their modifications with metals or another MOX. Notice that the literature provides further examples of single and complex MOXs (based on elements from the transition, post-transition, lanthanide, and alkaline earth groups from the periodic table) than those presented here, but these examples are comparatively less recurrent in VOC sensing. graphene oxide, carbon nanotubes), and polymers (POMs) (e.g., conductive polymers and others such as Poly(methyl methacrylate)—PMMA). (a) Summary of MOXs, POMs, and CbMs, plus their mutual modifications (MOX + MOX, CbM + MOX, POM + MOX, POM + CbM, POM + POM) or metal modifications (M + MOX, M + POM, M + CbM), and other minority materials (e.g., zeolitic imidazolate framework (ZIF) materials, metal dichalcogenides, and perovskites) used for VOCs sensing. (b) List and presence in the literature of the most common VOC sensitive MOXs (pure) and their modifi- cations2021, Nanomaterials with11,metals 552 or another MOX. Notice that the literature provides further examples of single and complex MOXs 4 of 33 (based on elements from the transition, post-transition, lanthanide, and alkaline earth groups from the periodic table) than those presented here, but these examples are comparatively less recurrent in VOC sensing. Figure 2. Figure 2. Schematic Schematic representation representationof ofthe thefiltering filteringstages stagesused usedasascriteria toto criteria search thethe search literature re- literature ports for this review, in particular those displayed in the tables (4 to 9). The stages include a selection reports for this review, in particular those displayed in the tables (4 to 9). The stages include a selection by application, materials, analytes, and number of analytes tested, with the results limiting the data by application, materials, analytes, and number of analytes tested, with the results limiting the data to those reports in which two or more VOCs are evaluated using the same operating temperature to those reports in which two or more VOCs are evaluated using the same operating temperature and concentration for each analyte. and concentration for each analyte. In summary, the tables provide to the reader a quick visualization of the best responses for a given 2. Gas/Vapor Sensitive Materials material and the degree of cross-response recorded at the same operating temperature for similar The gas/vapor concentrations sensitive of various VOCs.receptors The tablesusually showinginclude materials functional withalthough parameters, semiconducting providing properties quantitativesuch data,as MOXs, are POMs, and recommended to beCbMs, although, used only amongst for qualitative them, MOXs comparison, arereports as the the pri- do mary materials not share used protocols generalized for gas/vapor for thesensing. MOXs tests. Thus, work as sensitive the functional materials outputs are prone to due notto their depend properties only on theto adsorbproperties, material gas/vapor butchemicals also on theand transduce conditions usedthem into to test theconductivity changes. analytes. The review, in addition, These provideswere properties the most common exploited fortechnological the first time solutions in the 60sreported in the literature, by Seiyama including et al. to fabricate material ZnO features, based synthetic methods, chemoresistive sensing[31]. gas sensors principles, Sinceand the key then, enabling a vast systems number for theirhave of MOXs prac- tical use. We believe this work will allow for identifying at one glance the attributes been studied and addressed to enhance their sensing features, taking into account their of most common systems for VOCs(band electro-physical sensing, among gap, MOXs, POMs, and electroconductivity, CbMs type in terms of cross-response of conductivity, amongst oxygen diffusion), thermodynamic, surface, electronic, structural, and catalytic properties [3,32]. Thus, the literature [33] demonstrated that MOXs with large band gaps (e.g., between 2 and 4 eV) and low activation energy of the centers are generally an optimal combination for chemore- sistive sensors. In addition, the studies on various n- and p-type MOXs pointed out that the most effective and widely used MOXs are those with n-type conductivity, such as ZnO, SnO2 , and WO3 [33,34]. Certainly, this is consistent with Figure 1b, which suggests that these three materials are amongst the dominant gas/vapor receptors in the literature. ZnO, for instance, is an n-type semiconductor with a direct band gap of 3.37 eV [35] which has shown large surface-to-volume ratio morphologies that improved sensitivity and response speed with respect to other morphologies with bulky characteristics. This material showed especially good sensitivity to methanol [36], ethanol [37,38], chlorophenol [36], formalde- hyde [39], and acetaldehyde [39,40]. The wide use of ZnO in the literature is probably related to its relatively facile synthesis processes and high chemical stability [41]. SnO2 is another n-type gas/vapor sensitive MOX with wide band gap of 3.6 eV [42]. This material has been used intensively for studying the influence of grain size on sensor response [2,43], the conductivity of MOX gas sensors [1], or the key surface properties for gas sensing [3], amongst others. SnO2 has shown good mechanical stability, high conductivity, wide op- erating temperature range (200–600 ◦ C) [44] and excellent performance in the detection of ethanol [45], butanol [46], acetone [45], and toluene [47]. Other important gas/vapor sensitive MOXs include WO3 , In2 O3 , TiO2 , and Fe2 O3 . Specifically, WO3 showed high sensitivity to VOCs such as acetone [48], methanol [48], and toluene [49]. Moreover, In2 O3 exhibited very high responses to acetone and toluene with a detection limit of 500 ppb [50] and TiO2 demonstrated very promising results in acetone [51] and hydrogen [52] detection, while Fe2 O3 nanoparticles were used for the decoration of common MOXs (e.g., WO3 or SiO2 ) showing high sensitivity to toluene [49] and ethylacetate [53]. Nanomaterials 2021, 11, 552 5 of 33 POMs are organic materials usually belonging to the category of insulators, as they possess covalent bonds without free movable charge carriers (electrons or ions), although within POMs there is a group of conductive polymers, which behave similarly to semicon- ductor MOXs. Their conductivity originates from highly delocalized electrons between the conjugated polymer backbone (alternating single bonds (σ) and double bonds (π)) and the dopant (negatively charged species). When conductive polymers are exposed to the gas/vapor, these materials undergo oxidation and reduction processes as a function of the dopants (p- or n-type) [4]. During the oxidation, electrons are extracted from the highest occupied molecular orbital (HOMO) of the valence band, whereas during the reduction, electrons are transferred to the lowest unoccupied molecular orbital (LUMO) of the conduction band. The charge carriers in the form of polarons, bipolarons, or an equilibrium between both are created during the redox processes [5,6]. This charge transfer is generally transduced in conductivity changes that enable the gas/vapor detection even at room temperature, circumventing the need for thermal activation as in MOXs. Con- ductive polymers such as polypyrrole (PPy), polyaniline (PANI), polythiophene (PTh), and poly(3,4-ethylenedioxythiophene) (PEDOT), and their modifications with metals have been the most studied candidates for sensing VOCs [4,7] (Figure 1a). These materials demonstrated mostly resistive responses to vapors such as ethanol, methanol, and acetone, and gases such as NH3 , NO2 , and CO [5]. Macrocyclic compounds such as cyclodextrins (CDs), calixarenes, and cavitands have also been investigated due to the presence of cavities with molecular dimensions, which can act as molecular receptors [8]. CbMs include mainly carbon nanotubes (CNTs), graphene oxide (GO), or reduced graphene oxide (rGO). Like other gas-sensitive materials, it is widely accepted that the charge transfer between gas/vapor species and CbMs is responsible for the conductiv- ity changes of these materials and thus gas/vapor detection. Due to the typical two- dimensional (2D) single-atom-thick structure of graphene, GO and rGO, these materials are prone to change the local carrier concentration electron by electron providing these structures advantages over CNTs and other MOXs and POMs [9,10]. Overall, the gas sens- ing properties of CbMs are strongly connected with the presence of defects and residual contaminants at the surface. Hence, for practical applications, the sensitivity of CbMs is usually enhanced by modifying or functionalizing them with metals [54,55], MOXs [56], or POMs [57,58], as can be seen in Figure 1a. Overall, the key sensing features pursued in gas/vapor sensitive materials include sensitivity, selectivity, stability, and speed of response; features known as the four “S”s of gas sensing. Moreover, these materials are expected to interact reversibly and reproducibly with the specific analyte of interest showing strong stability in harsh conditions such as high temperature and/or high relative ambient humidity. From the economical point of view, the sensitive material should be widely available, simply and inexpensively synthesized, compatible, and easily implemented with specific transducing platforms. Table 2 presents a summary of the possible advantages and limits of these materials in their unmodified form. This table is intended to provide only a qualitative view, the quantitative evaluation for these materials is provided in the next sections. Despite the great efforts invested in solving the mentioned limitations for each material type, and the large amount of literature showing important improvements, there is still a continuous demand in the gas sensor field for enhanced materials properties. The pursued improvements are especially focused on finding materials with more sensitivity and selectivity to VOCs from the aliphatic and aromatic hydrocarbons, and carbonyl groups, rather than just those from the simple oxygenated hydrocarbons group, as well as lower detection limits that reach ppb-ppt levels. The demands also emphasize the search for scalable synthetic methods for on-chip integration and low operational power consumption. Currently, the most common strategies to pursue these improvements at the material level generally involve tailoring of the material by: • Tuning the surface morphology: shape, size, and dimensional control to obtain 0D (e.g., nanoparticles and quantum dots), 1D (e.g., nanofibers, nanotubes, and nanowires), Nanomaterials 2021, 11, 552 6 of 33 2D (e.g., thin films, nanosheets, and nanoplates), and 3D (e.g., porous films and nanoflowers, which consist of 2D nanosheets) materials. • Modifying or functionalizing the material: control of type and level of intentional impurities (doping, formation of Schottky barriers, heterojunctions, and/or inor- ganic/organic hybrid structures). Table 2. Comparative table showing the main properties of MOXs, POMs, and CbMs. The symbols attached to each qualitative estimation remark the degree, major (↑) or minor (↓), of each characteristic with respect to the other groups of materials. Properties MOXs POMs CbMs Notes Advantageous for all materials to specific gases/vapors, Sensitivity High↑↑ High↑ High although in major degree for MOXs with respect to POMs and CbMs. Poor for all materials within a large number of gases/vapors. Selectivity Poor↓ Poor Poor POMs and CbMs have shown advantages for specific gases such as NH3 and NO2 . More advantageous for MOXs due to operation at high Stability High Medium↓ Medium temperatures. The stability of POMs is highly dependent on humidity. More advantageous for MOXs due to operation at high Speed of response High Low Low temperatures. More advantageous for MOXs due to low probability of Long-lasting High Low↓ Low poisoning and degradation. POMs have more probability of lifetime degradations in humid ambient with respect to CbMs. Operating More advantageous for POMs and CbMs due to the capacity High Low Low temperature of adsorption at room temperature. Energy More advantageous for POMs and CbMs as they do not High Low Low consumption require thermal activation. More advantageous for MOXs and POMs due to the wider Cost Low↓ Low High choice of synthesis methods. Advantageous for all materials according to the transducing principle. MOXs are more advantageous in this area due to their proved compatibility with micro/nano fabrication Miniaturization High↑ High High processes, although the recent scalable integration methods potential based on roll-to-roll and printed electronics are facilitating more the scalable integration of POMs and CbMs into miniaturized systems. 3. Tailoring Materials for Enhanced Sensing Properties to VOCs 3.1. Surface Area—The Larger, the Better Several works in the literature have stated the correlation between the sensing per- formances, morphology, microstructure, and size of gas/vapor sensitive materials [59–64]. In this context, various materials with morphological shapes in the nanometric scale with increased surface-to-volume ratio have proven to be more effective for gas detection [46,47]. Generally, the surface properties of these peculiar morphologies, including crystalline and structural properties, have been shown to determine the sensing activity of these materials. More precisely, the density of states at the surface has been revealed to play an important role in the sensing mechanism of gases and vapors dominated by the space- charge-controlled grain-boundary conduction model, in which the grain sizes, surface chemical states, and gas/vapor adsorption amount determine the overall response of the sensors [1]. Table 3 displays a collection of low dimensional and 3D nanomaterials that have proved greatly improved functionality compared to bulk materials for VOCs sensing. Nanomaterials 2021, 11, 552 7 of 33 Examples of the appearance of selected morphologies are also displayed in Figure 3. Certainly, MOXs, and in particular SnO2 , have been the primary reference materials for determining the influence of surface area (particle size) on gas sensing properties in the past [2,43]. The literature related to VOCs, for instance, shows that SnO2 nanoparticles with reduced crystalline size (from 9.1 to 6.1 nm) show higher and faster response to butanol [46] and that non-commercial nanoparticles can be improved for obtaining better responses to toluene [47] (Table 4). Similarly, other comparative studies of sensors based on complex structures with flower-like hierarchical porous single-crystalline ZnO nanosheets and commercial ZnO powder demonstrated for the former three-times better responses to ethanol with nearly 40% faster response [37]. This fact brought attention to further studies, which focused on controlling the features of various ZnO nanostructures [40]. The results demonstrated enhanced sensitivity to acetaldehyde in the sub-ppm range (50 ppb) when tuning the morphology into nanorings shape instead of nanoparticles, nanorods, or nanotubes. Table 3. Summary of VOCs sensitive materials reported in the literature according to their dimensionality and/or morphology. Materials Morphology Features Ref. Particles Diameters of 1–100 nm [45,48,49,52,65–68] 0D Cubes Average size of 200 nm [69] Hollow spheres Diameter of 72 nm [70] Rods and needles Diameters of 100–200 nm and lengths up to 10,000 nm [48,49,52,71] 1D Diameters of 120 nm (inner) and 180 nm (outer) and Rings [40] lengths of 1000–1500 nm Size of 100–400 nm or as large as 30–70 µm and 2D Sheets and monolayers [57,72] thickness of 10–60 nm Porous films Pore size of 6–150 nm [36,38,45,46,73] 3D Flowers Sheet size of 120–2000 nm and thickness of 12–50 nm [37,39,47,50,51,74,75] Tetrapods Rod diameters of 50 nm and lengths of 1000 nm [76] Figure 3. SEM images of MOX nanostructures with different morphologies and dimensionali- ties (a) 0D—cubes, (b) 1D—needles, (c) 2D—sheets, and (d) 3D—flowers. Adapted from refer- ences [39,69,75], with permission from the American Chemical Society, 2014, and Elsevier B.V., 2013 and 2016, respectively. Other studies discussed the impact of the dimensionality of TiO2 nanostructures on the gas sensing performance. For instance, hierarchical 3D TiO2 nanoflowers [51] showed enhanced properties towards acetone at room temperature with respect to TiO2 Nanomaterials 2021, 11, 552 8 of 33 nanoparticles [77]. The use of hierarchical flower-like CuO nanostructures is another example of complex 3D-shaped MOX [74], which has reported excellent results towards ethanol, formaldehyde, acetone, and xylene for concentrations as low as 20 ppb and yet with fast response (11.2 s) and short recovery time (8.4 s). Novel MOXs based on highly porous Co3 O4 concave nanocubes with high surface area (120.9 m2 g−1 ) have also proven fast response and recovery time below 10 s to 10 ppm of ethanol [69]. Similarly, Co3 O4 in the form of monolayer has shown high responses to ethanol and xylene [72]. Other complex structures, for instance, nanosheet-based 3D hierarchical rosette-like In2 O3 microspheres in their hollow and full form, also showed promising properties to low (500 ppb) acetone and toluene concentrations [50]. Recently, a new study [76] reported the use of ZnO nanotetrapods, consisting of four cylindrical nanorod legs, which showed good results in the detection of VOCs, especially ethanol. Although there is no general rule to state a better sensing functionality for one or another morphological dimension [78], the literature compiled for this review suggests the 3D morphology to be superior in gas/vapor sensing compared to 0D, 1D, and 2D. It is worth bearing in mind that 3D structures are assembled from low dimensional (0D, 1D, or 2D) nano-building blocks and therefore further improvements might be related to synergistic effects [79]. The apparent improved behavior in 3D structures could also relate to their particular surface defects due to their porous and irregular arrangement. In addition, the interconnected structure, usually present in 3D-based films, seems to provide further advantages, especially in resistive-based sensors (the most representative in the literature of nanomaterial-based sensors) as it favors the electrical current percolation [80,81]. This is the opposite for other types of structures, for instance, those based on vertically aligned 1D structures. Thus, the selection of the materials by their dimension should not be associated exclusively with the surface-to-volume-ratio feature, but rather with the employed working principle and the targeted transducing platform. The reports in the literature are not limited only to single oxides; the morphology control of multicomponent oxides as well as the chemical composition for sensing VOCs have also shown to improve the sensing performance. For example, large surface nonstoi- chiometric Co-rich ZnCo2 O4 in the form of hollow nanospheres reported high sensitivity to formaldehyde with superior experimental and estimated detection limits of 13 ppb and 2 ppb, respectively, as compared to other reports in the literature [70]. POMs and CbMs with large surface areas are also pursued to enhance VOCs detection. However, in the gas/vapor sensing literature, there is a lack of information about the size influence on POMS and CbMs as frequently the reports regard the functionalization effect on these materials, which indeed have proven essential to promoting VOCs sensitivity; this is further discussed in the next section. Most of the reports on pristine POMs and CbMs state high sensitivity to gases such as NOx and NH3 , and nearly insensitivity to VOCs especially from the aromatic family [4,57,82–84]. Among the few examples of VOCs sensing with pristine CbMs materials (Table 4), we found that using pristine single-walled carbon nanotubes (SWCNTs) instead of multi-walled carbon nanotubes (MWCNTs) might be favorable to detect toluene at room temperature [85]. Besides MOX- and GO-based 2D materials, it is important to mention other emerging materials with the same dimensionality that have a significant role in VOCs detection, although not the scope of this review. Among them are 2D transition metal dichalcogenides (TMDs) with their unique physical and chemical properties, such as a high surface-to- volume ratio, large number of active sites for redox reactions, high surface reactivity, and high absorption coefficient [86]. These materials have the formula MX2 , where M stands for transition metal element (e.g., Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re, etc.) and X represents a chalcogen (Se, S, or Te) [87]. Some of the most common TMDs for VOCs detection are MoS2 , WS2 , MoSe2 , WSe2 , and SnS2 [86,88–91]. Nanomaterials 2021, 11, 552 9 of 33 Table 4. Sensing properties of pristine gas sensitive materials to VOCs. Material Morphology Size, nm VOCs c, ppm T, ◦ C R tR , s tr , s Ref. NPs 9.1 CS 150 190 9 N/A SnO2 Butanol 10 [46] NPs 6.1 CS 150 630 11 N/A NPs 6.3 CS 260 9.7 3 10 SnO2 Toluene 100 [47] NPs com N/A 240 6.2 N/A N/A NPs com 10–100 Ø N/A 2.6 16 100 ZnO Ethanol 100 [37] NShs 1000 T N/A 8.5 10 80 50 Ø 1000 ZnO NTPs (NRds) L Ethanol 500 340 57 50 70 [76] NPs N/A 220 2 N/A N/A ZnO Acetaldehyde 0.05 [40] NRgs N/A 220 1.1 N/A N/A NPs 10 Ø 200 400 7.8 240 N/A [77] TiO2 Acetone 120 L NShs 700 60 1.7 10 45 [51] 12–23 T CuO NShs 50 T Ethanol 0.2 250 2.1 11.2 8.4 [74] 2000 50 S cNCs T 10 300 1.7 <10 <10 [69] Co3 O4 Ethanol ML 17.6 CS 5 200 114 N/A N/A [72] NShs-hMSp 20 T 350 1.8 4 10 In2 O3 Acetone 5 [50] NShs-MSp 20 T 350 1.7 4 10 Co3 O4 HSps 225 1.3 N/A N/A Formaldehyde 1 [70] ZnCo2 O4 HSps 72 Ø 225 7.3 149 497 SWCNTs SWCNTs 6 Ø 2000 L RT 7.5 * N/A N/A Toluene N/A [85] 20 Ø 4000 MWCNTs MWCNTs L RT 2.0 * N/A N/A NPs: nanoparticles, com : commercial powder, NShs: nanosheets, NTP: nanotetrapods, NRds: nanorods, NRgs: nanorings, cNCs: concave nanocubes, ML: monolayer, hMSp: hierarchical microsphere, MSp: microsphere, HSps: hollow spheres, SWCNTs: single-walled carbon nanotubes, MWCNTs: multi-walled carbon nanotubes, CS : crystal size, N/A: data not available, Ø : diameter, T : thickness, L : length, S : size, RT: room temperature, c: concentration, T: operating temperature, R: response defined as Ra /Rg , tR : response time, tr : recovery time, and * sensitivity (kHz/ppm). Within the novel 2D materials, transition metal carbides/nitrides (MXenes) represent another important group. These materials have stable and easily tunable microstructure, high electrical conductivity, large chemically active surface, and adjustable hydrophilicity, which makes them good candidates for VOCs monitoring [86]. The most studied MXenes for VOCs detection are Ti3 C2 Tx, V2 CTx , Mo2 C, and Ti2 CO2 [92,93]. Another noteworthy 2D material is boron nitride (BN). The high sensitivity reported for BN toward VOCs is related to localized charges induced by the presence of B and N atoms [94]. Amongst 2D materials, there are also those that are constituted of just one atomic species such as one layer thick black phosphorus (phosphorene), silicon (silicene), or germanium (germanene) [95]. Black phosphorus (BP) is considered to be a promising alternative [96,97], since compared to graphene and TMDs, BP possesses a higher surface-to-volume ratio due to its puckered honeycomb lattice structure [98,99]. 3.2. Modification or Functionalization—Pushing the Performance Further Another strategy commonly used to improve further the sensing properties of materials towards VOCs consists in the incorporation of intentional “impurities” at/in the material surface/structure. This modification or functionalization process includes the so-called: • Decorated materials, when incorporating low amounts of noble metals or secondary materials (e.g., MOXs, POMs, CbMs) at the surface. These are represented by an “@” sign in between the guest and host material, e.g., Au@WO3 . Figure 4 displays examples of the decoration of WO3 nanowires with gold, platinum, or iron oxide. Nanomaterials 2021, 11, 552 10 of 33 • Simple mixtures, when mixing two or more gas sensitive materials randomly. These are represented by a hyphen “-“ sign in between the constituents, e.g., ZnO-CuO. • Bilayers and trilayers, when there is a well-defined partition or interface between the two or three gas sensitive materials. These are represented by a slash “/” sign in between the constituents, e.g., CuO/SnO2 and GO/ZnO/GO. • Doped materials, when incorporating “guest” atoms into the material structure, e.g., Ce-doped SnO2 . Figure 4. TEM images of (a) unmodified (high-resolution TEM) WO3 nanowire and modified (scanning TEM) with (b) Au, (c) Pt, and (d) Fe2 O3 nanoparticles. Hereafter, the review differentiates the functionalized materials by the nomenclatures exposed above. The enhanced sensing performance of these functionalized materials has been attributed to electronic effects (band bending due to Fermi level equilibration, charge carrier separation, tuning of the depletion layer, and increased interfacial potential barrier energy) and/or chemical effects (decrease in activation energy, targeted catalytic activity, and synergistic surface reactions). The functionalization in terms of doping and decoration by forming nano-Schottky barriers or heterojunctions has been discussed broadly in previ- ous studies [32,100]. Results in the literature [60,101] show that functionalized materials improve further the sensitivity, response time, working temperature, and/or selectivity to some extent by reducing the cross-responses to different gas or vapor molecules. In this work, we bring attention to the “decorative” and doping materials that have improved MOXs, POMs, and CbMs based sensors towards VOCs detection (Table 5). The number of entries in Table 5 demonstrates that the decoration of popular gas- sensitive MOXs (SnO2 , ZnO, WO3 ) with noble metals (e.g., Au, Pt, Pd) or secondary oxides (ZnO, TiO2 , CuO, Fe2 O3 ) is a generalized method for improving the sensing performance of MOXs towards VOCs. For instance, research on the load variations of Au at ZnO revealed the dependence of the response on Au concentration. Results showed that Au-loaded ZnO has better responses to various VOCs, including diethylether, acetone, chlorobenzene, trichloroethylene, isoprene, ethylacetate, butylacetate, formaldehyde, and hexaldehyde. The 3 at.% Au@ZnO systems improved the response to isoprene, while the 5 at.% Au@ZnO to methanol [65]. Similarly, investigations on Ce- and Mg-doped SnO2 found better sensing performance to formaldehyde, methanol, ethanol, and acetone for 2% Ce-doped SnO2 and ethanol, toluene, and acetone for 1 at.% Mg-doped SnO2 as compared to unmodified SnO2 [73]. Further, the decoration of TiO2 nanorods with 12 at.% of Pd showed enhanced responses to isopropanol as well as a clear improvement to hydrogen, which registers 35 times better response for the Pd@TiO2 systems than for the unmodified TiO2 nanorods [52]. Nanomaterials 2021, 11, 552 11 of 33 Table 5. Sensing properties of modified and unmodified gas sensitive materials to VOCs. Material Loading VOCs c, ppm T, ◦ C R tR , s tr , s Ref. Au@ZnO Au 3 at.% Isoprene 31 N/A N/A 50 360 [65] ZnO Au 0 at.% 25 N/A N/A Au@ZnO Au 5 at.% 21 4 3 ZnO Au 0 at.% Methanol 50 360 4.8 [65] N/A N/A Ce-doped SnO2 Ce 2 at.% Formaldehyde 10 8 4 SnO2 Ce 0 at.% 500 320 28 10 5 [73] Mg-doped SnO2 Mg 1 at.% 14 143 N/A Ethanol 80 160 [103] SnO2 Mg 0 at.% 1 N/A N/A Pd@TiO2 Pd 12 at.% Isopropanol 4.4 N/A N/A 5000 200 [52] TiO2 Pd 0 at.% 1.6 N/A N/A Fe2 O3 @WO3 Fe 3.9 at.% 8 150 15 Pt@WO3 Pt 3.7 at.% Toluene 100 220 7.8 100 30 [49] WO3 Fe & Pt 0 at.% 2.5 400 170 CuO/SnO2 CuO 3 mol% Xylene 10 N/A N/A 50 280 [45] SnO2 CuO 0 mol% 2.5 N/A N/A Cu 65 at.% ZnO-CuO Zn 35 at.% Acetone 10 300 1.2 22 26 [75] TiO2 /SnO2 N/A 13,000 * N/A N/A Benzene 100 220 [102] SnO2 N/A 5000 * N/A N/A Pt@MWCNTs Pt 1.8 at.% 1.01 N/A N/A Pd@MWCNTs Pd 2.1 at.% Methanol 11 RT 1.01 N/A N/A [54] MWCNTs O plasma treat. 1.01 N/A N/A FeO-MWCNTs Fe 2 at.% 1.02 489 N/A Toluene 5.8 RT [104] MWCNTs O plasma treat. 1.07 457 N/A NiO/MWCNTs N/A 500 180 3 27 100 87 100 [105] Ethanol NiO N/A 1.5 N/A N/A ZnO/MWCNTs 0.1 wt.% CNTs 62 41 90 ZnO 0 wt.% CNTs Acetone 320 270 7 79 108 [106] Ag/PPy 1:10 v.r. 5 kHz 150 230 Acetone 5 RT [107] PPy N/A 3 kHz 160 240 CdTe/PPy 0.03 at.% Cd Acetone 5 RT 4 kHz 155 270 [108] PPy N/A 3.5 kHz 165 290 Ti-PTFE N/A Acetone 2000 RT 1.03 N/A N/A [109] PEDOT-PSS/GO GO 0.04 wt.% 11 3.2 16 Methanol 35 RT [57] PEDOT-PSS GO 0 wt.% 1 N/A N/A PANI N/A Benzene 150 RT 45.65 NCC% N/A N/A [68] PANI-CD N/A 57.66 NCC% PANI N/A Toluene 46 RT 39.36 NCC% N/A N/A [68] PANI-CD N/A 40.68 NCC% Ag/SnO2 /GO N/A Acetone 150 300 1.26 2 8 [110] SnO2 /GO N/A 1.05 3 50 GO/ZnO N/A 7 <14 N/A GO/ZnO/GO N/A Acetone 200 450 7 <12 N/A [111] ZnO N/A 12 <13 N/A 23.7 at.% C, 38.4 rGO-SnO2 at.% Sn, 37.9 at.% O 100 300 70 11 N/A Ethanol [112] 14.6 at.% C, 45.2 SnO2 at.% Sn, 40.2 at.% O 100 300 62 9 N/A MWCNTs: multi-walled carbon nanotubes, PPy: polypyrrole, PTFE: polytetrafluoroethylene, PEDOT: poly(3,4-ethylenedioxythiophene), PSS: poly(styrenesulfonate), PANI: polyaniline, CD: cyclodextrin, GO: graphene oxide, rGO: reduced graphene oxide, v.r.: volumetric ratio, N/A: data not available, * CTL sensors: the response represents the relative CTL intensity, 100 : response and recovery time for 100 ppm, kHz : SAW sensors: frequency shift, NCC% : sensor efficiencies in%, RT: room temperature, c: concentration, T: operating temperature, R: response defined as Ra /Rg , tR : response time, and tr: recovery time. The functionalization of MOXs with secondary MOXs, instead of noble metals, also revealed further advantages. For instance, the sensing properties of WO3 nanowires decorated with Fe2 O3 nanoparticles to toluene proved to be comparable to Pt@WO3 nanowires, indicating that the use of expensive precious metals for MOXs functionalization could be circumvented [48,49]. Other examples of secondary MOXs include CuO, which was employed to improve the sensing properties of ZnO to acetone [75] and SnO2 to xylene [45]. This type of modified system (i.e., MOX/MOX) also showed advantages in cataluminescence (CTL) gas/vapor sensors. Thus, TiO2 /SnO2 exhibited better CTL Nanomaterials 2021, 11, 552 12 of 33 properties including stable intensity, high signal/noise values, short response, and better sensitivity to benzene and toluene compared to intrinsic SnO2 [102]. The modification of CbMs and POMs with inorganic materials, mainly MOXs and noble metals for improving their VOCs sensing properties is also frequent in the literature (Table 5). In this context, results on the use of Pt and Pd to decorate MWCNTs are of great interest since these modified materials enabled response to non-aromatic VOCs, in partic- ular methanol [54]. Further studies in the same context showed that the array of oxygen treated MWCNTs and FeO decorated MWCNTs allows for distinguishing the presence of aromatic VOCs such as toluene and benzene in a background of non-aromatic VOCs [104]. Another example of CbMs modification involves NiO/MWCNTs [55]. This composite material exhibited higher response than unmodified NiO nanoparticles to VOCs, especially ethanol. Authors attributed these improvements to the increased surface area and extra interfaces in NiO/MWCNTs, which offer more active sites for the adsorption of VOC molecules and facilitate to get over the energy barriers as well as reducing the operating temperature. CbMS have also been used as modifiers, for instance, low amounts (0.1 wt.%) of MWCNTs on ZnO hollow spheres matrix enhanced dramatically the response of ZnO to VOCs, mainly acetone [106]. These experiments also determine that the content of MWC- NTs defines the optimum operating temperature and degree of response enhancement to VOCs, allowing for a modulation of the cross-response among the targeted VOCs. The reports related to POMs modification also point out improved functionality by their combination with other materials. In this line, a study of nanocomposites in which small amounts of Au [113] or Ag [107] were embedded into PPy showed improvement in the sensing properties of PPy to low ethylene and acetone concentrations, respectively. A similar approach also indicated potential advantages on acetone detection for PPy modified with CdTe quantum dots [108] and polytetrafluoroethylene (PTFE) modified with Ti [109]. The modification of PEDOT with a low amount of GO also proved a higher response for PEDOT to methanol at room temperature. Similarly, CD incorporation in PANI enhances the sensor efficiency of pristine PANI to benzene and toluene detection [68]. Further modifications on gas/vapor sensitive materials include the use of multi- component such as Ag/SnO2 /GO [110] or multilayers of GO/ZnO/GO [111], although their performances were not proven to represent additional advantages with respect to traditional modified materials with two components. 4. Selectivity—In Search of Specificity Another important consideration in gas/vapor sensitive materials relates to their selectivity. In gas/vapor sensors, the selectivity refers to the ability of the system to identify (discriminate) a particular gas/vapor analyte in a complex mixture, avoiding interfering responses from other analytes, with the ultimate achievement of specificity [114]. However, in practice, a specific response is unrealistic, and thus most of the analysis of gas/vapor sensitive materials deal with selectivity, and usually, straightforward comparative studies of responses to various analytes. Tables 6 and 7 summarize these findings so that the reader can distinguish the degree of cross-response recorded for a given material and group of VOCs in selected reports. VOCs are numerous, varied, and ubiquitous. The list of these compounds amounts to more than 1000 species [115]. However, only a few of these are recurrent in gas sensing studies (e.g., ethanol, methanol, acetone, benzene, toluene, formaldehyde, or acetaldehyde) probably because of their abundance in the industry and the indoor/outdoor environ- ment, and their significance as biomarkers in disease diagnosis, as summarized in Table 1. Therefore, Tables 6 and 7 display a list of over 30 VOCs, which correspond to the usual compounds analyzed by the gas sensors community. The higher data density for ethanol, methanol, and acetone compared to the other compounds evidences the special attention on such vapors. The tables also include other gases of interest (e.g., ammonia, hydrogen, nitrogen dioxide, carbon monoxide, and hydrogen sulfide) commonly used in the cross- response tests performed in the literature. The bottom and top of the table display the Nanomaterials 2021, 11, 552 13 of 33 gas-sensitive materials and the “optimum” operating temperatures for the concentration annotated in each referenced material, respectively. The color codes from blue to red and numbers from 1 to 10 along each column show the tested VOCs and their increasing load of response with an increasing number for each referenced material; note the responses are compared for the same VOCs concentration in each case. The numbers from 1 to 10 are calculated so that the highest gas response (for one specific material) is marked with the number 10, while the responses from other gases are compared to that one. This calculation was repeated for each reference (each column) separately. Thus, for instance, in the first column of Table 6, one can notice that porous ZnO film synthesized by hydrothermal reactions showed the highest responses to methanol (red 10) with a high probability of interference from other VOCs such as 2-chlorophenol (brown 9), acetone (orange 8), and 2-chloroethanol (green 6) and moderate interference from VOCs such as formaldehyde (turquoise 3), and chlorobenzene (blue 1) [36]. It is worth noting that most of the gas-sensitive materials in Tables 6 and 7 rely on the resistive transducing principle (Tables 8 and 9 highlight this in more detail). For those reports using other approaches (e.g., cataluminescence or surface acoustic waves), the discussion includes a specific remark on this aspect. A couple of examples on the cross- response of Au and Ag nanoparticles using localized surface plasmon resonance is also included, due to the potential use of this transducing principle on MOXs, POMs, and CbMs modified with plasmonic nanoparticles. Hereafter, we sum up the VOCs interfering patterns by their functional group classification. • Simple oxygenated hydrocarbons Simple oxygenated hydrocarbons are hydrocarbons that contain oxygen as a part of their chemical structure. These compounds involve species such as alcohols (e.g., ethanol, methanol, butanol, to cite a few), and ethers (e.g., diethylether and tetrahydrofuran). The selectivity to methanol is apparently favored by using Pt-decorated WO3 nanonee- dles [48] and localized surface plasmon resonance (LSPR) Au nanoparticles-based sensors [66], which have shown moderate interference to ethanol with promising low cross-responses to acetone, toluene, and isopropanol. Another possible option for high sensitive methanol materials includes ZnO porous films [36], although acetone and 2-chlorophenol may inter- fere with methanol response. Among organic materials, poly(3,4-ethylenedioxythiophene)- poly(styrenesulfonate) modified with ultralarge graphene oxide (PEDOT-PSS/UL-GO) showed excellent selectivity to methanol in front of vapors such as ethanol, acetone, propanol, toluene, formaldehyde, and water [57]. MWCNTs decorated with Pt or Pd worked successfully for methanol detection too proving low cross-sensitivity to acetone, although with high interference to ethanol [54]. The combination of ZnO hollow spheres with 0.05 wt.% MWCNTs [106] also showed improved selectivity to methanol with moderate interference from ethanol and much lower interference from acetone and ether. Additionally, core-shell hybridized Fe3 O4 magnetic nanoparticles synthesized in the presence of polymerized ionic liquids and modified with poly(3,4-ethylenedioxythiophene) derived from poly(ionic liquid) (PEDOT-PIL@Fe3 O4 ) [116] suggested advantages to discriminate methanol from benzene and toluene, but with high interference from ethanol and acetone. Generally, the interference patterns in Tables 6 and 7 for oxygenated hydrocarbons such as ethanol indicate the best selectivity to this compound with only minor interfer- ences from acetone and toluene by using flower-like hierarchical structures consisting of porous single-crystalline ZnO nanosheets [37], porous Co3 O4 concave nanocubes [69], or ZnO nanotetrapods [76]. Another alternative for ethanol detection, although with moderate interferences from acetone and low interference from isopropanol and toluene, was achieved by electrochemically synthesized ZnO porous films [38]. SnO2 obtained by microwave-assisted approach [45] has shown to be a less discriminating material for ethanol in the presence of acetone. However, this material shows the potential to dis- criminate either ethanol or acetone in the presence of various aromatic compounds (e.g., benzene, toluene, and xylene) and formaldehyde. In contrast, hierarchical flower-like CuO nanostructures [74] have shown high ethanol responses, though with high interferences Nanomaterials 2021, 11, 552 14 of 33 to acetone, xylene, and formaldehyde. CbMs such as MWCNTs decorated with Au also showed a good response to ethanol with possible interference from benzene [55]. The addition of MHDA to this composite (i.e., MWCNTs/Au) improved further the selectivity to ethanol, reducing the interference to benzene. This implies that the functionalization with MHDA improves discrimination by boosting sensitivity to non-aromatic VOCs and suppressing the response to aromatic VOCs [55]. The use of composites formed by rGO and SnO2 also proved to be appropriate to distinguish ethanol among aromatic VOCs, such as benzene, toluene, and xylene with moderate interferences from methanol [112]. The reports in Tables 6 and 7 also show that the detection of propanol has been favored by the use of modified materials such as Ag-BC-bsh [117] and Ni/MWCNTs [105]. However, these materials present high cross-responses with other oxygenated hydrocarbons and eventually with ammonia. Further studies suggest the selectivity to butanol among ethanol, methanol, acetone, benzene, toluene, and formaldehyde by using mesoporous worm-like SnO2 films [46]. Similarly, an improved selectivity to pentanol with respect to octane and toluene could be achieved by Ag nanoparticles with LSPR, bearing in mind possible high cross-sensitivity to xylene [67]. Improved properties to ether were achieved using La2 O3 and SiO2 /Fe3 O4 nanopar- ticles, both indicating very low interferences with tetrahydrofuran, alcohols, acetone, benzene, aldehydes, and halogenated hydrocarbons [53]. Results for diethylether also suggested good selective detection of this VOC among acetone, heptane, decane, benzene, toluene, and xylene, with partial selectivity to methanol, hexaldehyde, chlorobenzene, and high interferences to isoprene and ethylacetate. • Aliphatic hydrocarbons Aliphatic hydrocarbons are present in several environmental and industrial sectors, with methane as the most representative compound among them. The track of results on aliphatic hydrocarbons using MOXs, POMs, or CbMs is not extensive, most likely influenced by the low sensitivity often reported for semiconducting materials to these compounds. This fact is visible in Tables 6 and 7 that display a low amount of reports, in which aliphatic compounds registered higher responses in respect to other VOCs or toxic gases. Among the few reports, one can see a relatively good selectivity to isoprene by using porous single-crystalline ZnO nanoplates decorated with Au NPs [65]. These records suggest low interferences of isoprene with heptane, decane, acetone, formaldehyde, trichloroethylene, and aromatic compounds including benzene, toluene, and xylene, as well as high interferences, particularly to ethylacetate and diethylether. Recently, hierarchi- cal hollow SnO2 spheres modified with Pt have also shown potential to detect methane, although their performances in terms of selectivity to compounds such as ethanol, ben- zene, formaldehyde, and ammonia appear to be unviable as their cross-responses are reduced only when the methane concentrations are hundreds of ppm (>250) higher than the concentration of the other compounds [118]. Table 6. Cross-responses to different Table VOCs6. Cross-responses recorded using MOXs. to different The color VOCscodes recorded and using numbers MOXs. in each Thecolumn color codes of the and Table numbers 6. Table Cross-responses in each 6. Cross-responses column to of different the to Table different VOCs6. Table Cross-responses recorded VOCs6. Cross-responses recorded using MOXs. to using different The MOXs. to different color VOCs The codes recorded color VOCs andcodes recorded using numbers and MOXs. using numbers in each The MOXs. column color in each The codes of column color the andcodes of numbers the and numbers in each column in each of column the of the Nanomaterials 2021, 11, 552 Nanomaterials Nanomaterials 2021, 11, 552 2021, 11, 552 17 of 34 17 of 34 17 of 34 table represent the load of responsetable forrepresent the referenced the load material of response towardsforthe thesame referenced gas concentration material towards of various the same testedgas table concentration represent table represent the of various load the of response tested load oftable response forrepresent thetable referenced forrepresent the the referenced load material the of response load material towards of response fortowards the thesame referenced forthe the gassame referenced concentration material gas concentration material towards of various towards theofsame tested various the gassame tested concentration gas concentration of various of tested various tested Table 6. Cross-responses to different VOCs recorded using MOXs. The color codes and numbers in each column of the analytes. N/A: data not available, RT: analytes. room temperature. N/A: data not available, RT: room temperature. analytes.analytes. N/A: dataN/A: not data available, not available, RT: analytes. roomRT: analytes. temperature. N/A: room datatemperature. N/A: not data available, not available, RT: roomRT: temperature. room temperature. table represent the load of response for the referenced material towards the same gas concentration of various tested analytes. N/A: data not available, RT: room temperature. ppm ppm 220 °C—2000 ppm ppm ppm °C—2000 ppm ppm °C—2000 ppm ppm 210 °C—2000 ppm ppm ppm ppm ppm 210 °C—2000 ppm 210 °C—2000 ppm 210 °C—2000 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm 210 °C—2000 ppm ppm ppm ppm ppm °C—2000 ppm ppm ppm ppm ppm ppm ppm ppm 210 °C—2000 ppm ppm ppm ppm °C—2000 ppm ppm ppm ppm ppm ppm °C—2000 ppm ppm Table 6. Cross-responses to different VOCs recorded using MOXs. The color Table codes and 6. Table Cross-responses numbers 6. Cross-responses in eachto column different to of different VOCs the recorded VOCs recorded using MOXs. usingThe MOXs. colorThe codes color andcodes numbers and numbers in each column in each of column the of the 400 °C—100 ppm 220 °C—100 ppm 340 °C—500 ppm 260 °C—100 ppm °C—500 ppm ppm ppm 220 °C—100 ppm ppm ppm °C—500 ppm °C—100 ppm ppm ppm 210 °C—800 ppm 220 °C—100 ppm ppmppm 210 °C—2000ppm ppb ppm ppm ppm °C—100 ppm ppm ppm ppmppm ppmppm ppm 220 °C—100 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm 210 °C—800 ppm ppm ppm ppm ppm °C—2000ppm °C—100 ppm ppm ppm 220 °C—500 ppb °C—500 ppb ppb °C—200 ppb RT—N/A 225 °C—400 ppb ppb ppb ppb ppb ppb 250 °C—200 ppb ppb ppb ppb ppb ppb ppb ppb°C—400 ppb 250 °C—87 ppm 360 °C—50 ppm 150 °C—10 ppm 280 °C—50 ppm ppm °C—50 ppm 350 °C—50 ppm °C—10 ppm °C—50 ppm ppm °C—50 ppm °C—50 ppm ppm 300 °C—10 ppm ppm °C—10 ppm ppm ppm ppmppm 150 °C—10 ppm ppm ppmppm °C—50 ppm ppmppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppmppm ppm ppm 300 °C—10 ppm ppmppm ppm ppm ppbppm °C—50 ppm °C—10 ppm °C—50 ppm 300 °C—10 ppm °C—500ppm °C—100ppm 60 °C—700 ppm ppm ppm ppm table represent the load of response for the referenced material towards thetable samerepresent gastable concentration represent the load theof of response various load of response tested for the referenced for the referenced material material towards towards the samethe gassame concentration gas concentration of various of tested various tested 220 °C—1 ppm °C—1 ppm °C—5 ppm ppm °C—50ppm ppmppm ppm ppm ppm ppmppm ppm ppm ppm ppm ppm ppmppm ppm RT—3800 ppm °C—1ppm ppm ppm ppm ppm ppm ppm ppm ppm 220 °C—2000 220 °C—2000 360 °C—5000 320 °C—2000 220 °C—2000 220 °C—2000 °C—2000 280 °C—5000 250 °C—2000 210 °C—2000 220 °C—2000 ppb°C—2000 °C—2000 220 °C—2000 °C—2000 °C—2000 °C—5000 360 °C—5000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—5000 °C—5000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 ppm ppm ppm ppm ppm ppb analytes. N/A: data not available, RT: room temperature. analytes.analytes. N/A: data N/A: not data available, not available, RT: roomRT: temperature. room temperature. 320 °C—100 °C—100 150 °C—200 250 °C—100 220 °C—800 300 °C—500 °C—100 °C—100 250 °C—400 °C—500 340 °C—500 360 °C—100 °C—500 °C—100 °C—500 °C—500 °C—100 °C—100 °C—500 °C—100 °C—500 ppb°C—500 °C—100 220 °C—100 °C—500 °C—100 °C—200 °C—500 °C—100 °C—500 °C—100 °C—100 °C—100 °C—800 °C—200 °C—100 °C—400 °C—200 °C—400 °C—800 °C—800 °C—100 ppb°C—400 ppm ppm ppm ppm °C—87 60 °C—700 280 °C—10 60 °C—700 350 °C—10 °C—87 °C—50 °C—87 280 °C—50 °C—10 °C—50 °C—87 °C—50 °C—87 °C—50 °C—10 °C—700 °C—50 °C—50 °C—10 °C—50 °C—10 °C—50 °C—10 220°C—700 °C—50 °C—50 °C—700 °C—50 °C—10 °C—50 °C—10 °C—10 N/A—N/A N/A—N/A N/A—N/A N/A—N/A N/A—N/A N/A—N/A N/A—N/A °C—5 280 °C—1 °C—1 °C—1 °C—5 °C—5 °C—5 °C—5 RT—3800 RT—3800 RT—3800 RT—3800 RT—3800 RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A °C—2000 220 °C—2000 220 °C—2000 °C—5000 °C—2000 210 °C—2000 210 °C—2000 210 °C—2000 210 °C—2000 400 °C—100 220 °C—500 220 °C—100 °C—500 260 °C—100 320 °C—500 220 °C—100 250 °C—200 210 °C—800 220 °C—100 225 °C—400 250 °C—87 360 °C—50 150 °C—10 °C—50 60 °C—700 350 °C—50 300 °C—10 280 °C—50 °C—10 N/A—N/A 220 °C—2000 ppm 220 °C—2000 ppm 220 °C—2000 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm Nanomaterials 2021, 11, 552 15 of 33 220 °C—1 250 °C—5 ppm ppm ppm 260 °C—100 ppm 320 °C—500 ppm 220 °C—100 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm RT—3800 RT—N/A 220 °C—500 ppb ppb ppb ppb ppb 250 °C—87 ppm 360 °C—50 ppm 150 °C—10 ppm 280 °C—50 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm °C—50 ppm °C—10 ppm °C—50 ppm 300 °C—10 ppm ppm ppm ppm 400 220 220 220 250 340 200 250 300 260 210 210 400 °C—100210 220 210 220 °C—100220 200 340 °C—500300 210 250 400 220 220 400 220 220 225 220 220 250 340 360 260 150 400 320 280 220 220 260 220 400 320 220 220 250 220 220 220 340 360 220 220 200 250 350 150 340 280 300 200 350 250 260 150 250 320 280 300 220 210 250 260 220 210 320 210 220 210 220 210 220 220 210 220 210 200 280 350 220 210 250 220 280 300 200 225 350 300 250 250 210 300 225 210 250 210 210 210 210 210 210 220 °C—100220 280 210 300 210 220 210 280 225 210 225 °C—400225 220 °C—1 ppm 340ppm 280ppm 220ppm 300ppm ppm 200ppm ppm RT—3800 ppm 60 60 60 200 °C—5000 210 °C—2000 210 °C—2000 400 °C—2000 210 °C—2000 210 °C—2000 280 °C—2000 °C—2000 220 °C—2000 220 °C—2000 °C—2000 °C—2000 200 °C—5000 300 °C—5000 250 °C—2000 210 °C—2000 210 °C—2000 210 °C—2000 °C—2000 °C—2000 °C—2000 210 °C—2000 210 °C—2000 210 °C—2000 250 °C—200 °C—100 °C—500 210 °C—800 220 °C—100 °C—100 °C—500 220 °C—500 °C—100 °C—400 340 °C—500 260 °C—100 150 °C—500 220 °C—100 °C—500 °C—100 220 °C—100 °C—200 °C—200 210 °C—800 210 °C—800 °C—100 225 °C—400 60 °C—700 350 °C—50 300 °C—10 220 °C—87 °C—50 °C—10 °C—50 °C—87 °C—10 360 °C—50 °C—50 °C—10 °C—50 60 °C—700 °C—50 60 °C—700 °C—10 250 °C—50 °C—10 N/A—N/A N/A—N/A 210 Methanol ❿ ❸Methanol ❻ ❷ ⓿ ❻ ❺❿ ❽⓿❸❿ ❸ ❶❻ ❶❷ ⓿ ❻ ❺ ❽ ⓿⓿ ⓿❸ ❹❿ ❸ ⓿❶ ⓿❶ ❻ ❷ ❿Methanol ⓿ ⓿ ❹Methanol ❸ ⓿❿ ⓿❻ ❿ ❷ ❸ ❿Methanol ❻ ❷❸ ⓿ ❻ ❻❷ ❺⓿ Methanol ❿❽❻ ⓿❺ ❸❿❽❿⓿ ❸ ❶❿ ❻❶❷❸ ❶ ⓿❻ ❶ ❻❷ ❺⓿ ❽⓿ ❻⓿⓿❺❸ ❹❽ ⓿ ❿❸ ⓿⓿⓿ ❸❹ ❶⓿ ❿❸ ❶❻⓿❶ ⓿❷ ❶❻ ❿❷ ⓿ ⓿❿ ❹⓿ ❸⓿ ⓿❹ ⓿❸ ❻⓿ ⓿ ❷❻ ❿❷ ❿ 250 °C—5 °C—1 °C—1 °C—5 °C—5 RT—3800 RT—3800 RT—N/A RT—N/A Ethanol ❿ ❿ ❿ Ethanol ❽ ❶ ❿ ❼ ❽ ❶ ❺❿ ❼❿ ❼❿ ❷❽ ❶ ❿❿ ❿❼ ❹❽ ❶❶ ⓿❺ ❸❼ ❹❼ ❷❼ ❻❿ ❶❿ ❷❹ ❼❶ ⓿ ❸Methanol Ethanol ❹ Ethanol ❼❻ ❿❿❶❿ ❷❿ ❼❿ ❿❽❸❿ Ethanol ❶❻ ❿❷ ❽❼⓿ Ethanol ❶❽❻ ❿❶❺ ❼❺❽❽ ❿❼❶ ❿ ⓿❼❸ ❺❿❿ ❼ ❿❷ ❿❼ ❽❿ ❶ ❶❿❶ ❷ ❿❿❽❼❹ ❶❿ ❽❶ ❿❿ ❶⓿❼❹ ❺ ❸❽ ❶ ❼ ⓿❹ ❶⓿ ❼❺ ⓿ ❸ ❹❼ ❹ ❷ ❸❼ ❼⓿❻⓿ ❿ ❶ ❷❼ ❿ ❻ ❷❻ ❹ ❼ ❿❶ ❶❿ ❷ ❷ ⓿❹ ❼ ❸❶ ❿ ❹⓿ ❸ ❹ ❼ ❻ ❶❼ ❷❻ ❼❶ ❷ ❼ Chloroethanol ❻ Chloroethanol ❻ Chloroethanol Chloroethanol ❻ ❻ Chloroethanol ❿❿❿ Chloroethanol ❻ ❽❶❿❼❽❶❺❼❼ ❻ ❷ ❿❿❹❶⓿❸❹ ❼❻❶❷❼ 210 220 220 400 220 280 250 300 340 360 220 250 225 150 280 320 220 260 320 220 220 220 350 250 200 350 210 300 250 210 210 210 280 300 220 280 Ethanol Nanomaterials 2021, 11, 552 Nanomaterials 17 of 34 Nanomaterials 2021, 11, 552 Propanol ⓿2021, Table 6. Cross-responses to different VOCs recorded using MOXs. The color codes and numbers in each column of the table represent the load of response for the referenced material ❺ Propanol ❹ ❺ ❶ ⓿❹ ⓿❶ ⓿⓿ ❺Propanol Propanol Chloroethanol ⓿ Nanomaterials 11, 552 ❻❶ ❺❺ 2021, 11, 552 ❺❹ Propanol Propanol ❹ ❺ ❶⓿ ❺❹❶ ⓿❹ ⓿❶ ⓿⓿⓿❶ ⓿⓿ 17 ❺of 34 17 ❺of 34 ⓿ ⓿ ⓿ 17 of 34 ⓿ ❺ ❺ Methanol ❿ ❸❻❷⓿❻❺ ❽⓿❸❿ ❶ ⓿⓿❹ MethanolMethanol ❿❸ ⓿❿ ⓿ ❻ ❸❷ ❻ ❷❸ ❿⓿❻ ❻❷ ❺⓿ ❽❻ ⓿❺ ❸❽ ❿⓿ ❸ ❶❿ ❶ ❶❶ ⓿ ⓿ ❹⓿ ❸⓿ ⓿❹ ⓿❸ ❻⓿ ⓿ ❷❻ ❿❷ ❿ Butanol Butanol❿ ❸ ❿ ❸ ❶ Butanol Propanol❶ Butanol Butanol ❺ ❿Butanol❹❸❿ ❸ ❿⓿ ❸❿ ❶ ❸ ❶ ❶⓿ ⓿ ❺ ❶ ❶ Ethanol ❿❿❿ ❽❶❿❼❽❶❺❼❼ ❷ ❿ ❿❹❶ Ethanol ⓿❸❹ Ethanol ❿❼ ❿❻ ❿❿ ❶❿ ❷ ❽❿ ❼❶ ❿❽ ❼❶ ❽❿ ❶❼ ❺❽ ❼❶ ❼❺ ❼ ❷❼ ❿❷ ❿ ❹❿ ❶❿ ⓿❹ ❸❶ ❹⓿ ❸ ❹ ❼ ❻ ❶❼ ❷❻ ❼❶ ❷ ❼ towards the same gas concentration of various tested analytes. N/A: data not available, RT: room temperature. Pentanol Pentanol Chloroethanol ❿ Nanomaterials Nanomaterials PentanolPentanol ❻ in each column of the Table 6. Table Butanol2021, 11, 552 2021, 11, 552 ❿ PentanolPentanol Chloroethanol Chloroethanol ❿ ❻ ❸❻ ❶ ❿ 17 of❿34 17 of 34 ❿ ❿ Table 6. Cross-responses to different Ether VOCs recorded using MOXs. The color codes and numbers Cross-responses 6. Cross-responses to Table different 6. to Cross-responses different VOCs recorded VOCs torecorded using different MOXs. using VOCs The MOXs. recorded colorThe codes using color and MOXs. codes numbers ❸andThe numbers color in ❸each codes column in each and of numbers column the ❿ ofinthe each column of the ❿ Ether Nanomaterials 2021, 11, 552 Nanomaterials 2021, 11, 552❹ ❸ ❽ ❿ ❿Nanomaterials ❹ ❸ 2021, Ether ❽ ❿Ether 2021, 11, 552❿N m Ether Ether ❹ ❽ ❹❿ ❽ ❿ ❿ ❹ 34❸17 ❽ ❹ ❿ ❿34❸ ❽ ❿ ❿ table represent the load of response❾ for the referenced material towards the Propanol same gas concentration of ❺ various ❹17 table tested of 34 Nanomaterials represent 11, 552 Pentanol table represent the load ❶ the of ⓿ table response load Propanol represent of response for the the Propanol referenced load for the of ⓿ ⓿ for response referenced material N 17mof 34 ❺material towards the ❺❹ ❺same referenced towards the ❹material the gassame concentration towards gas ❶ ⓿ concentration theofsame various❶ gas of ⓿ tested 17 of various concentration tested of of ⓿ ⓿tested various ⓿⓿ ❺ ❺ Diethylether Diethylether ❾ ❶ Diethylether Diethylether❶ ❾ Diethylether ❾ Diethylether ❾ ❾ ❶ ❶ ❶ ❶ Ether ❸ ❹❸❽❿ ❿ ◦ C—2000 ppm ppmC—2000 ppm ppm ppm ppmppm ppmppm ppm ppm ppm ppmppm C—2000 ppm analytes. N/A: data not available, RT: room temperature. Butanol ❿analytes. ❸ analytes. N/A: dataN/A: not data available, analytes. not N available, RT: mN/A: room N data Butanol RT: mtemperature. not roomavailable, Butanol temperature. ❶RT: room temperature. ❿ ❸❿ ❶ ❶ ppm ppm ◦ C—500 ppm ppm ppm ppm ppm ppbppm ppm ppm ppbppm ◦ C—500 ppb ppb 225m◦ C—400 ppb ❸❹❸❹ ❸ Tetrahydrofuran ❸❹ ❸ ❹ 6. Table ❸ Tetrahydrofuran ❸color ❹and❸ ❸❹ ❹and❸❹ ❸column ❸ ❸ ❹the❸❸ ❹of❹the❸ ❹ ❸ ❸ ppm ppm ppm ◦ C—50 ppm ppm ppm m ◦ C—10 ppm Tetrahydrofuran Tetrahydrofuran Tetrahydrofuran Table Cross-responses 6. Cross-responses Tetrahydrofuran to different to❾ different VOCs recordedVOCs recorded using MOXs. usingThe MOXs. colorThe codes codes numbers numbers in each in each of column C—50 ppm ppmppm ppbppm ❶ ppm ppm Diethylether RT—3800 ppm Pentanol PentanolPentanol ❿ ❿ ❿ ❷ ❷ table represent table represent the load the of response load of response for the referenced for the referenced material material towards ❷ towards the ❷ same the gas same concentration gas concentration ❷ of various ❷ of tested various tested N/A—N/A Methane Methane Methane Methane TMethane Methane ppm °C—2000 ppm °C—2000 ppm ppm °C—2000 ppm C—10ppm °C—2000 ppm °C—2000 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm Table 6. Cross-responses to different VOCs recorded using MOXs. The Table color 6. codes Cross-responses and numbersto different in each column VOCs recorded of the Table Tetrahydrofuran using6. MOXs. Table Cross-responses 6.The Cross-responses color tocodes different andto numbers different VOCsC recorded VOCs in ❸each recorded using column MOXs. of using theTheMOXs. T❿color VOCThe codes C colorand codes numbers and ❸ MOX ❹each ❸❹ numbers in column VOC in each of the ❸ column of❸ the m❽ MOX❿❸ ❽ ❿ m ❿ ❿ m m 300 RT—N/A ❹ ❽ ❿ ❹ ❹ °C—100 ppm °C—100 ppm °C—500 ppm °C—100 ppm °C—500 ppm ppm °C—800 ppm °C—100 ppm °C—100 ppm ppm ppmppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm Ether Ether Ether C—2000 C—5000 C—2000 C—2000 C—2000 C—2000 ppb°C—500 ppb ppb°C—200 ppb ppb°C—500 ppb ppb ppb ppb ppb ppb ppb analytes. analytes. N/A: data N/A: not data available, not available, RT: room RT: temperature. room temperature. C—200 N m Heptane ppm 360 °C—50 ppm 150 °C—10 ppm ppm 350 °C—50 ppm °C—10 ppm °C—10 ppm 360 °C—50 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm °C—50 ppm mppm ppm ppm ppm ppm ppm ppm °C—10 ppm 60 °C—700 ppm ppm ppm ppm ppm ppm ❶ ❶ ❶ ❶ ❶ ❶ ◦ C—100 ◦ C—100 ◦ C—100 ◦ C—100 C—800 ◦mC—100 Heptane Heptane Heptane Heptane Heptane C—500 table represent the load of response for the referenced material towards table the represent same gas the concentration load of response of various for the ❾ referenced tested tablematerial represent table towards represent the loadthethe ofsame response loadgasof response concentration for the referenced for theof referenced various material ❶tested material towards towards the same the gassame concentration m gas concentration wof variousof tested various m tested m w m ppm 250 °C—5 ppm ppm ppm °C—50ppm ppm ppm ppm °C—10ppm ◦ C—87 ◦ C—50 C—10 ❷ RT—3800 ppm Methane ppm mppmppm RT—3800 ppm C—700 ◦mC—50 ◦ C—1 C—5 Diethylether Diethylether T Diethylether TC C ❾VOC ❾VOC MOX MOX m m ❶ m❶ m °C—2000 °C—5000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 m°C—2000 °C—2000 m°C—2000 °C—5000 m°C—5000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—2000 °C—5000 °C—2000 °C—2000 ◦°C—2000 °C—2000 m◦°C—2000 °C—2000 °C—2000 °C—2000 °C—2000 Octane N/A: data not available, RT:Octane analytes. room temperature. analytes. N/A: data not available, RT: room temperature. ❶Octaneanalytes. analytes. Octane N/A: dataN/A: not data available, not m ❶ Octane N available, RT: room N RT: Octane mtemperature. N room A temperature. R m NmA R m m ❶ ❶ ❶ ❶ °C—100 ppb°C—400 °C—100 °C—100 °C—500 ppb°C—500 °C—100 °C—500 °C—100 °C—500 °C—100 °C—500 °C—100 °C—100 °C—500 °C—500 °C—100 °C—100 °C—100 °C—200 °C—500 °C—200 °C—100 °C—800 250 °C—200 °C—100 °C—800 m°C—100 °C—400 ppb°C—400 °C—800 m °C—100 225m°C—400 Heptane ❶ 220°C—87 280 °C—50 280 °C—50 °C—87 °C—87 °C—10 °C—50 °C—10 °C—50 °C—87 °C—50 °C—10 °C—700 °C—50 °C—50 °C—700 °C—10 °C—50 m ◦°C—10 m °C—700 °C—50 °C—10 °C—50 °C—10 °C—50 mw w m ❸m❹ ❸❸ N A—N A N/A—N/A N/A—N/A N/A—N/A N/A—N/A N/A—N/A ◦ ppm ◦ppm ppm ◦ppm ppm ppm ppm ppm Tetrahydrofuran Tetrahydrofuran ❸❹❸❹ Tetrahydrofuran ❸ ❹❹ ❸ ❹ ❸ ❸ 220 °C—1 ◦°C—1 °C—1 280 ◦°C—1 °C—5 °C—5 300 °C—5 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm RT—3800 RT—3800 RT—N/A RT—N/A RT—N/A RT—N/A Decane ❸Decane ❸ Decane Decane ❸Decane ❸Decane ❸ ❸ ppb ppb ppb ppb ppb m mppb ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm N m N m Octane N m N ❷m Methane N mN A NA R mR m m m ❶ 250ppm ppm ppm ppm Methane Methane ❷ ❷ m ppm m ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm 220ppm 220 °C—2000 ppm 220 °C—2000 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm 60ppm ppm ppm ppm ppm ppm m mppm m ppm mppm m mppm ppm mppm ppm ppm m ◦m m m m C— mm m m m C— C— 210 ◦m m m m TIsopreneC ❿VOC MOX 220 °C—1 ppm ◦ Isoprene ❿ m m ❿ ❿ ❿ ❿ ◦ IsopreneIsoprene Isoprene Isoprene C—°C—2000 C—°C—2000 C—°C—2000 °C—2000 °C—2000 °C—2000 C—°C—5000 C—°C—5000 °C—2000 C—°C—2000 C—°C—2000 °C—2000 C—°C—2000 °C—2000 C—°C—2000 °C—2000 °C—2000 °C—2000 ppm ppm ppm ppm ppm ppm 400 °C—100 ppm 220 °C—100 ppm ppm 340 °C—500 ppm ppm 260 °C—100 ppm 320 °C—500 ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppmm ppm ppmm ppm m ppm 250 m m m m ppmm m ppm ppm ppm mm m 300 m m m ❸ 280 °C—50 ppm◦ Decane 250 220 °C—500 ppb ppb ppb ppb ppb ppb ◦ m◦ 400 °C—100 220 °C—500 °C—100 °C—100 °C—500 340 °C—500 °C—100 C—°C—500 C—°C—100 C—°C—500 °C—100 °C—500 °C—100 °C—100 °C—200 °C—200 °C—800 °C—100 °C—800 °C—100 C—°C—400 °C—400 C— 250m°C—87 ppm C— 360m°C—50 ppm C— 150m°C—10 ppm C— 280m°C—50 ppm C— 350m°C—50 ppm C— 300m°C—10 ppm 360 °C—50 ppm ppm ppm 150 °C—10 ppm ppm ppm ppm 360 °C—50 ppm ppm ppm ppm 280 °C—50 ppm ppm m ppm ppm ppm m mppm ppm mppm m ppm 300 °C—10 ppm m mm mppm mmppm m m m m m °C—700 ppm ppm ppm ppm m m ◦ 250 150 250 °C—87 280 350 m300 C— m300 Heptane ❶ Heptane Heptane ❶ ❶ R —N A RT—N/A 220 250 360 N m N m N m 280 400 220 340 C— 280m°C—50260 220 210 220 250 °C—87 °C—50 °C—87 °C—10 °C—50 C—°C—50 °C—10 °C—50 m w ❷ m °C—700 C—°C—50 °C—700 °C—10 C—°C—50 °C—10 C—°C—50 C—°C—10 °C—50 °C—10 C—N/A—N/A C— C— mm mC—N/A—N/A °C—100400 C— 220 °C—500220 °C—100220 250 °C—500340 °C—100260 °C—500320 220 220 220 220 200 C— 250 °C—200250 210 210 210 210 °C—800210 °C—100220 300 210 225 400 220 220 °C—500220 400 220 220 220 340 220 250 150 340 400 360 220 220 260 150 320 220 280 220 250 260 220 340 320 220 360 mm220 220 220 150 220 200 280 350 220 260 250 320 300 200 220 350 220 250 210 220 300 210 220 250 210 C— 200 210 210 C— C— m mm 210 °C—800350 210 210 m210 220 280 210 300 210 250 m220 210 280 225 210 300 210 210 210 225 210 C— 220 280 210 m °C—1 ppm m °C—5 ppm °C—1 ppm ppm ppm m ppm ppm A Am m ppm ppm R —R —C— m m m RT—3800 ppm C— ppm m 320 ❷ ⓿❶❽ ⓿❶ ❶❽ ❶⓿⓿❷⓿❿❿❻ ❶❶ ❶❶ ⓿ ⓿ ❷Isoprene ⓿❿❿❻ ❶ ❶ T❷ ⓿ C❷ ❶ ❽⓿ C ❶❽ ❷VOC⓿❷ ❶❶ ❽❶ ⓿ ⓿ ❶❶⓿❽❶ ❷ ⓿⓿⓿ ❿❷ ❿ ⓿❻❿ ❿ ❶❻ ❶ ❶❶❶ ⓿❶ ❶ ⓿m❶ ❷⓿ ⓿⓿ ❿ m❷ ❿⓿ ❻❿ m❿ ❶❻ ❶m ❶ ❶ 220 220 200 210 210 210 210 Benzene Benzene Benzene Benzene Benzene Benzene m m m m m m m —C— m m 60 220 °C—1 °C—1 °C—5 °C—5 60 60 T VOC MOX MOX C—RT—3800 RT—3800 Octane ❿ RT—N/A RT—N/A C— 220 °C—2000 C— 220 °C—2000 C— 220 °C—2000 C— 200 °C—5000 C— 210 °C—2000 C— 210 °C—2000 C— 210 °C—2000 C— 210 °C—2000 °C—2000 220 °C—2000 200 °C—5000 °C—2000 210 °C—2000 °C—2000 °C—2000 C—°C—2000 C—°C—2000 °C—2000 °C—2000 C—°C—2000 C—°C—2000 C—°C—2000 °C—5000 C—°C—5000 m m 210 °C—2000 °C—2000 C—°C—2000 °C—2000 C—°C—2000 °C—2000 °C—2000 C—°C—2000 m m 210 °C—2000 C—°C—2000 m m m m m mm m mm m m m m C— m C— mm mC— C— m NA R m m Octane Octane ❶ ❶ ❶ °C—100 C— 225 °C—400 220 °C—100 °C—100 °C—200 °C—100 °C—100 °C—500 250 °C—500 °C—100 210 °C—500 210 °C—100 °C—800 C—°C—500 °C—100 °C—100 C—°C—500 °C—400 °C—100 °C—100 °C—200 C—°C—200 °C—100 °C—800 °C—100 C—°C—400 C—°C—400 m m m m C— m m Ethylbenzene Ethylbenzene ❷❺❽⓿❸❿ ❷ ❽ EthylbenzeneEthylbenzene ❿❽ ❿ Ethylbenzene ❷❷ Ethylbenzene ❷ ❷ ❷ ❽⓿ ❽ ❷❽ ❿ ❽ °C—10 60 °C—700 °C—50 °C—10 °C—87 °C—87 °C—10 °C—50 °C—10 °C—50 °C—10 °C—700 °C—50 °C—700 °C—10 °C—50 C—°C—10 °C—50 °C—50 C—°C—10 m❶ m ⓿w w⓿ m⓿❿ m⓿❿ N/A—N/A N/A—N/A N/A—N/A N A—N A m ❿ ❸ ❻❷⓿❻ ❶❶ ⓿⓿❹❸⓿⓿❻ ❷ ❿ ❸ ❻ ❷❸ ⓿ ❿❻ ❻ ❺⓿ ❽❻ ⓿❺ ❸❽ ❿❻⓿ ❷❸ ❶ ⓿❿ ❶❻ ❺❶ ❽❶ ⓿ ❸⓿ ❿ ⓿ ❹ ❶⓿ ❸ ❶⓿ ❹⓿❸ ❻ ❷⓿❻ ❹❷ ❸ ⓿❻ C— m Methanol TMethanol Methanol Benzene TMethanol C❷ VOC⓿ C ❶ ❽MOX ❶ ⓿ ⓿m❷ ⓿ ❿ m❿ ❻mm ❶ ❶ 300 °C—1 °C—5 °C—5 °C—5 T C C T CDecane VOC T VOC MOX ❸VOC ❸VOC MOX m MOX MOX m mm m m RT—3800 RT—3800 m m RT—3800 RT—N/A RT—N/A AC—A m m RT—N/A 220 A A Decane ❸ Decane C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— A A N A—N C— C— C— C— C— C— C— A—N Methanol Chlorobenzene ❶ ¿ ❿ ❿ Chlorobenzene ❿ ❼ ❽ ❶ ❿ ❼❶ ❽ ❶ ❺ ❼¸ ❼ ❼ ❷» ❿ ❿ ·❹ ❶ ❷⓿❸ ⓿❹ ❷» ❼º❻ ❶ ❷❷ ½ ❼❷ ❼ Chlorobenzene ❷¸ ❶ ¿ ❶ ⓿Chlorobenzene ⓿ ❿❷ ❼❿ ¶ ❿ Chlorobenzene ❿❿ N❼ A¶ ❽Chlorobenzene ❿ ❶ ❿ ❽❼❶ ❽ ❿ N❿ A ❶❿ ❼❺ ❿❶❽❼❶ ❼ ❽ ❺ R❼ ❶ ❼❷❿❼ ❼ mR❿ ❽ ❼m ❷❿ ❶ m ❹ ❺❿ ❷ ❶ ❼ m❿⓿ ❼ ❹ ⓿❶ ❸ ❷❷ ❹ ❷⓿ ⓿ ❸❿❹ ❷❼ ❿ ¹ ❻ ❹ ❷ ❶❶❼ ¸ ❷ ⓿ ❻❼❸❶❷ ❷ ❼ ❹ ⓿❼ ❷ ⓿❷ ❼ ❷ ❼⓿❻ ⓿❶ ❷❷ » ❼❷ ❷❼· ❼ ¿ C— C— 400 220 220 220 360 220 250 150 340 360 280 260 150 320 280 220 260 220 320 220 220 220 220 200 350 220 250 300 200 350 250 250 210 300 210 250 210 210 210 210 210 210 220 280 210 300 210 220 210 280 225 300 210 C—225 Ethanol Ethanol Ethanol Ethanol ❷ ❽ 350—N —N Ethylbenzene m m w w m m mm w m w m w m m A m m m m m m m m m 60 60 Isoprene ❿ Isoprene TIsoprene ❿ ❿VOC C— 400 m C— 220 m C— 340 m C— 260 m C— 320 m C— 220 m C— 210 m C— 220 m T⓿ T CToluene C❽ ❸ VOC VOC C❶ MOX MOX m m❺ MOX m m⓿ m❷ ⓿❽ m ⓿ C—A C—A R — ❸❸ ❶ ❸⓿❶❿ ❹ ❺❸ ¿ ❷❸ ❿ ⓿❶ ❸ ❾⓿ ❶ ❷❿ ½❺ ⓿❹ ⓿❺ ¶ ❷❷ ¿ ❿⓿⓿ ❹ ❽❾ ⓿❷ ❺ ❷⓿Chlorobenzene ❷ ⓿❻ ❹ ⓿❸ R❸ ¼ ❿N❶ ❷ ❸❸m⓿ ❶ ❶ ❸❿⓿ ❹ ❿❺ ❸❷❹ ❸❿m ❺ ⓿ R❸ ❷ ❶ m❸❿ ❸❾ ⓿ ⓿ R❶ ❶❷❸❾ ❿❺ m⓿ ⓿ ❶❷ ❹⓿❿❺ ❺ ❷❷ ⓿ ❷❹ ⓿ ❿⓿ ❷ ⓿❹ ❷ ❽ ❿⓿ ❾ ⓿ ❹⓿❽ ❷❾ ❺ ⓿ ❷ ❷ ⓿❺ ❷⓿ ❷⓿ ⓿ ❹ ❽⓿ ❹ ❷ ¶ ❷· C— m m C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— m mm mmC—C— m m mC— m m m C— C— C— Toluene Toluene Toluene Toluene Toluene m 60 m ❻ ❻ ❻ A—N Ethanol Chloroethanol ¿ ¿ ¼ ❷½ Chloroethanol Chloroethanol N A¼ Chloroethanol ⓿ ❶¶ N Aº A· ¿ ¿ m¹ m¶ ¸ ¹ ¼ » ❶ ¼ ❶ A R N❼ m ❷m N mA❷ R❽ m m m ⓿ ⓿ ❷ ❷ ❼ C—mmmmmmC— mm mm mC— C— 220 C— 250 300 210 400 220 350 250 400 220 220 250 C— m m 220 340 220 210 250 150 m 340 360 210 260 150 220 320 m 280 220 300 mm mm 260 220 320 210 220 225 m mmmm220 220 C—m m 220 m220 200 220 m 250 m200 mm300 350 250 250 m mm300 210 mm m 250 210 C— C— C—m m 210 210 210 210 220 280 m mm m 210 210 220 m mm mm280 m 225 m m mm300 C—m m 210 m m mm225 ❿ ❸ ❻ ❸ ⓿ ❻ ❻ ❺⓿ ❿❻ ⓿ ❺❸ w❽ ❿ ❷⓿ ❸❶ ⓿❿ ❶ ❽❶ ⓿❶ ❽ ⓿ ⓿m❹ ⓿❸ ⓿⓿w ❹ ⓿❸ ❻ ⓿ ❶⓿ ❷ ⓿❻ ❿ ❷❷ ❿❿ m ❽ Methanol Methanol —N R —N Benzene ❶❶ Benzene ⓿ ⓿ Benzene ❷ ⓿ ❿ m ❻ m ❶ ❶ w ❷❶ ❶ ❶ ❶ m ⓿❶ ⓿ ⓿⓿ ❷❿⓿ ❻ ❿❿ ❶❻ ❶ ❶ m m m m m m m m m m C— C— mm m m C— C— m m m C—m m m m C—mm mm m R —C— C— m m m m—Rm 60 60 N R R mm mm m m m mm mm m C— m mm m m mm mm mm m m m mm mm mm mm m m m m Xylene ❺ ❶ ❿ Xylene ❹ ❷ ❶❶ ⓿❿ ❼❷ ❿ ⓿⓿❿ ❼ ❿ ❿ N m Propanol Xylene ❺Propanol ⓿ Xylene ⓿ ❿ ❺ ❶❿ ❿ Xylene ❺❹ ❶ ❷ ❿ Xylene ❹ ❷ ❺ ❹ ❶ ❶⓿ ❿ ❶❼ ❶❷ ⓿ ❿ ❿ ❼ ❷ ❿ ❶ ⓿ ⓿ ⓿ ❿ ⓿ ⓿❼ ❿ ❿ ❺ ❼ ❿ ⓿ ❿❺⓿ ❿ ⓿ ⓿ ❿ ⓿ ❺ ⓿ ❿ ❿ ❿ R —R —R —m m Rm— Propanol Toluene Propanol ❸❿ ❸m ❶ ❸m ⓿ ❶ ❿❶ ❹❼ ❷❶ ❿⓿ ❾ ❷❹ ❺❿ ⓿❿ ❷❶ ⓿ ❹❹ ❽❻❶ ⓿❼ ❷ R m m Am AmN m Chloroethanol » ❶ Ethylbenzene ❷ NEthanol A ❶ NEthanol A R❿ ❷❿ Ethylbenzene R❿ m ❿❽ Ethylbenzene❿ ❶ Nm❿A❽ ❼ ❽ ❿ ❽❶ ❺ ❽❼❶❼❺ R❼ ❷ ❷❼ m❿ ❿ ❷m ❷❿ ❶❸⓿ ❹❸ ❹ ⓿⓿❸ ❷❼ ❷❻ ❼ ❿❶ ❷ ❼ ❽ ❷❽ ❿⓿⓿❹❸⓿⓿❻ m Methanol ❿ ❸ ❻ ❷ ⓿ ❻ ❺❾ ❽⓿❸❿ ❶Methanol ❿⓿⓿❹❸⓿ ❸⓿ ❻ ❻ ❷⓿ ❷❻❺ ❿ ❽ ⓿ ❸Methanol Methanol ❿ ❿ ❾❶ ❿ ❸ ⓿ ❻⓿ M ❸❹ ⓿ ❻❸❻ ❷ ⓿❺ ⓿ ❿ ⓿❽ ❻ ❻⓿ ❺❸ ❽ ❷❿ ⓿ ❸❿❶ ❿ ❻❷⓿❻❺❽⓿❸❿ M ❶ ❶ ⓿ ⓿❹ ⓿ ⓿⓿ ❸❹ ❶❶ ⓿ ❻❷⓿❻❺❽⓿❸❿ ❸❻ ⓿ ❻ ❿❷ ⓿⓿❹❸⓿⓿❻ ❶❶ ❷ ❿ mm m m mm C— C—m mm m m m m m Am mm m m m Rm Chlorophenol Butanol ❾ Chlorophenol ❿ ❸ ❶ Chlorophenol Butanol Chlorophenol Butanol ❾Butanol ChlorophenolChlorophenol ❿ ❸ ❾ ❿ ❸ ❾ ❿ ❸ ❶ ❶ ❶ m m m m C— mm mm m m m mm mA mm mAmmm m m m m m Xylene ❶ ❿ ❷ ❼ ❿ ⓿ ⓿ ❿ ❿ C— C— Propanol º❺ ¹❻ ❶ ❼ Chloroethanol Chloroethanol❻ ❻ ¶ ❷ ⓿ ⓿ ❷ ❶ ❷ ❼ ❼ ❷❷ ⓿❼ ⓿❷ ❷⓿ ⓿ ❷ ❷ ❷❼ ❼ º C— mm mm C— C— m m AC—A m m C— C— m mm m mm m m m m mm m m m m C— C— m mm m ❿ Chlorobenzene Chlorobenzene Chlorobenzene ❹❿ ❻❿ ❽ ❶❶ ⓿❿ ❿❼ ❽ ❿❶ ❼ ❼❸ ❷❶ ❿ ❿ ❹❿ ❶ ⓿ ❸❿ ❹ ❿❿ ❿ ❼ ❽❻ ❶ ❶❿ ❷ ❼❼ ❽ ❶❺ ❿❼ ❺❼ ❽❷ ❿ ❿ ❿ ❿ ❶❹ ❿ ❸❿ ❶ ❿ ⓿❽ ❶❿ ❸ ❶ ❹❿ ❿❽ ❼ ❻❶ ❽ ❿❿ ❼❶ ❿❼ ❻ ❺ ❽ ❿❿❿ ❶❼ ❷❶ ❷❼❺ ❼ ❼❷ ❼ ❽❶❿❼❽❶❺❼❼ ❿ ❷❿ ❹ ❿❶ ❿⓿ ❹ ❸ ❿❿❿ ❶❹ ⓿ ❸ ❹ ❷❼ ❽❶❿❼❽❶❺❼❼ ❻ ❿❿❹❶⓿❸❹ ❶ ❸❼ ❷ ❻❼ ❶ ❷ ❼❻❶❷❼ ❼ ❿❿❿❹❶⓿❸❹ ❼❻❶❷❼ C— Ethanol Ethanol Ethanol Ethanol C— C— m m m Acetone Pentanol ❽ ❸❷ Acetone ❻❽ ❿ ❿❹ ❷❻ ❿❷ ❿⓿ ❾❿ ❹ ❸❿ ❼❿ ❿ ❷❺ ❼ ❿❿ ❾❸ ❹❶ ❸❿❼ Chlorophenol Acetone Pentanol Acetone Pentanol ❼❿ ❽❹❸ ❻ Pentanol ❹M ❷ ❻ Acetone❸ ⓿ M❷ ❶ Acetone ❿ ⓿ ❽ ❿ ❻❿❿ ❽❿ ❹ ❿ ❻ ❿❸❿ ❻❷⓿❻❺❽⓿❸❿ ❹ ❷ ❷ ❻❶❿ ❸❿ ⓿❾ ❻❷⓿❻❺❽⓿❸❿ ❷❿❹❶❿ ❻ ⓿❾ ❿❸ ❿❹ ❿❼❻❿ ❿❿ ❸ ❷❺ ❿❼❿ ❶❶ ❿ ❿❷ ❺❼ ❿❿ ❶❶ ❿❾ ❼ ❹ ❶ ❿ ❿ ❾⓿⓿❹❸⓿⓿❻ ❸ ❸ ❿ ❹ ❼ ❿⓿⓿❹❸⓿⓿❻ ❶ ❸ ❺❼ ❿ ❺ ❿ ❷ ❸❿ ❼❶❿ ❿❷❸❶❿ mm C—m C—mm m m m m m C—m m —C—m ❾ N❺ m ❺❹ ❹ N m ❶ ⓿ ❶ ⓿ ⓿ ⓿ ⓿ ⓿ ❺ ❺ R — Propanol Propanol m m m m m m mm m m m ❸❸ ❶ ❸Chloroethanol ⓿❶❿ ❹❺ ❷❿⓿ ❻ ❾ ❷❺⓿ ⓿❷ ❻ ⓿❹ ❸❽ ❸ C❸ ⓿ ❶❸ ❸ ⓿❶ ❷❶❸ ❿⓿ ❻❶ ❹❿ ❺ ❷❹ ❿❺ ⓿❷ ❿ ❾⓿ ❷❾ ❺ ⓿❷ ⓿ ¶❺ ❷⓿ ⓿ ⓿❷ ❹ ❽⓿ ❹ ⓿❽ ⓿❷ ❷ C— C— C— C— C— C— C— C— C— C—AC— C— C— C— AC— C— C— C— C— C— C— C— C— AC— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— A C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— Butanol Toluene ❻ ¿ Toluene Toluene ❻ ⓿ ¸ ❿ ⓿❻ A—N NAA—N Chloroethanol Chloroethanol Chloroethanol C Butanone m m ❿❿❿ ❿❿❿ ❽❶❿❼❽❶❺❼❼ ❸ ❽❶❿❼❽❶❺❼❼ ❷⓿❸ ❿❿❹❶⓿❸❹ ❷❸❿ ❿❿❹❶⓿❸❹ ❿❼❻❶❷❼⓿ ⓿ ❼❻❶❷❼ C— Butanone Butanone ❸ ❹ ❸ ❽ ❿ ⓿❸ Butanone Ether TButanone ⓿C ❸ButanoneVOC ❸MOX ❹❹ ❸ ❽❹ ❿❸ ⓿ m❽ ❸⓿❿ ⓿❿❹ m❽ ❿ ❿❶ ⓿⓿ —N R —N Ether Ether ❽ Ether ❼ A A N A—N A C— m C— m C— m m m ❶ ❿❺Propanol ❷ Acetone Butanol Butanol ❹ ❻❿ ❷ ❶❿ ⓿ ❿⓿ ❻❿ ❸ ❿ ⓿ ❿❿❸ ❿ ❷❿ ❿ ❿❾ ❸ ❼ ❿ ❺ ❶ ❼❼❶ ❸ Pentanol Propanol ❺ ❷ ❸ ❹ ❷ ❶ ❾❸ ❶Propanol ⓿ ❶❷ Xylene ❺ ⓿⓿ ⓿ ❹ ❶ ⓿ ⓿❶ Propanol ⓿⓿ ❺ ❸❺ Xylene ❹❷ Xylene ❶❹ ⓿ ⓿ ❺ ❺❶❶ ⓿❹ ❶ ❶ ❿❷❿ ⓿ ❷ ❺ ⓿❶ ❶⓿ ⓿⓿ ❹⓿ ⓿ ❿ ❺ ❼ ❿❺ ⓿ ⓿⓿ ⓿ ❿⓿ ⓿ ❿ ❺ ❿ ❶⓿ ❿ ⓿⓿ ¿❺ C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— RC— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— —C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— —RC— ❻ ❺❻ A—N N A—N A—N N A—N Formaldehyde Diethylether ❸ ❿❿❺ Formaldehyde ❾ ❻❿ ❺ ❷ ❸ ❾❶ ❾ ❶❻ ⓿ ⓿❶⓿ ⓿ ❶ ❷❶ ❾ ❷ ❿❶ ⓿ ⓿Formaldehyde Formaldehyde Diethylether❶Diethylether❸ ❿ ❷❺❸ ❶❿ ❿❺ ❷C ❾ Formaldehyde Diethylether C❷❾ ❸ ❾❷ Formaldehyde ❸❻❶ ❿❾ ❸❻ ❾❿ m❺ ❷ ❸ ❶ ❷w ❷❾ ❸❶❶ ❾❶ ❷❾ ❻⓿ ❶m⓿❾❶ ❶❻ ⓿⓿ ⓿⓿ ⓿ ❶ ❷ ⓿ ❶❶ ⓿❾ ❶❷ ❿ ❶❶ ❾❶ ⓿❿ ⓿❶ ❶❶⓿ ⓿⓿ ⓿ ❶⓿⓿ ❷⓿ ⓿ ❶❷ ❿ ❶❿ C— C—C— C— C— C— M ❸ ❻❷⓿❻❺❽⓿❸❿ ❶❶ ⓿⓿❹❸⓿⓿❻ ❿ Butanone ❸ ⓿ ⓿ —N —N —N —N —N ❿ ❸ Chlorophenol ❾ ❶ ❿ ❸ Pentanol Pentanol ChlorophenolChlorophenol ❿ ❶❸ ❾ ❿ ❸ ❾ ❿ ❸ ❶ ❶ ❿ ❸ ❿❶ ❿ ❶ N Ether Butanol Butanol Butanol Butanol N❿ A❿ R m m ¹❶ ¸❸ ❷½ ¿ ❸❸ ❺❶ ¿ ❺ A Acetaldehyde ❿ ❿ ❿❿❿ Acetaldehyde ❿❿ ⓿❹ ❸ ⓿❸ ❶❹ ❷ ⓿ ❼❻❶❷❼ ❶ Tetrahydrofuran ❸ ⓿⓿ Acetaldehyde❶ ❷ Acetaldehyde ⓿ ❶❿ ❿❺ ❿ AcetaldehydeAcetaldehyde ❿❾ ❿❻❿ ❺❿ ❺❹ ❹ ⓿❹ ❸ ⓿ ❶❸ ❸ ⓿❷❹ ❹ ⓿ ❶⓿ ❶⓿❹ ❶⓿ ⓿❶ ❸❸ ❹ ❶ ⓿ ❹ ⓿ ❶⓿ ❷⓿ ⓿⓿ ❶⓿❸ ❷ ⓿⓿ ⓿❶ C— m C—C— C— C— C— RC— m C— C— RC— m C— m C— m C— m C—C— C— m C— RC— C— C— m C— C— C— m RC— C— m C— RC— C— m C— C— C— C— C— C— C— C— C— m C— C— C— m C— C— C— C— C— m m C— C— C— C— C— C— C— m mR —RC— C— C— RC— Tetrahydrofuran Tetrahydrofuran Tetrahydrofuran N A—N N A—N N A—N ❽❶❿❼❽❶❺❼❼ ❷ ❿❿❹❶⓿❸❹ Formaldehyde Ether Ether❿ ❸ ❹ TM❷ ❸ ❷ ❶ C❺ ❿ ❶ mT❻❷⓿❻❺❽⓿❸❿ VOC ❾ C❿ ⓿ ❹ ⓿ ❸❿ ❶ ❽MOX ❹⓿ ❿❸ ⓿ ❿❽ ⓿❾❿ ❷VOC ❶ ❿ ❿ ❼ ❿❿ m❿ MOX m m m ❿ mC— m C— C— Acetone ❽ ❹N ❻❸ m ❷ ❶ ⓿Pentanol ❿❻❿ ❿❿❷ ❿❾ M Acetone ❸ ❼❿ Acetone ❿ ❽ ❽❹❼ ❻❿ ❸❹ ❻❷⓿❻❺❽⓿❸❿ ❸ ❷ ❻ N❶❸ ❶ ⓿ ❷❿ ❶❻⓿ ❿ ❿❻ ❿❿ ❷❿ ❶❶ ❿ ❷ ❶❶ ❿ ❹ ❿❾ ⓿⓿❹❸⓿⓿❻ ❸ ❹ ⓿⓿❹❸⓿⓿❻ ❸ ❺❼ ❺ ❼❿❷ ❸❼ ❶❿ ❿❷❸ ❶❿ R —N R —N R —N Diethylether Pentanol Pentanol ❼¾ ❿ Pentanol ❿ ❼ ❷❿❼ ❷ ❸❿ ❸ ⓿⓿ ❿ ❿ ¶❶ ❶w ❿ N N CHexaldehyde Methane ❻ ❼ Hexaldehyde ❷ Hexaldehyde Methane Hexaldehyde Methane Acetaldehyde Methane ❿❿ ❼ Hexaldehyde Hexaldehyde ❼ ❷❶ ❷ w❶ m ⓿ ❶m m m DiethyletherDiethylether ❾❿❶ ❿❾ ⓿ ❿❿❿ ❿❿❿ ❽❶❿❼❽❶❺❼❼ ❽❶❿❼❽❶❺❼❼ ❷ ❸ ❿❿❹❶⓿❸❹ ❷ ❿ ❶ ¹m❿❿❹❶⓿❸❹ ⓿ ⓿ ❼❻❶❷❼ ❼❻❶❷❼ m m m Butanone❹ ❸ ❽ ❿ ❿ ❸ Butanone ❹ Butanone ⓿ ❸ ⓿⓿ Ether Tetrahydrofuran Ethylacetate Ether M Ether M Ether❿ ❿ M ❸ ❽❸ M ❽ ❻❷⓿❻❺❽⓿❸❿ ❶❸ ❿ M❻❷⓿❻❺❽⓿❸❿ ❸❿❿ ❻❷⓿❻❺❽⓿❸❿ ❶ ❸ ❶❶ ❻❷⓿❻❺❽⓿❸❿ ❽ ❶❸ ❶❶ ❻❷⓿❻❺❽⓿❸❿❹ ❸ ¸❽ ⓿⓿❹❸⓿⓿❻ ❶❶ ❹❿ ❸❸ ¹❽ ⓿⓿❹❸⓿⓿❻ ❶❶ ❿ ❸¸ ❶❶ ❿ ❷⓿⓿❹❸⓿⓿❻ ❿ ❹❸❽❿ ❷ ⓿⓿❹❸⓿⓿❻ ❿⓿⓿❹❸⓿⓿❻ ❸❿ ❸❷ ❸ ❿❷ ❿ ❿❷❹❸❽❿ ❿¸ ❿ ❿ ❿ m m m Heptane ❽❶ ❺ Ethylacetate ❶ ❽ ❶ ❿ ❿ ❸ ❸ ⓿⓿ ❷ ❿ ❿❸ Ethylacetate Heptane ❸ Ethylacetate Heptane ❷Heptane Ethylacetate ❶ ❽ Ethylacetate ❶ NA R ❽ ❶m N ❿ mA❿ ❸ ❿ ❿ ❸ R❷ ❷ ❿ m❿ ❸❸ ❿ ❷ ❷ m m m m ❹ ❶⓿ ❺ Hexaldehyde Tetrahydrofuran Tetrahydrofuran C C ❼ ❻ ❻ ❸ ❹ ❸ ❸❹ ❹ ❸ ❹ ❸ m C—m m Diethylether ❾ Formaldehyde Diethylether ❸ ❿ ❺ ❶ ❾ ❷ ❸ ❷ ❶ ❾ Diethylether ❻ Diethylether ❿❿❿ ❶ ❾ Formaldehyde D ❾ ❿❿❿ ❶ ⓿ ⓿ Formaldehyde ❾ ❶❸ ❶ ⓿❿ ⓿❺ ❸⓿ ❿ ❷ ❺❷ ❶ ❸ ❾ D❿❿❿ ❿ ❷❷ ❸❶ ❾ ❷❻ ❶ ❾ ❻ ❷ ❶ ❾ ❶ ❶❾ ❶❶ ❾⓿ ⓿ ❶❶ ⓿ ⓿ ⓿⓿ ❶⓿ ⓿ ❷ ❶ ❼❻❶❷❼⓿ ⓿❶ ❷❿ ❶ ❿ ❶ Methane Butylacetate Octane ❺ Butylacetate ❿ ❸ ❺ Butylacetate M❶OctaneButylacetate MOctane ❿ ❿ Octane❸ ❺ ❽❶❿❼❽❶❺❼❼ ❻❷⓿❻❺❽⓿❸❿ ❸ Butylacetate ❺ ❽❶❿❼❽❶❺❼❼ ❻❷⓿❻❺❽⓿❸❿ Butylacetate M ❿❿❿ ·❿ ❿❿❿ ❽❶❿❼❽❶❺❼❼ ❶❶ ❺❷❽❶❿❼❽❶❺❼❼ ❸ ❿❿❹❶⓿❸❹ ❶❶ ❺❷❽❶❿❼❽❶❺❼❼ ❿❿❹❶⓿❸❹ ❻❷⓿❻❺❽⓿❸❿⓿⓿❹❸⓿⓿❻ ⓿⓿❹❸⓿⓿❻ ❿❿❹❶⓿❸❹ ❷❼❻❶❷❼ ❿❿❹❶⓿❸❹ ❶❶ ❷ ❷❼❻❶❷❼❿ ❿❿❹❶⓿❸❹ ❹❸❽❿ ❷ ❶ ❿❹❸❽❿ ❼❻❶❷❼ ⓿⓿❹❸⓿⓿❻ ❶ ❿❶ ❼❻❶❷❼ ❿ ❷ ❿ Acetaldehyde ❸ ❿❶ ❿ Ethylacetate Methane ❻Methane Acetaldehyde ⓿❽ ❹⓿ ❶❶ ❷ Acetaldehyde ❿ ❿❻ ⓿ ❸❿ ❺❿ ❶❺ ❹ T ❹ ❷C ❷ ❿ ❿❸ ❶⓿ ❸ ❶⓿ ❷ ⓿ ⓿ ❶⓿ ❷⓿ ⓿⓿ ❶⓿ ❷ ⓿⓿ ⓿❶ ❺❶ ❺ Tetrahydrofuran Tetrahydrofuran ❶❹ ⓿❸ ❼❹ ❻ T❸ C ❶ Tetrahydrofuran Tetrahydrofuran CChloroform C C❻ VOC C ❸❹ ❶C ❸ ❻ ❶❻ MOX m ❸ ❶❹ ⓿❸ ❼❸ ❹ ❶❹ ⓿m ❸ ❼❹ ❻ ❸ VOC ❸ ❸❹❸❹ MOX ❸ ❸❹❸❹ m ❸ m N A—N A m m m mm m m m m m m mm m m m m m m Heptane Chloroform Decane ❸❶ Chloroform ¶ ❶❻ ❶ ❶ ⓿ Decane ❼ ❶ Chloroform Decane Heptane ❶ ❿❿❿ Decane D ❿❿❿ D ❸ ❽❶❿❼❽❶❺❼❼ Chloroform ❽❶❿❼❽❶❺❼❼ Chloroform ❸ ❶m ❶ w ❸ ❾❷❶ ❿❿❿ ❾ ❻ ❿❿❹❶⓿❸❹ ❷❶ ❻ ❿❿❹❶⓿❸❹ ❽❶❿❼❽❶❺❼❼ ❶ ❶❼❻❶❷❼ ❶ ❷ ❻ ❿❿❹❶⓿❸❹ ❼❻❶❷❼ ❶ ❶ ⓿ ❼❶ ❶⓿ ❼ ❶ ❶ ❶❶❼❻❶❷❼ ❶ m m C— m m m mm mm m m m m mm m m ❿ ❺ R —N A Butylacetate Heptane ❿❼ ❹ ❸❿ ❸❶⓿⓿⓿ ❶ ❶ C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C—A C— C— C— m m m m m m m m m m m m m m Methane ❷ Methane Hexaldehyde ❼ Methane Methane ❷ ❺ Hexaldehyde M ❺❹ Hexaldehyde ❹ ❺ ❺❹ ❶⓿ ❼ M m ❺❹❷ ❶⓿ ❷ ❶⓿ ❷ ⓿⓿ ❶⓿ ❺ ⓿⓿ ❺ m ⓿⓿ ❷ ⓿⓿❺w ❺ m ❺ Octane m m C— A m ¶ m m Trichloroethylene Isoprene ❷ Trichloroethylene ❿ ❷ ❹❸❽❿ ❿ Trichloroethylene Trichloroethylene C Isoprene CChloroform Isoprene ❻ ❻Isoprene ❷ Trichloroethylene Trichloroethylene ❿ C ❿ ❷❶ ❻❿ ❷ ❷ ❻ ❶ ⓿ ❼ ❶ ❸❹❸❹ ❶ ❸❹❸❹ ❸ ❸ N A ❽ ❶ Heptane Heptane Octane Octane R m m ❿ ❿ ❸ N A R m m ❶ ❶ ❿ ❿ ❿❸ ❷ ❽ ❶❶ ❿❽ ❶ ❿❿ ❸❿ ❸❿ ❸ ❸ ❶ ❷ ❷ C—R — Decane Heptane NH3 ❶ ❾NH3⓿ ❶ ❷ ❻❽ ❸ ¸ Ethylacetate Heptane ❻ ❶❸ ❶⓿⓿❷⓿❿❿ ⓿ ❶❻ ❶ ❹❻❶❶ ⓿ Benzene NH NH3 ⓿ ❹ ❻ Benzene ⓿ M ❺ Ethylacetate ❶ H ❷NH ❺❹3❿ Ethylacetate ⓿M ❶ ❷ ❻ ❶NH❹❸ ❸ ❽ 3⓿ ❶ ❻❸ ❸ ❽ ❷ ❸ ❿ ❶⓿ ⓿❶ H ❺❸❽❶⓿ ❻❶❹❸ ❸❶ ❿ ⓿ ❻❶⓿❸ ❸❶ ❷⓿ ⓿⓿ ❶ ⓿⓿ ❿ ❷⓿ ❿ ❶ ⓿❶❶ ❹ ❻ ❷❻ ⓿⓿ ❿❶ ⓿ ❶⓿ ❿❶⓿❻❹ ⓿ ❶ ⓿ ❷ ❻❺❷❶ ⓿⓿ ❶❶ ❿❺ ❿ ❻❶ ⓿ ❶❹ ❶ ❻⓿ ❹ ⓿⓿ ⓿❻ ⓿❺ N A—N AN A—N A D Benzene ❺ 3 Benzene Trichloroethylene Decane Decane ❷ ❸ ❺ ❺ ❹❸❽❿ ❹❸❽❿ ❿ ❿ mA Octane Butylacetate ❶ Butylacetate Butylacetate ❶ ❶ ❶ ❶ ❶ ❺ Octane Octane Octane O H2 ❿ O ❿ ❿ ❿ ❿ ❿ mC— C— mC— C— mC— m C— C— C— C— C— C— C— C— mmC— C— mC— C— C— C— mC— C— C— mmC— C— C— mC— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— mC— C— C— C— C— C— C— mC— mC— C— C— A—N Isoprene H2 H2 ❷ ❿¿ ❺ ❿❽ H H H H ❷ H❸ ❿ ❷ ❸ ❺ ❶❿❷ ❺ ❶❿ ❿ ❸ ❺❶ ❿❽ ❺❶ ❿❽ ❽ ❶ m m m m m m m m m m m m m m m m m C— C— Ethylbenzene ❸❹❸❹ ❸ ❶ Ethylbenzene 2 Ethylbenzene 2 NH3 Isoprene Ethylbenzene 2 ❻❸ ⓿ ❹❻ ❻❷ ⓿ C— R —N R —N A R —N M Isoprene ❿ ❻D ❸ ❻❷⓿❻❺❽⓿❸❿ ❿ ⓿ ❼❿ D ❶❶❶ ⓿⓿❹❸⓿⓿❻ ⓿❿ m m m m m m m m m m m m m m m Decane ❸ DecaneChloroform ❸ ❸ Decane Decane Chloroform ❸D D ❶ Chloroform ❸ ❶ ❾ ❶ ❸ ❾ ❶ ❹❸❽❿ ❹❸❽❿ ❸ ❻ ❿❹❸❽❿ ❶ ❿ ❹❸❽❿ ❼❶ ❶⓿ ❼ ❹❸❽❿ ❶❿ ❶ ❶❶ ❿ ❶ ❼❶❸ ❿ m m m m m m m R — — Benzene M NO2 ❼NO2 ·❷ ❷ ⓿ ⓿ ❷¶ ½ ❷ ❶Chlorobenzene NOChlorobenzene NO ❸ ❶ ❶ ❿❿❿ ONO2 ❶❼ ONO2 ¶ ❸❷ ❶ ❸❷ ❿ ❶ ❿¿ ❷ »❸ ❸ ❶❶ ❶ ❿¶ Chlorobenzene ❶ ❼ H2 2 ❶Benzene Chlorobenzene ❼ ❶ ❽⓿❼ ❷¶ ❿❿❹❶⓿❸❹ ❷❶ ⓿❶ ⓿❷ ❷ ⓿ ⓿ ❷ · ❷ ⓿❿⓿ ❼ ❷ ¿ ❶ ❼ ❸¶ m C— N m Rm 2 Trichloroethylene ❷ Benzene Trichloroethylene ❷ ❽❶❿❼❽❶❺❼❼ Trichloroethylene ⓿❷ ❶❽ ❷ ❺ ❷ ❿ ⓿ ❶⓿❶ ❷⓿ ⓿⓿ ❿❷ ❿ ⓿❼❻❶❷❼ ❻ ❿❶ ❸❹❸❹❻ ❶ ❸❹❸❹ ❶ Isoprene ❿ Isoprene ❿ D Isoprene D Isoprene D D ❿ ❾ D ❿ ❾ ❾ ❿ ❾ ❸ ❾ ❶ ❿ ❶ ❶ ❶ ❶ Ethylbenzene CO Toluene H ❸❸ ❶CO ❸⓿❶❿ ❹❺❷❿⓿ ❾ ❷❺ ⓿⓿⓿❷ ·⓿❹❽ ⓿ ⓿⓿ Toluene ❷COC CO2 Toluene NO ❻❸ ⓿ ❸ ❸ Toluene ❶D❸ CO ❸ ⓿❶ D❶ CO ❸❿⓿ ❸❶ ❹❸❿ ❺❷ ❶ ❹❿ ❸❺ ⓿ ⓿❷ ❶❿ ❿⓿❸ ❾ ❷ ❹❾ ⓿ ❺ ❺⓿ ❹❸❽❿ ❷❷ ⓿ ❿❺⓿ ❷ ⓿⓿ ❹❸❽❿⓿⓿ ❾❷ ❹❽ ❷⓿ ❺❹ ❿ ⓿⓿❽❽ ⓿ ⓿❸ ⓿❷⓿❿ ❷ ⓿ ❶⓿ ⓿❹ ❹❸❽❿ ❷ ❽ ⓿½ ❷⓿ ❿ ⓿ ❿❷ ❷ ❽ C— A Benzene ❷ ⓿❶❽ Benzene ❶ NH❶ 3 ⓿⓿❷⓿❿❿ ❷ ❻ ⓿ ❶❶ ❶ ❻❸ ❽ Benzene EthylbenzeneEthylbenzene ❶❶⓿ M ❷NH ⓿3❷ ⓿ M ❷⓿❶NH❿❽ 3⓿ ❶ ❻❽❿ ⓿ ❶ ❹❶❻ ❷⓿❸ ⓿❶❽ ❻❶❸ ❶ ❸❹❸❹ ❻❷⓿❻❺❽⓿❸❿⓿ ❻❶⓿❸❶ ❷❿ ⓿⓿⓿ ❿❷ ❿ ❷⓿❻❿ ❷ ❸⓿❶❽ ❿❶ ❶❶ ❻ ❷ ❶ ❶❶⓿⓿❷⓿❿❿❻ ❶ ❻❷⓿❻❺❽⓿❸❿ ❶ ❸❹❸❹⓿⓿❹❸⓿⓿❻ ⓿❸❹ ❺ ❻ ⓿ ❶❶❶❶ ❹⓿❻ ❷ ⓿ ❿⓿⓿❹❸⓿⓿❻ ❶❶⓿⓿❷⓿❿❿❻ ❶❶ ❷ ❿ H2S D Benzene M M ❸❹❸❹ ❸❸❹❸❹ ❿❺❸ ❸❹❸❹ ❸❿ ❺❸ —N A H2S ❶H❿ 2S ❷ ⓿⓿❿❺ D❿ H 2S ❺ Xylene ❾H❿ 2S D ❶ ❾ H❿2S ❶❿ ❿ ❿ ❾ ❿❼ ❶ ❺ ❶ ❶ ¼ ❼ H2❿ ❶ ❷ ❷ ❷ ❼ ·❿ ⓿⓿ ⓿ ❼ ❿ ⓿❿ ⓿ ❿⓿ ⓿❺ ⓿ ❿ ❿ C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— —C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— C— Xylene Xylene Xylene A—N O Chlorobenzene Ethylbenzene ¶ ❶ CO Chlorobenzene ❺ ❶M Chlorobenzene ❿ ❶M H❺ ❼ ❹ ❼ ❶ ❷❶⓿ ⓿❷⓿ ❷❷ ⓿⓿⓿ ⓿ ❷ · ❷ ❷ ❼ ❼ · ¼ C— C— ❷ Ethylbenzene ❽ ❷ Ethylbenzene Ethylbenzene H2 M H ❷H 2 ❷ ❽ ❿❿❿ ❶❷❽❶❿❼❽❶❺❼❼ ❷⓿❶❽ ❺❷ ❿❿❿ ❿❽ ❺❷ ❸ ❿ ❷ ❷❽ ❽❶❿❼❽❶❺❼❼ ❷ ❿❿❹❶⓿❸❹ ❽ ❷❼❻❶❷❼ ❿❿❹❶⓿❸❹ ❽ ❼❻❶❷❼ 2/Fe3O4 [53] SiO2/Fe3O4 [53] [53] 4 [53] [53] ZnO [36] ZnO [39] ZnO [40] ZnO [37] ZnO [38] ZnO [76] Au@ZnO [65] [36] ZnO3 [39] ZnO3 [40] [37] ZnO3 [38] In2O3 [75] [66] [67] [36] ZnO2 [39] ZnO [75] [40] [36] [36] [37] [39] [39] 2O4 [38] [40] [40] [66] Au [76] [37] M M [67] Ag [65] [38] [38] [76]2 [76] [65] [46]22 [36] [45]23 [39] [47]23 [40] [36] [73]23 [37] [39] [48]33 [38] ZnO3 [38] Pd@ZnO [71] [49] [40] [48]32 [76] [37] 3 [65] [49] [51]2 [76] [49] 3O4 [75] [66] Ag [67] [66] [67] [75] [66] Au [75] O4 [67] Au [66] Ag [67] SnO2 [46] SnO2 [45] SnO2 [47] Ce-dop. SnO2 [73] [48] [48] Pt@WO3 [48] [49] TiO2 [76] Pd@TiO2 [65] [51] [52] In2O3 [50] [71] CuO2 [74] [46] 2 [45] 3O4 [69] 2 [47] 3O4 [72] [73] SiO22 [53] WO32 [53] [48] Au@WO3 [48] 2O3@WO3 [49] ZnO [36] Y2O3 [53] 3 [48] ZnO [39] La2O3 [53] 2O3 [119] TiO2 [51] [37]2/SnO2 [102] 2 [52] CuO/SnO2 [45] [50] CuO [74] O44 [69] [70] O4 [72] Ag2 [53] CeO2 [53] Y2O3 [53] La2O3 [53] Au/La2O3 [119] 2 [102] [45] R [70] [71] [46] SnO2 [45] [71] [71]2 [47] [46] [73] [48] [45] [48] [47] [48] [73] [48] [48] [51] [48] 2 [52] [71] 2O3 [50] [74] [51] [46] [65] [52] 2 [45] 3O4 [69] [71] [50] 3 [47] 3O4 [72] 2 [46] 2 [74] [73] SiO2 [53] 2 [45] 4 [48] [69] 2 [53] [48] O2334 [47] [72] [53] 2 [73] La2O33 [48] [53] [53] WO323 [53][48] Au/La2O3 [119] 3 [48] 2O3@WO3 [49] N2 [75] 2 [102] [53] 3 [51] 3 [48] 2O32 [52] [53] 2 [45] 3 3[119] [50] [74] 2 [102] [51] R24 [52] [45] 3O24 [69] 2O4 [70] 2O3 [50] Au [72] [74] 3O24 [53] 3O4 [69] Co3O4 [69] 2O42 [53] [70] 3O4 [72] Co3O4 [72] O3 [53] [53] O23 [53] 2 [53] 2O3 [119] 2O3 [53] TiO2/SnO2 [102] La2O32 [53] [45] 2O3 [119] 2 [102] ZnCo2O24 [45] [70] ZnCo2O4 [70] Chlorophenol ❾ ChlorophenolChlorophenol ❾ ❾ Chlorophenol ❾ ❷ ⓿❶❽❷ ❸❹❸❹ ❸❹❸❹ ❶❶⓿⓿❷⓿❿❿❻ ❸ ❶❶⓿⓿❷⓿❿❿❻ ❸❹❸❹ ❶❶ ❶❶ ❸ R — D ❸ H2S Toluene Toluene ❸ O ❸ ❸ ❶O ❸❸2⓿ ❿ ❶ ❶❸ ❸⓿ ❿ ❶❹❿ ❺ ❸ ❷ ❹❿❺ ❶ ⓿❷ ❿ ❾⓿ ❷ ❾❺ ⓿ ❷⓿ ❺❷⓿ ⓿❶ ⓿❷ ❹ ❽⓿ ❺❹ ⓿ ❽ ⓿ ❷ ❷ ❸ ❷ ❶❸ ❷⓿⓿❷ ❶ ❶ ❶ Toluene ¸ ¸ NO NO NO ❿ ¶ ¸ ❸ ❼ ❿ ❺¶ ¿❿ ❸ ❶ M ¹ H º❽ ·❽ ¿❸❹ ¾❻ ❿· º❿ ·❿ ❸ ¹❿ ❸❽ ½ · Chlorobenzene ❶ ❼ Chlorobenzene 2 ❶❷ ⓿ ⓿ ❷ ❼ ❷ ❼ Chlorobenzene H Chlorobenzene ❶ ❶ H C ❷ ❼ ❶❻ H ⓿ ⓿ C 2 ❼ ❶ H❷ ❶ ❻❷ ❶❿ C ❼ ❶ C ❶❷ ❶ ❷ ❷ ⓿ ⓿❻ ❷❷ ⓿ ⓿ ❼❷ ❷ ❷ ❷⓿⓿❷ ❼ ❼ ❽ ❼ ❷ ❼ ❷ ❷ ❷ [53] [37] Acetone M ❽M [65] 3 [48] 3 [49] In[75] Y2O3 [53] AuY[66] [67] 2 [52] 3 [50] [74] [69] 4 [72] [53] 2 [53] [53] 3 [53] [119] [102] 2 [45] 4 [70] ❽ ❹❻❸❷ ❿❶⓿❿❻❿❿❿❷ ❿❾❹ ❼ ❹ ❻ ❷ ❶❸ ⓿ ❷ ❿❶ ❻⓿ ❿ ❹ ❿❿❻ ❿ ❸❿ ❷❷ ❶❿ ❿ ⓿❷ ❿ ❻❿ ❾ ❿ ❹ ❿ ❿❾ ❸ ❷ ❹❼❿ ❿ ❿ ❸❺❼ ❿ ❿❾❺ ❼ ❹ ❸❼ ❶ ❼ ❿ ❿❸ ❺ ❶ ❼ ❿❶ 3O3 2Ag SiO2/Fe3Ag Au@ZnO Pd@ZnO Au@ZnO Pd@ZnO Au@ZnO Pd@ZnO Au@ZnO 2Pd@ZnO CuO Au@ZnO Pd@ZnO 2O2 CuO 2 2O2 2 CuO CuO Co3Au 2Au ZnO ZnO ZnO ZnO-CuO ZnO ZnO-CuO ZnOZnO ZnO ZnOZnO ZnO ZnOZnO ZnO ZnO ZnOZnO ZnO 2ZnO 2ZnO 2ZnO ZnO 2ZnO ZnO ZnO ZnO 3ZnO ZnO ZnO ZnO-CuO ZnO-CuO ZnO-CuO ZnO-CuO Acetone Acetone Acetone WO Fe2O3@WO Ce-dop. SnO CeO Pt@WO Pd@TiO SnO WO Pt@WOSnO [45] 3WO O3@WO Pd@TiO Pt@WO O3@WO Pd@TiO SnO 4WO CeO Pt@WO3 [48]Pt@WO SnO CeO 3@WO Pd@TiO Pt@WO SiO2/Fe3O Pd@TiO 2OCeO Au/LaCeO /Fe3O Au@WO SnO SnO SnO Co23O SiO CuO/SnO ZnOSnO SnO SnOSnO SnOSnO SnOSnO Au@WO Au@WO TiO TiOSnO2TiO SnO SnO CuOSnO SnO Au@WO SnO Y23O SiO Au@WO TiO CuO/SnO 2O TiO CuO/SnO 4SiO SiO CuO/SnO CuO/SnO ❸❸ ❶❸⓿❶❿ ❹ ❺ ❷ ❿ ⓿Toluene ❾ ❷CO❺ ⓿ ⓿ ❷ ❸❸ ⓿❹ ❶ ❽ ❸⓿ ⓿❶ ❿ Toluene ❷ ❹❺ ❷Xylene ❿ ⓿ Xylene ❾ ❸❸ O❷❸❺ C❸ ⓿ ❶D O❸ CO ⓿❸ ⓿ C❷ ⓿❶ ❶D❶ ❿ CO ❸ ⓿❿⓿ ❹ ❶ ❷ ❶ ❽❹ ❿❿❺ ❸❸ ⓿❷ ❷❹❿❺ ⓿❷ ❸ ⓿ ❶❸⓿❶❿ ❷❺ ❿ ❸ ❾⓿⓿⓿❹❸⓿⓿❻ ❷ ❾❺❼ ⓿❷ ❹❺❷❿⓿ ⓿❺ ❷❼❸❸ ⓿⓿ ⓿ ❿❷❹ ❶❸⓿❶❿ ❽ ⓿ ⓿ ❾ ⓿ ❹⓿ ❿ ❽ ⓿ ❷❺⓿⓿❷⓿⓿ ⓿ ❷ ❿ ❹❺❷❿⓿⓿ ❶ ❿ ❷ ⓿❹❽ ❾❶⓿ ⓿ ⓿ ❷❺⓿⓿❷ ❷ ⓿ ❼ ⓿❹❽⓿⓿ ⓿ ❷ ❺ TiO2/SnO Y22/SnO 2O 2/SnO SiO22/SnO Toluene Toluene ❹ ❺ ❶⓿ ❹ ❶ ⓿⓿ ❶ ❼ ❺⓿⓿❹❸⓿⓿❻ ❶ ❶ Xy ene ❸ ¶ ¿ · M HO ❿ HO ❻❷⓿❻❺❽⓿❸❿ ❶ ❶ HO ❶ ❶ ❶❶ ¼ ❸❼ ❸M ❶¿ ❼ ❿ ❷ ❸ ❿ ❻❷⓿❻❺❽⓿❸❿ ❷⓿⓿❷ ❷⓿⓿❷ ¿ ❶❶❷ ❷ ¿ ❷ ❿ Co Co In Co 2OIn Co Co Co 2O3 In Co Co /Fe ZnCo ZnCo La ZnCo ZnCo La O3Y Y Butanone ❷ ⓿❶❽ ❶❶⓿⓿❷⓿❿❿❻ ⓿⓿ ❶❶ Butanone Butanone Butanone ❸ ⓿⓿ ⓿ ⓿ ⓿⓿ 2O3 [53] Ag SiOAu@ZnO CeOPd@ZnO 2 ZnO ❿ ❹❸❽❿ In23ZnO-CuO ❾ ❾❶ ❿ Au/La Au/La Au/La Chlorophenol Chlorophenol ❺ ❿ ❿ ❺❼ ❺ SnO Fe2WO 2/Fe3OPt@WO 3@WO Pd@TiO Co3O 3O 2CeO /Fe32O Au@WO SiO 2O Co3CuO/SnO Xylene ❶❿ ❷ Xylene❼ H2S❿ ⓿⓿ ❶ ❿ ❿ ❷ ❿Xylene Xylene ❼D ❽❶❿❼❽❶❺❼❼ ❿ ❸ XH❿ 2S ❶ ❸ ❷ H❿ ⓿2S ⓿❷ ❿ ❸❸ ❸ ❶❿ ❿ ❸X ❼ ❸❷ ❿ ❿❼ ❹❺❷❿⓿ ❸ ⓿⓿ ❶❿❿⓿ ⓿❼❷ ❿ ❿❿ ❿ ❸ ❿ ⓿❹❽ ⓿⓿❿⓿❹❽ ❶ ❿❿❿❹❶⓿❸❹ ❿ ⓿⓿❿❶ ❿ Ce-dop. Ce-dop. SnOCe-dop. Ce-dop. D D D D ❸❸ ❶❸⓿❶❿ ❶❸⓿❶❿ ❹❺❷❿⓿ ❾ ❷❺⓿⓿❷ ❾ ❷❺⓿⓿❷ ⓿ ⓿❷ ❷ /SnO Ch o opheno Formaldehyde ❸❿ ¾❺ ❷❸ ❷❶ ❷❾❻ ❶❾ ❶⓿⓿❶⓿⓿⓿❷ O Formaldehyde ❶ ❿ Formaldehyde O ❿❿❿ ❸Acetone ❿❺ ❸❿❺ Formaldehyde ❷ ❸❻ ❸ ❷ ❷ ❸ ❸O❶ ❿ ❾ ❺ ❷❻ ❶❶ ❾❷ ❻ ❷ ❸ ❷❿❿❹❶⓿❸❹ ❶ ❶ ❾ ❾ ❻ ❶❶ ❾⓿❹ ⓿ ❶ ❶ ⓿⓿❼❻❶❷❼ ⓿⓿ ❶❿ ❶⓿ ❾❿❿❿ ⓿❷ ⓿❶ ⓿❶⓿ ❷ ❽❶❿❼❽❶❺❼❼ ❿ ⓿❿ ❶❸❶ ❶ ⓿ ❿⓿ ⓿ ❶ ❷ ❶❿ ❷ ❶ ❼❻❶❷❼ SiO 2SiO O ❽ In Co La ZnCo 4 [53] 4 [53] [53] ZnO [40] ZnO [38] ZnO [76] Au@ZnO [65] 2O3 [36] 2O3 [39] [40] [36] [37] ZnO2 [38] [39] [40] [76] [75] [37] [65] [38] [66] Au2 [76] [67] [65] [75] [66] Au [75] O4 [67] Au [66] Ag [67] Pd@ZnO [71] SnO2 [46] SnO2 [45] SnO2 [47] Ce-dop. SnO2 [73] WO3 [48] Au@WO3 [48] Pt@WO3 [48] Fe2O3@WO3 [49] TiO2 [51] Pd@TiO2 [52] In2O3 [50] CuO [74] Co3O4 [69] Co3O4 [72] 2 [53] 2 [53] [53] [53] 2O3 [119] [102] [71] [70] [46] 2 [45] [71] SnO2 [47] 2 [46] 2 [73] WO23 [48] [45] [48] SnO23 [47] 2 [73] [49] [48] 3 [48] [49] [51] 3 [48] Pd@TiO2 [52] [50] [74] [51] O24 [52] [69] O34 [50] [72] [74] 2 [53] 2 [53] 2 [53] 2 [53] 2O3 [119] 2O3 [53] TiO2/SnO2 [102] La2O32 [53] [45] 2O3 [119] 2 [102] ZnCo2O24 [45] [70] ZnCo2O4 [70] ❽ ❽ ❹ ❹ ❷ ❾ ❻❶ ❸ ⓿ ❷ ❿ ❻ ⓿❿ ❿❿ ❻❿ ❿❷ ❿ ❿❿ ❷ ❿ ❿❾ ❿ ❾ ❸ ❹❼ ❸❺ ❼ ❿ ❶ ❺❼ ❼ ❶ ❿ ❸ ❶ ZnO TiO TiO2 TiO TiO22TiO TiO ❾ ❾ D Acetone ❾ ❾ ❾ ❷ ⓿❶❽❷ ⓿❶❽ ❾❷ ❻ ❶❶⓿⓿❷⓿❿❿❻ ❶❶⓿⓿❷⓿❿❿❻ ❶❶ ❶❶ In2Au/La Chlorophenol Chlorophenol ChlorophenolChlorophenol C ❿ X ❿ X ❿ C ❿ ❶❿ ❿ ❶❿ ❷ ❼ ❿ ❼ ❿ ⓿⓿❿ ⓿⓿❿ ❿ ❿ ❿ ❿ Fe 4Fe WO33Fe O33Fe C ❻ ❸ ❸ C ❸ Ce-dop. A e one Acetaldehyde ❶ ½❿ ❿ ⓿ ⓿ ❶ ❷ ⓿ ❶ D Acetaldehyde D Acetaldehyde ❿ ❿ ❿ ❿ Acetaldehyde D❿ ❿ ⓿ ⓿ ❶ ⓿❷ ⓿ ❶⓿ ❷ ⓿❶⓿ ⓿ ❶ ❶ ❷ ⓿ ❶ ❹ ❻ ❸ ❷ ❶ ⓿ ❿¹ »❿ ❷ ¸ ❿Acetone ·❿ ❾ ❹ ¶ ❽❷⓿⓿❷ ¿❸ ❷❼ » C ❼ ❷¿ ❼¿ Butanone ¿❿ ·❿❽ ¿ ¿❻ ¾❿ ¹❿ ¸ ❿❹❻❸ ❸❹❸❹¼❿ ¿ º ❸ ¼❿❾❹ ¿ ❶❶ ¸ ❹❸❽❿¶ Butanone A ❿❸ ❹❿❸ ⓿ ⓿ ⓿❿⓿ SiO 2O ❽ ❻❿❿ ❸❼❿ ❹ ❺ ❻ ❶❿ ⓿ ❸❿ ❶ ❻❿ ❿❿❷ ❽ ❾ ❹❹ ❻ ❷ ❷ ❽ ❽ ❹❸ C❹ ❸ ❷ A❻ ❼❶❸ ❿ ⓿❷ ❺❿❶ ❻⓿❶❽⓿ ❽❿❿ ❼❿ ❿ ❿ ❿ ❹❻❸ ❸❷❷❿❶ ❿ ❷ ❷❶⓿❿❻❿❿❿❷❾ ❽❾❸ ❹❼ ❸❺❼ ❿❷❶⓿❿❻❿❿❿❷ ❺❼ ❿❾❹ ❿ ❸❼ ❶ ❸ ❸❼❿❺ ❶ ❹❸❽❿ ❿ ❼❿❸❶ ❸❼❿❺ ❼❿❸❶ Ag SiO2/Fe3Ag Au@ZnO Pd@ZnO Au@ZnO Pd@ZnO CuO CuO ZnO ZnO Au/LaZnO ZnO ZnO ZnO ZnO-CuO ZnO ZnO ZnO ZnO ZnO ZnO-CuO ZnO-CuO Acetone Acetone Acetone ⓿❶❽ ⓿❶❽❷ ⓿❶❽❷ ❶❶⓿⓿❷⓿❿❿❻ ⓿❶❽ ❶❶⓿⓿❷⓿❿❿❻ ❶❶⓿⓿❷⓿❿❿❻ ❺❶❶ ❶❶⓿⓿❷⓿❿❿❻ ❶❶ ❶❶⓿⓿❷⓿❿❿❻ ❶❶ ❶❶ ❿ ❿ SnO Fe2O3@WO Pt@WO Fe2O3@WO Pd@TiO CeO Au/LaCeO /Fe3O CuO/SnO 2O SnO SnO Ce-dop. SnO SnO Au@WO Au@WO TiO O SiO SiO CuO/SnO CuO/SnO ❺ C ❾ ❾ ❶⓿ ⓿⓿ ❺ ❹ ❶⓿ ⓿⓿ ❺ 2 Hexaldehyde ❸❸ ❼ ❶❸⓿❶❿ ❹❺❷❿⓿ ❾ ❷❺⓿⓿❷ ⓿❹❽ ⓿ Hexaldehyde Hexaldehyde ❷ Formaldehyde Hexaldehyde ❿ ❼ ❿ ❼ ❼ ❿ TiO2/SnO SiO22/SnO Fe Bu anone M Formaldehyde ❸❿❺ C❸ ❿ ¸ ❺❷ C ❸⓿ ❷ ❷ ❶ ❸ ❶ ❾❷ ❶❻ ❶ ❾❸ ❻❼ ❼ ❶ ❷❾ ❶ ❶❾ ⓿⓿ ❶❶ ⓿⓿⓿ ⓿❶ ⓿ ⓿❷⓿ ⓿ ❶ ❷⓿⓿❷❷ ❿ ❶ ❷⓿⓿❷ ❿ ❷❽ ❷❼ ❼ ⓿⓿ Co Co Co La ZnCo La Y Y Butanone ❸Butanone ⓿⓿ Butanone Butanone ❸ D ❷ ⓿ ❽ ❷ ❽❹❻❸ ❷ ❸ D ❾❷ ❷ ⓿ ❸⓿ ❽ ⓿ ⓿ ❾ ❽ ❽ ⓿⓿ ❸ ❽ ❶ ❶ A ❶ A ❶ ⓿❶❽ ❽ ❶ ❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❷❶⓿❿❻❿❿❿❷ ❿❸ ❿❾❹ ❿ ❿❾❹ ❸❼❿❺ ❸ ❸❼❿❺ ❷❼❿❸❶ ❼❿❸❶ Au/La Ethylacetate X ❽ ❶❿ ❶ ❷ ❼ ❿ ❿ ❿ ❸ ❸ ⓿⓿❿ ❷ EthylacetateEthylacetate ❿ Acetaldehyde Ethylacetate❿❽ ❷ ❸⓿❶❽ ❷ ❽ ❷ ❶❶⓿⓿❷⓿❿❿❻ ⓿❶❽ ❿❶❸❿ ❿❶❶⓿⓿❷⓿❿❿❻ ❸⓿ ❿❶ ❸ ❶❶ ❷❿ ❿ ❿ ❸ ❶❶ ❷ ❸ ❶❶⓿⓿❷⓿❿❿❻ ❶❶ ❶ Ce-dop. ❸❿ H Acetaldehyde ❾❿ ❿❿ m❿ ❿ ❶ ❸❸ ❿❷❸❸ ❶❸⓿❶❿ ❶❸⓿❶❿ ❹❺❷❿⓿❶⓿ ❹❺❷❿⓿ ⓿❷ ⓿❾ ❶ ⓿ ❷❷❺⓿⓿❷ ❾ ⓿❶ ❿ ❶ ❷❺⓿⓿❷ ⓿❹❽ ⓿❹❽ ⓿ ⓿❷ ❷ Fo ma dehyde Formaldehyde ¸❺ ¿❷ ❸ ❺º ❷❶❾❻ · ❶❾ 10¸ 20 load Formaldehyde ❶ ❸⓿❿ ⓿❺ ❶⓿⓿ % ·50 60¶ ❷⓿ ❸ ❷ ❷ 0 70 10¾ ❶❶ ❿ 80 20 90» ❾ ❻ FormaldehydeFormaldehyde ❸ ❿❶ ❶ 70 80C❶ ❺❸ ❺ ❶ ❷⓿ ❸ ❼ ❺ C❶ ⓿ ❷❶ ❷❼ ❸⓿❶⓿ ❾❷ ❸ ⓿ ❶❻❶ ❿❺ ❷❾❶ ❷¶ in ❻ ❶ %¾ ❼ ❸ m ❶❷❶❾❻ ❼ ❾ ❼¶ ❶❶ ❸❾⓿ ❿❺ 20❷⓿⓿❷ ⓿❶ ⓿⓿ in❷⓿⓿❷ ⓿⓿ ❷❶⓿ ❸⓿❷ 60¶ ⓿ ❶❾ ❷❶❾❻ ⓿❶ ❷ ❷ ❷⓿⓿❷❿ ❶⓿⓿❶⓿⓿⓿❷ ❶ ❷ ❷⓿⓿❷ ❼ ❸❹❸❹ ❷⓿⓿❷ ❼ ⓿⓿ ❷· ❶❾ ❶❿ ❷❼ ❶⓿⓿❶⓿⓿⓿❷ ❸ ❷❼¶ ❸❹❸❹ ❼¿ ❶❿ ❸ SiO 30CButylacetate C 90 CResponse %60050❸ 0 ❸ ⓿⓿ TiO N m Response load in % 0 Response 30 in 40 100 40 50 ❷ in ❷3050 ❽ ❿❽ ❽80 100 ❿ CButylacetate ❾ ❿❿ ⓿❿ ⓿❿ ❶❷ ⓿ ❶ O60 Hexaldehyde Butylacetate Hexaldehyde ❿ ❿ ❸❸ Butylacetate ❿❿ 100 ⓿ X ⓿ ❶M ❺ Response ❼load ❷X ⓿ ❼% load ❿❿ ❶ ❺ 0 Response ❶❿ 100 20Response load 1030 in ❶❿ ❷ 40 ⓿ % load ❷ ❿❿⓿ ❶⓿ 40 ❷ ⓿ ❶ 70 10 ⓿ ❷ 80 20 70 ⓿❼ 90 1030 ❶❷❺⓿⓿❷ 80❿ ❷ 20 100 ❼❶ 40903050 ⓿⓿❶❷ 100 ❿ ❷ 40 ❶ 60⓿⓿❿ 5070 ⓿ 6080 ⓿ ⓿⓿❿ 70 ❶ 90 ❷❷ ⓿ 90 100 ❿ ⓿⓿❶❷ ❿ ⓿ ❶ A e a dehyde Acetaldehyde Chloroform A ❽ ¿ ❹❻❸ ❷❶⓿❿❻❿❿❿❷ ¿❶Number ⓿ Acetaldehyde ❿ ❶ ❻ ❷ ❸ Number ❸❼❿❺ ❿❾❹ ❹❶ ⓿❺ ❼ ❶❻ ⓿ ❼ ❶ ❽ ❼❿❸❶ ❷ ❶❾ ❸ C Acetaldehyde ❿❹ ❺ C Chloroform Acetaldehyde ❻ Chloroform D ❼ Ethylacetate ❶ ❽ ❶ ❾ Chloroform Ethylacetate A❸❸ C ❿ ❶❸⓿❶❿ ❼m ❶ C Number C ❶❸⓿❶❿ ❼m Response ❸ ❽ ❶ ❶ ❸❸❸ Number ❾ ❽ ❹❺❷❿⓿ ❿❺ load❶in ❸ ❶ ❾ ❸❸❶❸⓿❶❿ % ❹❺❷❿⓿ ❿❺❶ ⓿ ❷A❸❸❶❸⓿❶❿ ❸❶ ⓿ ❾ ❻ ❷❷ Number 0 M ❶❸⓿❶❿ ❷❶❾❻ ❸❶ ❷❺⓿⓿❷ ❹❺❷❿⓿ ❹❸❽❿ ❼ 10❸ ❾❷❶❾❻ ❻ ❷❹ Number 20 ❷❺⓿⓿❷ ❹❺❷❿⓿ ❷⓿⓿❷ ❶ ⓿ ❸ 30❺ ❼ ❹40 ❿ ❻ ❹❺❷❿⓿ ❷⓿⓿❷ ❶❶ ⓿ ⓿ ⓿❹❽ ❾❼ ❻❺ ❿50 ❼ ❸❿ ❿ ❶ ❶ ❸ ❷❺⓿⓿❷ ❻ 60¶ ⓿ ❽ ⓿❹❽ ❾ ❶❾ ❿❸ ❷ ❼ ⓿ ❷ ❶ 70❶❾ ❶ · ⓿ ❸ ❽ ❸80 ❷ ❾ ❶❾❼ ❷❿ ⓿ ❷ ❶ ❹ ❷ ❼ ❶ ❾ ❷❺⓿⓿❷ ❶⓿⓿❶⓿⓿⓿❷ 90 ❸ ⓿❹❽ ❺❶⓿⓿❶⓿⓿⓿❷ ❿❷⓿⓿❷ ❹ 100 ❷ ❼❻ ⓿❹❽ ❺ ❼ ❶ ❻ ❽ ⓿❹❽ ❼ ⓿ ❶❿ ❾ ❽ ❿❷❷ ❶❿ ❾ ¶ ❿ ❷ ❹❸❽❿ ❼ ❿ Hexaldehyde ❼ Hexaldehyde ❼ Hexaldehyde Hexaldehyde H ❼ ❶❿ A ❶❿ ❼ H ❷ ❷ ❶❿ H ❼ ❶❿❷❾D ❼ ❶ H ❶❿ ❷ ❿❼❷ ❿❶ ❹❺❷❿⓿ ⓿⓿❿ ❼ ❼ ⓿❹❽ ⓿⓿❿ ❿❼ ❶ ❿❼ ❿ ⓿⓿❿ ❿ ⓿⓿❶❷ ❿ Hexa dehyde Trichloroethylene ❷ ❸ ¼ ⓿⓿ D X Trichloroethylene Trichloroethylene X ❸❸❾X A❸❸ Trichloroethylene ❷ X ❶❸⓿❶❿ ❷ X ❶❸⓿❶❿ ❿❿ ❹❺❷❿⓿ ❷❿❿ ❹❺❷❿⓿ ❸❸ ❶❸⓿❶❿ ❷❺⓿⓿❷ ❾ ❷❺⓿⓿❷ ⓿❹❽ ⓿❾ ❾ ⓿⓿❶❷ ⓿ ❷ ❷❺⓿⓿❷ ❷ ⓿⓿❿ ⓿ ⓿⓿❿ ⓿❹❽ ⓿❶❿ ❶ ⓿❿ ❿ ❷ ❶ ❽❶ ❿❿❸❸ ❽❶ ❷ ButylacetateButylacetate A❿ ❺ ❿ A ❽❺ Number ❽❹❻❸ ❹❻❸ ⓿ ❷❶⓿❿❻❿❿❿❷ ❷❶⓿❿❻❿❿❿❷ ❶ ❷ ❿❸ ❿ ❸❹ ❺ ❿❽❶ ❻❿❾❹ ❼ ❿❷❽ ❿❾❹ ❾ ❿ ❸❼❿❺ ❸❼❿❺ ❼❿❸❶ ❷❼❿❸❶ T C Ethylacetate Ethylacetate EthylacetateEthylacetate ⓿ C XNH3CChloroform XNH3 ❾ C❾ NH3C H❿ ❽ ❶❿C ❶❸ H❾❽ ❸ O ❶❿ ❶ ❷ ❸❾ ❻ ❷ ❸❾ ❷ ❽❶ ❼ ❻ ❸❼ ❼O ❸❹❸❹ ❶❿ ❿❼❷¿❿ ❿❸❿ ⓿⓿❿ ❸ ❸ ❸ ⓿⓿❿ ❻❼ ❷ ❿❿❸❸ ❿⓿❿⓿❿ ⓿⓿❿ ❿❿❸❸ ❶ ❸❹❸❹ ❿ ❷ ❸ ❶ EVOC hy a e a e OM mNH3 C M ❸ ❿❺ Butylacetate ❷ ❸m ❷❶❾❻ ❺ ❻❸ m ½❶❾ ¶❶⓿⓿❶⓿⓿⓿❷ Butylacetate ⓿ ❹ ❻ ❶❿ ❺ Response load in Butylacetate A % Butylacetate A Chloroform ❽ 0 ❽❹❻❸ A 10 20 ❺ ❹❻❸ 30 ❷❶⓿❿❻❿❿❿❷ A ❽ ❷ ❺ 40 D X ❻ ❶ 50 ❷❶⓿❿❻❿❿❿❷ A ⓿❶❽ ❽❹❻❸ 60 ❶ 70 80 Response ❽❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❿ ❺90 ❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❿❾❹❿ ❸ D ❻in % Response 100 load ❷❶⓿❿❻❿❿❿❷ ❿❾❹ ❸❼❿❺ ❻in❶ ❶❶⓿⓿❷⓿❿❿❻ load ❿ % ❸❼❿❺ ¿ ⓿0 ❿❾❹ ❼ ❸ ❿ ⓿ ❶ ❺❶❹ 10 ¸ ⓿0 ❼❿❸❶ ❻ ❿❾❹ ⓿❶ ❼ ❸ 20 ❿ ❸ ❹ 10 ❸❼❿❺ ⓿ ¸ 30 ❼❿❸❶ 20 ❿❾❹ ❶ ❶❶ 40 ❸❼❿❺ 30 50 ❶40 ❸❼❿❺ ❹❻ 60⓿⓿ ❼❿❸❶5070 ⓿ 60⓿⓿ 80 ❼❿❸❶ 70 90 80 ❼❿❸❶ 100 90 · 100 m H2 w m ❺ ❿º M C 2C HTrichloroethylene❾ ❾ HTrichloroethylene H2 C ❷ ❾ ❺ ❷ ❽❶ ❿❺ M ❽❶ ❿ ❺ ❿ ❿❿❸❸ ❿❿❸❸ ❷ ❷ ❷ Bu y a e a e A ❿❿ ❶ ❻ ⓿⓿❶❷ ❶⓿❼❶ ⓿ ❶ ❶ ❶ Number 2 ❻ ❶ C❶ m ⓿❶❼❹ m ❶❶ ❸ ❷ ❷ ❿❺ ❸❶ ❿❺ ❷ ❸❾❶ ❻ ❷❷❶❾❻ ❸ ❻ ❷❶❾❻ ❶⓿❼ ❶❶⓿ ❼ ❸❶ ❶❶❾ ❻❷ ❶ ❽ ❶❾ ❶⓿⓿❶⓿⓿⓿❷ ❶ ❶⓿❼❶ ❶⓿⓿❶⓿⓿⓿❷ ❻❼ ❶❿ ❶ ❶❿ ❶⓿❼❶ ❶ A NO2 ❽ ⓿ ❽ ❶ ❷ ❸ A ❺ ❻ ❻ ❼ ❽ ❽❸ ❿ ⓿ ❶ ⓿ ❶ ❸ ❷ ❹ ❸ ❺❹ ❻⓿⓿ ❺ ❼❻ ❽ ❾ ❽❿ ❾ ❿ NA R m m Chloroform Chloroform Chloroform Chloroform ❹❻❸ m C❹❻❸ ❺ m Number ❸ ❿ ❿❾❹ ❺ Number ❸❸❼❿❺ ⓿⓿ ⓿⓿ ❸ ❿❶❿ ⓿⓿ ⓿⓿ 2 ❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❷❶⓿❿❻❿❿❿❷ ❿ ❿❾❹❿ ❷❶⓿❿❻❿❿❿❷❸❼❿❺ ❼❿❸❶ ❼❿❸❶ H NO2 Ch o o o m Trichloroethylene ❼ ❷ Trichloroethylene ¶ ❷ ❸ H ❶ A NOC 2 Trichloroethylene NH3 Trichloroethylene m CO ❸ ❿❺❸m❿❺ NH3 ❶ NO❷ A ❸ ❷m ❷ A ❼ ❷ ❷❶❾❻ ❸ ❸m❿❺❷❶❾❻ ❸ ❿❿ m❿❺ »❸ ❷❸❸ ❻ ❿❿ ❿❺ ❸ ❷ ❷❷❶❾❻ ❸❶ ❶❾❷ H ❷ ❷❶❾❻ ❸ ❶❾ ⓿❶❽ ❶⓿⓿❶⓿⓿⓿❷ ❷❶❾❻ ¶❶⓿⓿❶⓿⓿⓿❷ ❷⓿⓿❷ ❶❾ ❷¼ ❸ ❶❾ ⓿ ❷ ❹ ❶⓿⓿❶⓿⓿⓿❷ ❶❿ ¶❿❾❹ ❶ ❸ ❻ ❶❾ ⓿❷ ⓿⓿❶❷ ⓿❶❽ ❹ ❶ ❸❼❿❺ ⓿❻ ⓿⓿❶❷ ❶❶⓿⓿❷⓿❿❿❻ ❶⓿⓿❶⓿⓿⓿❷ ❶❿ ❼⓿❸⓿ ❶ ⓿ ❶ ❼❿❸❶ ❶❿ ❶ ❶❶ ¶ ❶❶⓿⓿❷⓿❿❿❻ ❶❶ CO ❽❶ ⓿ ❿❿❸❸ ❷O⓿ m CO CO C❸❸ Cm ❸ ❺ ❼ ❸ O❶ ❿ ❺ ⓿ ❿ ⓿ ⓿⓿ ❻ ⓿⓿ ⓿ ❸ ❶⓿ ❻ ❶⓿❼❶ ⓿❶⓿⓿❶⓿⓿⓿❷ ❶⓿❼❶ ❶❿ ⓿⓿⓿ ❶ ❶❿ ❶ ❶ T h o oe hy ene NH3 ❻❸ ·3 NH ⓿❹❻ ❻ ⓿ ❸ A NH 3A H2 3 ❿❿ NH H2 A ❿❿ AH NH A H ❶❸⓿❶❿ ❻ ❿❿ ❸ ⓿ ❻ ❹ ❿❿ ❸❻ ❹❺❷❿⓿ ⓿ ❿❿ ❼NH ❻❸ ❾ ❷❷❺⓿⓿❷ ⓿⓿❶❷ ⓿⓿❶❷ ⓿ ❹ ⓿❹❽ ❻ ⓿❶ ❻❸ ❹ ⓿⓿❶❷⓿ ❻ ⓿ ❶ ❷ ⓿⓿❶❷ ⓿ ⓿ ❷ ⓿⓿❶❷ ⓿❹❻ ⓿❶ ⓿❶ ❽⓿ ❶ ⓿❹❻ ❽⓿ H2S ❺ ❺ D mH2S mH2S ❸ ❿❺❸❸ ❿❺ H2S❷ ❸ ❷ ❷❶❾❻ ❸m ❷❶❾❻ ❸ ❿❺ ❷ ❶❾ ❷❷ ❸ D ❶❾ ❶⓿⓿❶⓿⓿⓿❷ ❷❶❾❻ ❶⓿⓿❶⓿⓿⓿❷ ❺ ❶❿❸ ❶❾ ❺ ❶❿ ❶⓿⓿❶⓿⓿⓿❷ ❺ ❶❿ X NO NO ❶❿ ❷ ❺ ❽❶ ❺C ❽❶ ❼ ❿ ⓿⓿❿ ❸ ❿❿❸❸ ❶ ❸ ❿ ❿❿❸❸ ❶ ❷» ❷ NH » ¸ 2 2 ❿¹ ⓿❹❻ ppm ppm ppm ppm ppm C—ZnOppm C—SnO2ppm H2 ❺ ❿H2 H H2 H ❺ H 2 ❿ H ❿ H ❼NH H CH ❼NH ❶❼ ❼❿H ❼❿ ❶ ❺ ❿ ❼ ❷⓿⓿❷ ❺ ⓿❹❻ ❷ ❷⓿⓿❷ ❼ ❷ ❼ SiO2/Fe3O4 [53] 2/Fe3O4 [53] O OO O SiO2/Fe3O4 [53] ZnO [36] ZnO [36] ZnO [39] ZnO [40] [37] ZnO [38] ZnO [38] ZnO [76] ZnO [76] ppm [65] ZnO-CuO [75] Au [66] Ag [67] [51] ZnO [36] [52] ZnO [39] [50] ZnO [40] ZnO [37] [36] ZnO [38] [39] [72] ZnO [40] [76] Au@ZnO [37] [65] ZnO [38] [76] 2 [36] [65] ZnO2 [39] ZnO2 [40] 2 [37] ZnO3 [38] ZnO [76] [65] Co3O4 [75] [75] Au [75] [66] [67] [66] Au [66] [67] [75] Au [66] Ag [67] ppm [71] [46] SnO2 [45] SnO2 [47] SnO2 [47] SnO2 [73] WO3 [48] ppm 3 [48] 3 [48] 2O3@WO3 [49] TiO2 [51] TiO2 [51] Pd@TiO2 [52] 65 In2O3 [50] In2O3 [50] CuO [74] CuO [74] C O 71 Co3O4 [69] Co3O4 [69] Co3O4 [72] Co3O4 [72] SiO2 [53] SiO2 [53] CeO2 [53] CeO2 [53] Y2O3 [53] Y2O3 [53] La2O3 [53] La2O3 [53] Au/La2O3 [119] TiO2/SnO2 [102] CuO/SnO2 [45] 73 2O4 [70]ZnCo2O4 [70] [71] [46] [45] [71] [47] 2 [46] [73] [48] [45] SnO23 [48][47] SnO23 [48][73] O3@WO3 [48] [49] [71] 3 [48] 3@WO3 [49] TiO2 [46] [51] 3 [48] [52] 2 [45] [50] 3 [47] TiO2 [74] [51] 2 [73] 2 [52] WO3 [69] [48] O334 [48] [50] [72] SiO23 [74] [49] [48] [53] 3O4 [69] CeO32 [53] 3O4 [72] [53] [51] 2 [53] 2O32 [52] [53] 2 [53] 3 3[119] [50] Y2O2 3[102] [53] [74] La2O32 [45][53] 3O4 [69] 2O3 [119] [72] 2/SnO2 [102] 3O24 [53] 2 [45] 2O42 [53] [70] Y2O3 [53] O3 [53] 4 [70] 2O4 [70] [119] 2/SnO2 [102] O Ag [67] Ag2 [45] ZnCo2O4 [70] ❻❸ ❻❸ ⓿ ⓿ ppm C m ❶ ❻ ❶⓿❼❶ ❶ A A ❿❿ ❿❿ A ❿❿ ⓿⓿❶❷ ⓿⓿❶❷ ⓿ ⓿❶❿ ❶ ⓿⓿❶❷ ⓿ ❶ pp NO2C NO CO2 CO ❾ ⓿ ❹❺❷❿⓿ ⓿ ⓿ ⓿ ppm ❸ ❶ ❸ ❽❶ ❶ ❸❸ ❺ NO ❺ ❶❸⓿❶❿ ❸❷ ❶ ❸ ❶ ❿❿❸❸ ❸❷❶ ❸ ❶ ❶❶ H NO2 NO2 ❷º ⓿❶❽ ❼NO ❽❶ H H ❽❶ ❼¿ ❽❶ ❽❶ ❿❿❸❸ ❿❿❸❸❺ ❸❸ ❶❸⓿❶❿ ❿❿❸❸ ❾ ❷ ❿❿❸❸❷❺⓿⓿❷ ❹❺❷❿⓿ ❷ ⓿❹❽ ❾ ❷ ⓿❶❶⓿⓿❷⓿❿❿❻ ❷❺⓿⓿❷❷ ⓿❹❽ ⓿ ❷ ppm ppm H H H ❶❶⓿⓿❷⓿❿❿❻ ❼ ❶❶❿❺ ❷❿ ⓿❶❽ 3 4 Ag [67] La2Ag RT—Au@ZnO ❷ Pd@ZnO Pd@ZnO Au@ZnO Pd@ZnO 2 Au@ZnO 2Pd@ZnO 2O2 CuO CuO 3 2 CuO ZnO ZnO ZnO ZnO ZnO ZnO ZnO ppm ppm [39] ppm [40] ppm ZnO-CuO ZnO-CuO ZnO-CuO H2S ❽ C❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❿❶❿ ❿❾❹ ❸❼❿❺ ❻ ❶❿ ❼❿❸❶❺ ❺ Pt@WO SnO [45] WO Pt@WO WO SnOPt@WO Pd@TiO SnO Pd@TiO O [53]Pt@WO 2O3@WO Pd@TiO CeO CeO Au@WO AH SnO SnO2 [45] SnO SnO SnO SnO Au@WO SnOAu@WO TiO2 [51] SnO SnO SnO In23O TiOAu@WO TiO O SiO 2O CuO/SnO SiO CuO/SnO ZnCo2CuO/SnO 2S C m m ❶ ❶ ❻ ❶⓿❼❶ ❶⓿❼❶ ❶ ❶ 3 RT— ppm RT— ppm RT— ppm ppm CO CO ⓿ ⓿ CO CO ⓿ ❺ CO NO ❺ X ❺ ⓿ ❺ CO ❺X ⓿ ❷ ⓿ ⓿ ⓿ ❼ ❷ ⓿ ❿ ⓿⓿❿ ⓿ ⓿ ❼ ❿ ❿ ⓿⓿❿ ⓿ ❿ 2O /SnO O NO ❿❿❸❸ ❸ ❶¸❸ ❷ ❶ ¶ 3 NO 2 ❽❶❷ ❽❶ ❽❶ ❿❿❸❸ ❽ ❿❿❸❸ ❷ ❷ ❷ ❽ In Co Co Co Co In Co SiO2/Fe La ZnCo O 2OZnCo Y 4 [53] 2/Fe3O4 [53] [74] [36] [69] [39] [40] [36] [37] [39] [38] [40] [53] [76] [37] [65] [38] ZnO2 [76] [65] Au [75] [66] O Ag [67] [71] [46] [45] Pd@ZnO2 [47] [71] 2 [46] 2 [73] 2 [45] [48] 2O3@WO3 [49] 2 [47] 3 [48] 2 [73] 3 [48] 3 [48] 3 [49] 3 [48] [74]2 [51] 3 [48] 2 [52] 2O3 [50] 2 [51] [74] O 3O24 [52] [69] O34 [50] [72] SiO2 [74][53] 3O4 [69] 2 [53] O4 [72] TiO32[53] 2 [53] 2O3 [53] 2 [53] 3 [119] 2O3 [53] 2 [102] 2O3 [53] 2 [45] 2O3 [119] 2 [102] 2 [45] O4 [70] NH ❻❸ ⓿❹❻ ⓿ RT—N A RT—N A RT—N A RT—N A 2 ❷H❶ ❸❷ ⓿⓿ Au/La Au/La Au/La C— C— C— C— C— C— H2S H2S ❺ C H 2S Cm H m C C m H❶ C C m ❶ m ❺❾ ❶ ❻ C ❶❻ ❶⓿❼❶ ❾ ❶⓿❼❶ ❻ ❻❺ ❶❶⓿❼❶ ❺ ❻ ❶ ❶⓿❼❶ ❶⓿❼❶ ❶ ❺ ❶ ❶ ❺ OCe-dop. SnOCe-dop. Ce-dop. Ce-dop. OCe-dop. CO ❺ ❿ C ❶ ❼ ❺CO ❺CO C ❷⓿⓿❷ ❺ ❶ ❷ ❼ ⓿ ⓿ ⓿ ⓿ ❷⓿⓿❷ ❷ ❼ N A— H 3SiO 2O m ❸ ❿❺ NH❷ ❸ ❷❶❾❻ ❶❾ ❶⓿⓿❶⓿⓿⓿❷ ❶❿ O SiO2/Fe3O4 [53] [53] SiO2/Fe3O4 [53] ZnO [37] O Au@ZnO [65] ZnO [36] ZnO [39] ZnO [40] ZnO [37] ZnO [38] ZnO [76] Au@ZnO [65] Z O C O ZnO-CuO [75] Au [66] Ag [67] ZnO-CuO [75] OO [66] [71] 2O3@WO3 [49] TiO2 [36] Pd@TiO2 [39] ZnO [40] [37] [38] 3O4 [76] Au@ZnO [65] 3 [75] [49] [66] [67] SnO2 [46] 2 [45] 2 [73] 3 [48] 3 [48] 3 [48] [49] Pd@TiO2 [52] O 45Au/La2O3 [119] O TiO2/SnO2 [102] C O O CuO/SnO2 [45] 47 Pd@ZnO [71] SnO2 [46] SnO2 [45] SnO2 [47] Ce-dop. SnO2 [73] WO3 [48] Au@WO3 [48] Pt@WO3 [48] [53] [71] [53] [46] [53] 2O3O2[119][47] 2[102] [73] WO23 [48] [48] 4 [48] [70] O2 [52] [50] [69] 4 [72] [53] [53] 2O3 [53] 2O3 [119] 2 [102] 2 [45] 4 [70] A❻❸ ❻❸❽ ⓿❹❻ ⓿❹❻ ⓿ ⓿ ❸❼❿❺ OOYTiO YAO TiO 3 Au@ZnO Pd@ZnO Au@ZnO 2 SiOO2CuO Z OOCuO Z OACOO O A A O O Au Z OZnO Z3OO4ZnO ZnO ZnO ZnO Z O2ZnO ZnO Z2OO23ZnO ZnO ZnO ZnO ZnO-CuO ZnO-CuO A NH ❽❷ ❾ ❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❹❻❸ ❿ ❷❶⓿❿❻❿❿❿❷ ❿❾❹ ❸❼❿❺ ❿ ❼❿❸❶ ❿❾❹ ❼❿❸❶ SnO WO OPt@WO @WOWO OPd@TiO Pt@WO Pd@TiO OCeO @ZOOOCeO O SnO SnO Z OZ OOSnO SnO SnO OAu@WO Z OOAu@WO TiO O 23O OY23O SiO CuO/SnO CuO/SnO ❷H❶ C ❷ ❷ ❻ ❷ RT— RT— RT— RT— RT— RT— RT— RT— RT— RT— H S ❶ C Cm ❸❸ m ❶❸⓿❶❿ H❶ m ❹❺❷❿⓿ ❷❺⓿⓿❷ ❶❻ ❶⓿❼❶ ⓿❹❽ ❶⓿❼❶ ❸❸ ⓿ ❶❶❸⓿❶❿ ❷❻ ❶ ❶⓿❼❶❹❺❷❿⓿ ❺ º❾❺ ❶❷❺⓿⓿❷ ⓿❹❽ ⓿ ❷ 2O 2/SnO O 22/SnO 76Fe2O3@WO3Fe 2O43Fe O3Fe 2Fe NO ❸ A ❿❿ ⓿⓿❶❷ ⓿❿ ❶ OIn S OO 53 OCeOIn Co Co Co Z OCo /Fe @ OLa OLa ZnCo H ❻❸ H ❻❸ ❻❸ ❻❸ ❻❸ ❺ ⓿❹❻ ❿❺ ❸ ⓿❹❻ ⓿ ⓿ ⓿❹❻⓿❹❻ ⓿⓿⓿ ❸⓿❹❻ ⓿ ❼ ⓿❿ ⓿⓿ 3 4 Ag O Pd@ZnO 3 CuO 2 Pd@ZnO O 3 CuO 3 O O O O C OO Au CAu ZnO ZnO ZnO P @WO 48 InZnO ZnO @WO 49CoZnO ZnO O O Z O2O O ZnO-CuO OZ3OO O O Z OO OOC O Z OC O NH NH NH ❷ NH NH Au/La Au/La WO Pt@WO ❶❿ ❷ CeO OSnO ZPt@WO OWO O3@WO ❼ Z @WO Pd@TiO 69@ZZOFe@WO ❿ O OCWO O ⓿⓿❿ ZO @WO O @WO O ❶❿ ❿ O ❷ WO @WO @WO O O AO OC O ⓿⓿❿ O ❿ SnO A @WO Au@WO O SiO SnO O SnO O n O 50CuO/SnO Au@WO Z @WO O23O O OCo3O A @WO O CuO/SnO C 2O O O O @WO O @ O 45O O C O O AYO A O Au 66 C O O Ag 67 Z C O X ❷ ❷X A O A A Ce-dop. Ce-dop. A CO ⓿ ⓿ Z2/SnO TiO2/SnO OOCO ZnCo O A70 O O H ❼ ❸ ❶ ❸ ❶ 3 2 NO NO 2 2 SiO SiO ❸ ❿❺ ❷ ❸ ❷❶❾❻ ❸ ❿❺ ❷ ❸ ❶❾❷❶❾❻ ❶⓿⓿❶⓿⓿⓿❷ ❶❾ ❶❿ ❶⓿⓿❶⓿⓿⓿❷ ❶❿ A @Z @Z @Z @Z @Z Z Z Z Z Z Z CZ ❿❿❿❹❿ ❸ ❺ ❿ ❾❷❻❷❾❶⓿⓿❻ m m OC Co 2/Fe O2In Co ZnCo La 74Fe2ZnCo La CZnCo Y CY OY M h n ❺ ❿❺C ❿ ❺ ❾❿❺ ⓿❹❻ ❿❺ ⓿❹❻ ❿ TiO OTiO H ❾ NH H H H H CC C ❻❸ ❻❸ ❻❸ ⓿ ⓿ ⓿❹❻ ⓿ O O O Z O O O O NH NH Au/La O Au/La C OOCAO Z O CAC 75 OC @ ❺ WO @WO O WO @WO @ O @WO @WO O OO O CCZ OO O CO O O OO A @Z O O A @WO @WO A @WO O O O YO O O O O C OC OO O CZOCZ O H 102 Fe COA ❽❶ ❿❿❸❸ ⓿❷ ❶⓿ ⓿⓿❶❷ Z C53 A Z O OZ O Ce-dop. O O 119 CO ⓿❸ ❶ ❶ ⓿ ⓿⓿❶❷ OZCO O ❾❾❽❹❺❾❺❺❿❿ ❸ ❾ ❽ ❿ ❿ ❿ ❶ Response ⓿ ⓿ ❿load ❾❼ ❽❻ NO NO NO NO NO ❿❿ A ❿❿ ❸ ❶ ❸ ❸ ❶ ❸ ⓿ ❶ ⓿ ❶ Z O C SiO O Eh n Au@WO Fe48 10 20 30 40 50 60 70 80 A90 100 H ❽ H ❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❿ 0❿❾❹ O A @Z @Z 20❸❼❿❺ 050❽4010❼❿❸❶ ❹❻❸ ❷❶⓿❿❻❿❿❿❷ ❿100 ❿❾❹ ❸❼❿❺ ❼❿❸❶ Z Z Z Z C OC OO C C Y %❺ ❿10❺ A❿ 60❺ ❿ O 52 A A TiO O O H in % A CA in % 0 Response Response load load inResponse load0 in %1030 2040 30 60 502070 30 80 7040 90805010090 60 70 100 80 90 ❺ C ❺ @ C OA H H ⓿❺ 72 53 CO CO CO CO H NO CO H❼ ⓿ ⓿ ⓿ ❼⓿ ⓿ ❶ ⓿⓿ ⓿ ⓿ 53 53 SnOC O46 WO 48 P p n ❻ ❿❹❶ ❾ Z CSnO Number ❸ ⓿ ⓿⓿ ❿ ❽ ❾ ❸ ⓿⓿ 51 36 39 40 ZnO O37 ZnOWO38 ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ NO C NO m ❸ ❶ ❸ ❿ ❸ ❶ O Number Number Number ❶ ⓿❷ ❶ ❸❻❷ ❹❸ ⓿ ❺❹ ❶❻❺ ❷ ❼❻ ❸ ❽ ❼❹ ❾ ❽ ❺ ❿ ❾❻ ❼ O C O O O O O O O O O C CuO ❶ ❶⓿❼❶ ❶ OC C Au@ZnO CuO/SnO @WO O @WO @ O C O O O O O O YO O O Pd@ZnO Z ❽❶ ❽❶ ⓿⓿ ❿❿❸❸ ❺ ❷ ⓿❶⓿⓿❶⓿⓿⓿❷ ❿❿❸❸ ❷ A OO A ❸ ❿❺ ❶❿❺ ⓿❺ ❺ ❺ A /SnO ❸❶ m H CO ❸ ❿❺ H CO ❷ ❸H ❷❶❾❻ H H CO ❶❾ ❶⓿⓿❶⓿⓿⓿❷ m ⓿ ⓿ ❷❸ ❷❶❾❻ ❶❾ ❶❿ Eh A @Z @Z Z ZnOZ Z Z ZnOZ Z ❷ Response Response load in % load in % 0 ❺ 100 20 1030 2040 3050 4060 5070❺ 6080 70 9080 10090 100 @Z CuO OZ O O ZnO ZnO OOC/Fe O O O O COOOC OO O OZ O OZ O Z OO Z OO OZ O O Z OOZ OZ OO OC OOCeO O SnO SnO WO @WO WO @WO @ O @WO @WO C O O O CC O C O O O O O O O OO O O @WO COO O O O O O YOO Y O O O O O O O O O OPd@T ❿❿ A ⓿⓿❶❷ ⓿❿❿ ❶ ❺ ⓿⓿❶❷ ⓿ ❶ O A A A A ❺ ❺ A A A A H H H CZ Au/La ❷ O OOO O O u n Ce dop NH ❻❸ ⓿❹❻ ⓿ O p Number Number p⓿nH C ❶ ⓿d❷ ❶ ❸50 ❷ ❹60 ❸ ❺R ❹ ❻ ❺ n❼❼ ❻ ❽ ❻❼ ❾ ❽❿ ❾ ❿ Z @Z @Z @Z @Z Z OZ OZ C Y O ❶ ❶ ❶⓿❼❶ ❻ ❶ ❶⓿❼❶ ❶ ZnO OC OZCo Co Response load in % 0 10 20 30 40 50 60 Response 70 80 H load90 in 100 % 0 10 20❼ 30 Response 40 50R C load60 nin70m % 80d n% 90 0 R 10010 20 30 nm40 % 70 p80 90 d n 100 % CC La O O O O OO O O O OO O O Z OZ O Z OZ O Z OO O FeOZZOO Z OZ O O O O Z OO O Z O Z OO O Z OO Z O Z OO O Z O Z O O O OC OO O Z T C @ Y A O WO WO @WO Z OWO O @WO Z @WO @WO WO O @WO C O WO O @WO O @WO O WO @WO O @WO OO@WO O O O C AO ZO O @WO Z @WO O O O O C C OC O OA O C O @WO OC OO O @WO O O O O O YO @WO O O OOO O OO O O O OCAO YO O O CC O C O O OO O O C OO O ZOO ❷ OOC A O A O OA O O O OA O H n A ZCA H ❺ ❷ ❿ O ZCO O Number ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ Number ⓿ ❶ ❷❽❶ ❸ ❹ Number ❺ ❻Num ❼ ❽ ❾⓿ R❿❶ ⓿Num❷ ❶ ❸ ❷❹ ❸ ❺ ❹ ❻ ⓿ ❺ ❼ ❶❻ ❽ ❷ ❼ Num ❷❾ ❸ ❽ ❿❹ ❾ ❺ ❿ ❻ ⓿ ❼ ❶ ❽ ❷ ❾ ❸ ❿❹ ❺ ❻ ❼ ❽ ❾ ❿ A @Z Z @Z @Z @Z @Z @Z @Z @Z @Z Z OZ Z Z Z OOZ Z Z Z Z OOZ Z OZ Z CZ SOCZ Y Y OA O CA A C C C TOC ❿❿❸❸ p n R p dn n % d n % ❷ ❽❶ ❿❿❸❸ ❷ O O O Z O Z O O O O Z OZC OO C C C Z OZC OC OC C C C @ @ @ @ @ C C OA ❻ ⓿ ❸❷❽⓿ ❶❷ ❶ ❷ O O C C @WO WO O @WO @WO O O @WO AO O O O OO O O WO A ACOOO @WO @WO O O C AO AO @Z O O O A O A @WO A @WO OO CA O CO O CC O O O O C C C O O A @WOYOO O O @ OO O O O CZOO C OCZOO B n n NO ❸ ❶ OA A Z OZCA O O ❻❸ ❻❸ ⓿❹❻ ⓿ ⓿❹❻ ⓿ O ZO O ❺ NH NH Num Num ⓿ ⓿ ❶ ⓿❷❺ ❶ ❸❷ ❹⓿ ❸ ❺❹ ❻❺ ❼❻ ❽❼❾❽ ❿ ❾ ❿ A @Z @Z A @Z A @Z @Z Z Z Z Z Z Z O C Z Y Y A A O O O O O O A CA A C C ❶❶ CO C C Z C Ch n n @ @ C CZOC OA A A A A H H ❺ ❿ ❺ ❿ O C m ❶ ❻ C❶⓿❼❶ m ❶ ❶ ❻ ❶⓿❼❶ ❶ Re pon e oad n% R p0n d n% ❾10 20 30 H 40 ❷ 50 60 70 ❺ 80 90 100 O Eh n n O C C ❸ ❶ ❸ ❶ NO NO C A ❷ T u n ❸ ❿Numbe ❶❿ ⓿❿❶ ❿❿⓿ Num⓿ ❹ ❹ ❹ ❸ ❽⓿ ¶ ❶ ❷ ❸ ❹ ·❺ ❻ ❼ ❽ ❾ NH¸ ❿ ¹ ❻❸ º CO R p»n R pCO NH dn n % d n ⓿❹❻ %¼ ⓿ ❻❸ ½⓿ ¾ ⓿⓿ ¿ ⓿❹❻ ⓿ ⓿ A @Z O @Z O C O Z O Z O Z O Z O Z O Z O Z OC O Num nn % d n %⓿ ❶ Num ⓿❷ ❶ ❸❷ ❹❸ ❺❹ ❻❺ ❼❻ ❽ ❺ ❼❾ ❿❽ ❺❿ ❾ ❿ ❺ Z O WO Z O @WO Z OO @WO @ O O C O O C O WO C O O O O O @WO O O YO O OO O O OZ C O X n ⓿ ❿ R p n R pHdn n % ❺ d Rn% p nR pd nR n %pHHd A A H ❿ O O A n ❸❷❾❿❷❷❿⓿❿❺ ❷❿❿❿❺❸❾❶❶ ❷❿❿❿❿ NO R p Num nn % d n %⓿ Num nR pd Num❶⓿ ❷ R ❶ Num ❸ NO p n❷❹❸ Num ⓿ d❺n❹ ❶ %❻⓿❺ ❸❷ ❼❶❻ ❸ ⓿ ❶❽❷ ❼❹ ❾❶❸ ❽❺❷ ❿❹ ❾❻❸❺❼ ❿❹❻❽❺❼❾ ❻❽❼ ❿❾❽ ❿❾ ❿ ❸ ❶ C OOO O O A @Z O @Z O @Z OO C O O C OO CZ O Z O Z OO O Z O Z O O Z OO O Z O C OO Z OC OZ OC O M P ❽ Z A ⓿⓿ ⓿ ❹ ❺ ❻ ❼ ❽ ❾ ⓿❿ ⓿ @WO Z C O O @WO O O WOO @WO O @WO A O @WO O O C AO C O O O Z O A @WO C O C O O A @WO A @WO YO O OO @ O C O@ O C O O YO O C O O C O O Z C O Z C O CO Num Num ⓿ ❶ ❷❶ CO ❸ ❷❹❸ Num ❺❹ ❻❺ ❼❻⓿❽❼❾❶❽❷❿❾❸ ❿ A A @ C A A O O O A @Z AO A @Z Z Z C m d h d ❸ ❽ ⓿ H H ❺ ❺ C O Z C O O O O O Eh ❹❷ ❺ O A A @Z O @Z O C O @Z O Z O Z O Z O Z O Z O Z O O ZOCO O O Z OO O ZAO A d ❺ O WO @WO O @WO @ O C O C O C O O WO @WO C O C O O O O A @WO O O YO O C O O O O O O YO O C R p n d n% A A O O Z Z Z OC A n ❺ Num ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ O O T h mn ❺ R p n d n% R p n d n% A A NH ❿ ⓿ ❿ C C Num ⓿ ❶Num❷ ❸ ❹ ❺ ❻ ⓿ ❼❶❽❷❾❸ ❿ ❹ ❺ ❻ ❼ ❽ ❾ ❿ NanomaterialsNanomaterials 2021, 11, 552 2021, 11, 552 18 of 34 18 of 34 NanomaterialsNanomaterials 2021, 11, 552 2021, 11, 552 18 of 34 18 of 34 Table 7. Cross-responses Table 7. Cross-responses to different to VOCs different recorded VOCs using recorded POMsusing and CbMs. POMs The and color CbMs.codes The color and numbers codes and innumbers each column in each column Nanomaterials 2021, 11, 552 of the table of represent the tablethe Table 7. Cross-responses represent load of response Table 7. Cross-responses the load of to different to VOCs forresponse the referenced different recorded VOCs for the material using referenced recorded POMsusing towards material and CbMs. thetowards POMs The same gas and color the concentration CbMs.codes same gas concentration The color and numbers of various tested codes and innumbers of various tested each column in each column 16 of 33 analytes. N/A: analytes. data not N/A: available, data notRT: available, room temperature. RT: room temperature. of the table of represent the tablethe represent load of response the load offorresponse the referenced for the material referenced towards material thetowards same gas the concentration same gas concentration of various tested of various tested analytes. N/A: analytes. data not N/A: available, data notRT: available, room temperature. RT: room temperature. Nanomaterials 2021, 11, 552 Nanomaterials18 2021, of 34 11, 552 18 of 34 ppmppm ppmppm ppb ppm ppmppm ppmppm ppm ppm ppmppm ppmppm ppmppm ppmppm ppmppm ppmppm ppb ppb ppb ppmppm ppm ppmppm ppm ppm ppm ppmppm ppmppm ppm ppm ppm ppmppm ppmppm ppm ppm ppm ppm ppm ppm ppm ppm ppm °C—500 °C—500 °C—320 °C—320 300 °C—320 °C—320 300 °C—150 450 °C—200 °C—200 °C—150 °C—200 180 °C—200 °C—100 °C—100 °C—150 °C—150 ppm ppm ppm ppmppm ppm ppm ppmppm ppmppm ppmppm ppmppm ppmppm N/A—1000 N/A—1000 ppm Table 7. Cross-responses to different VOCs recorded and CbMs. Theusing color codesPOMs and in eachCbMs. The color codes VOCs and recordednumbers using POMs andin each column of the table columnrepresent the load of response for the referenced ppm ppm RT—5000 RT—5000 RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A Table 7. Cross-responses to different VOCs recorded using POMs and numbers Table column 7. Cross-responses to different CbMs. The color codes and numbers in each 250 ppm 250 ppm ppm ppm ppm ppm ppm ppm RT—200 RT—200 RT—250 RT—250 °C—500 °C—500 250 °C—320 250 °C—320 300 °C—320 °C—320 300 °C—150 450 °C—200 °C—200 °C—150 °C—200 180 °C—200 °C—100 °C—100 300 °C—150 300 °C—150 ppm ppm 300ppm ppm 300ppm RT—10 RT—10 RT—10 RT—10 RT—20 RT—20 RT—20 RT—20 RT—10 RT—10 of the table represent the loadNanomaterials of response2021, 11, 552 Nanomaterials 18 of 34 Nanomaterials 2021, 11, 552 2021, 11, 552 18 of 34 18 of 34 N/A—1000 N/A—1000 for the referenced material towards the same gas concentration of various of tested the table represent the load of response for the referenced material towards the same gas concentration of various tested RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5000 RT—5000 RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A material towards analytes. N/A:the same gas concentration of various tested analytes.analytes. N/A: N/A:data data notnot available, RT: room 2021,temperature. ppm ppm RT—200 RT—200 RT—250 RT—250 data not available, RT: room temperature. available, RT: room temperature. 250 250 300 450 300 450 450 180 RT—10 RT—10 RT—10 RT—10 RT—20 RT—20 RT—20 RT—20 RT—10 RT—10 Nanomaterials 11, 552 18 of 34 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 ppmRT—5 RT—5 RT—5 RT—5 ❿ ❿❿ ❿❹❿ ❿ ❿❹❿ ❸ ❺❸ ❿ ❺POMs ❾❿ ❷and❻ ❾❷❷ ❾and❻❶ ❷ ⓿codes ❾ ⓿❶ ❻ and⓿ ⓿❻ 250 250 300 ppm450 300 450 450 180 Nanomaterials 2021, 11, 552 18 of 34 Table 7. Cross-responses to different VOCs recorded using POMs and CbMs. The color codes and numbers Methanol in each column Methanol Table 7. Cross-responses Table 7. Cross-responses to different to VOCs different recorded VOCs using recorded using CbMs. POMs The color CbMs. The colornumbers codes and innumbers each column in each column ppm °C—200ppm C—150 ppm ppm °C—200ppm EthanolofMethanol Ethanol❾ ❾ ❽ ❾❹ ❾ ❺❿ ❽ ❾ ❹❺load ❺❺❾ ❿ ❺ ❿ ❺ ❿ ❸ ❿ ❾❿ ❸❽❻ ❿ ❾❿❷ ❽ ❿ the❿ ❶ ❿ ⓿gas❿ ⓿ ❶❿ ⓿❾gas ⓿❼ ❿❽of ❾ ❻❼ ❽ ❻ ◦ C—500 ppb of the table represent the load of response for the referenced material towards the same gas concentration of various Methanol tested the table of represent the ❿ table the represent load of ❹ response the of forresponse the❸referenced for❺the material referenced towards material towards same the concentration same concentration various tested of various tested 250 °C—320 ppm 250 °C—320 ppm 300 °C—150 ppm 450 °C—200 ppm ppm 180 °C—100 ppm 300 °C—150 ppm RT—5 ppm250 °C—320 ppm 250 °C—320 ppm 300 °C—150 ppm 450 °C—200 ppm ppm ppm 300 °C—150 ppm ppm ❿ ❿ ❿❹ ❿ ❿ ❿❸ ❺ ❿ ❾ ❷ ❾❷ ❾and❻❶ ❷ ⓿ ❾ ⓿ ❶❻ ⓿ ⓿ ❻ 250 °C—320 ppm 300 °C—500 ppb 300 °C—500 ppb N/A—1000 ppm ppm ppm Table 7. Cross-responses to different VOCs recorded using POMs CbMs. The color codes and numbers in each column RT—250 ppm RT—5000 ppm RT—5000 ppm RT—200 ppm analytes. N/A: data not available, RT: room temperature. analytes. N/A: analytes. data not N/A: available, data notRT: available, ❻❾room temperature. RT: ❿❺ ❻ room ❹❿ ❶ temperature. ❿ ❹ ❶ ❾❿ ❾ ppm ppm ppm ppm Propanol Propanol C—320ppm RT—250 ppm ppm RT—250 ppm ❾ ❾ ❽ ❾❹ ❾ ❺❽ ❾❹ ❺load ❺❺ ❿ ❺ ❿ ❸for❿ ❾❸ ❽ ❿material ❾❿❽ ❿ ❶❿ ⓿❿ ⓿the❶❿ ⓿❾gas ⓿❼ ❿❽❾ ❻❼ ❽ ❻ ppm ppm RT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm Ethanol Ethanol Nanomaterials 2021, of11, 552 the table represent the of response the referenced towards same concentration 18 of 34tested of various ppm ppm ppm RT—N/A RT—N/A RT—N/A RT—10 ppm RT—10 ppm ppm 450 °C—200 ppm RT—20 ppm RT—10 ppm RT—10 ppm ppm ppm RT—20 ppm RT—20 ppm RT—10 ppm RT—N/A 180 °C—100 Table 7. Cross-responses to different VOCs recorded using POMs and CbMs. The color codes and numbers in each column RT—5 ppm RT—5 ppm ppm ppm RT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm ppm RT—5 ppm RT—5 ppm RT—5 ppm Ether Propanol ❸ ❶ ❸ ❶ Ether analytes. N/A: data not available, 300 ◦N/A—1000 RT:❻ room ❶temperature. C—320 C—200 C—200 C—100 ◦ C—150 Propanol ❻ ❿ ❹ ❿❹❶ ❾ ❾ N/A—1000 RT—N/A RT—N/A 250 °C—320 ppm RT—N/A RT—N/A RT—5 ppmRT—N/A RT—5 ppmRT—N/A RT—N/A RT—N/A of the table represent the load of response for the referenced material towards the same gas concentration of various tested RT—5000 250 ◦RT—200 ◦RT—200 Furan Furan ❷ ❷ ppm RT—20 300 °C—150RT—10 ❸❶ ❸❶ RT—10 RT—10 RT—20 RT—20 RT—10 analytes. N/A: data not available, RT: room temperature. Ether Ether 250 °C—320 ppm °C—320 ppm ppm ppm ppm ppm ppmppm ppmppm ppmppm ppm °C—150 ppm ppm ppmppm ppmppm ppmppm ppm ppm ppmppm RT—5 RT—5 RT—5 ◦RT—5 RT—5 ppb ppb ppb ppb ppm ppm Hexane Hexane Table 7. Cross-responses to ❷ different VOCs❷ recorded using POMs and CbMs. The color codes and numbers in each column RT—5000 ppm RT—5000 ppm ppmppm Furan Furan ❷ ❷ 250 ◦450 450 300ppm ppm ppm ppm ppm ppmppm ppm ppm ppbppm ppm ppm RT—20 ppm ppm ppm RT—10 ppm RT—10 ppm RT—10 ppm RT—10 ppm ppm ppm ppm ppm ppm ppm of the table represent ❻ the load❻ of response for⓿ the referenced material towards the❽same ❷gas concentration of various ❷ tested ❷ Benzene Hexane Benzene ❷⓿ ❸❷❽ ❸⓿❷ ❶ ⓿ ❶❶❷ ❶ 180ppm 300 °C—500 °C—500 300 °C—500 °C—150 °C—200 450 °C—200 180 °C—100 °C—320 °C—320 300 °C—320 °C—320 450 °C—200 °C—200 °C—150 °C—200 450 °C—100 °C—200 °C—100 °C—150 °C—150 ❷ RT—5 ppm RT—5 ppm ppm ppm ppm ppmppm ppm ppm ppmppm ppmppm ppmppm ppmppm Hexane RT—N/A ◦ N/A—1000 N/A—1000 N/A—1000 Methanol ❿❿❿❹❿ ❸ ❺ ❿ ❾❷❻❷❾❶⓿⓿❻ Methanol ❿❿❿❹❿ ❸ N/A: analytes. ❺ data❿ ❾ ❷ RT: not available, ❻ room ❷❾ ❶⓿⓿❻ temperature. ppm RT—5000 RT—N/A RT—200RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A RT—N/A ppmRT—N/A RT—N/A RT—N/A RT—N/A ❶ ❶ ❸❶ ❷❶ 450 450 300 ppm ppm Chlorobenzene Chlorobenzene ppm 250 ppm ppm ppm ppm RT—200 RT—250 RT—200 RT—200 RT—250 RT—250 Benzene Benzene ❻ ❻ ⓿ ⓿ ❽❸ ⓿❷ ❽ ❶⓿ ❷ ❶ ❶❷ ❶ ❷ ❷ ppm RT—N/A 300 °C—500 ppb°C—320 °C—320 RT—20 ppm300 °C—150 RT—20 ppm450 °C—200 °C—200 RT—10 ppm180 °C—100 RT—5 ppm 300 °C—150 ppm RT—10 RT—10 RT—20 RT—250RT—10 RT—10 RT—10 RT—20 RT—20 RT—20 RT—20 RT—10 RT—10 N/A—1000 Ethanol ❾ ❾ ❽ ❹ ❺ ❾ ❺ ❺ ❿ ❿ Nanomaterials ❸ ❾2021, ❽❿ ❿❿❶⓿⓿❿❾❼❽ ❻ Ethanol ❾❾❽❹❺❾❺❺❿❿ ❸ ❾18❽ ❿❿❿❶⓿⓿❿❾❼❽❻ ppm 11, 552 of 34 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 RT—5 Ethylbenzene Ethylbenzene ❾ ❾ ppm 450 °C—200RT—5000 RT—N/A N/A—1000 RT—N/A RT—N/A 300 °C—150RT—N/A Chlorobenzene Chlorobenzene ❶❶ ❶❶ ppm RT—10 ppm RT—20 ppm RT—20 ppm ppm 450 °C—200RT—200 RT—250 300 °C—500 300 °C—150 450 °C—200 180 °C—100 300 °C—150 Propanol ❻ ❿❹❶ ❾ Propanol ❻ ❿ ❹❸ ❶ ❾ 250 300 450 250 250 250 300 450 300 450 180 180 300 300 RT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm 250 °C—320RT—10 250 °C—320RT—10 RT—5 ppm RT—20 RT—5 ppm RT—20 RT—5 ppm RT—10 N/A—1000 Methanol ¿ ¿ ¿ ¹ ¿ ¸ º Toluene ¿ Toluene ¾ ❸❿ ·❶ ❿ ❿ »❶ ⓿ ❿ ❿ ❶ · ⓿ ❿ ❶ ¾ ¶ ❿ ❿ ⓿ ❿ ⓿ ❿ ❹ ⓿ ❹ ⓿ ❹ ❹ ❸ » ❹ ❽ ❹ ❸ ❽ 300 °C—150RT—5 RT—5 ppm RT—5 RT—5 ppm RT—5 RT—5 RT—5 RT—5 Ethylbenzene Ethylbenzene ❾ ❾ ppm ppm ppm ppm ppm ppm ppm RT—5000 RT—N/A RT—N/A RT—N/A RT—N/A Ether ❸❶ Ether ❸❶ ⓿❷ ⓿ ❿❻ ❿ ❿❿❿❹❿ º ❸ ¾❺to different ❿ ❾❷❻❷❾❶⓿⓿❻ ❿❿❿ ❿ ❹ ❶❿ ❿ ❿❿ ❹⓿❿ ❸ ¿❺⓿❸ ❿❿ ¿❺ ❾❿ ❷❻ ¶ ❿❾ ❷❿❷❾⓿❻ ❶ ⓿ ❹❾ ⓿ ❹❶ ❻ ❹⓿ ❸⓿ 300 °C—500250 250 180 °C—100450 Xylene Methanol Xylene Methanol ¾ Methanol RT—5000 ppm RT—10 RT—10 EthanolFuran ¾ ½ ¹❷ º VOCs º ¿ POMs and ¿ CbMs. The color Toluene ¸ and numbers in❸ Toluene ¾ ❸❿½ ❿¿ ❶ ❿ ❿❶ ❶ ❿ ⓿ ❿ ⓿ ⓿ ❹ ¿ ❹ ❽ ❹ ❸¾ ❽ ¼ ½ » RT—200 ppm RT—250 ppm Table 7. Cross-responses recorded using Furan codes each❷column Ethanol ❾11, Nanomaterials 2021, ❾ ❽❹ 552 ❺ ❾❺ ❺ ❿the ❿load of ❸response❾ for ❽the❿referenced ❿ ❿ ❶material ⓿ ⓿towards ❿❾❼ ❽ ❻ Acetone Xylene Acetone Ethanol ❸34❷ 18 of Xylene ❾❾ ❸ ❿❾ Ethanol Methanol ❷ ❷❽ ❾❷❿ ❿ ❿❿ ❾ ❹ ❾❷ ⓿❿ ❺ ❽❷ ❿❹ ❾ ❹❿ ❺❿ ❺ ❺⓿❾ ❺ ❿❷❺ ❺ ❿❸ ❿ ❺❿❿ ❷ ❿❺ ❸ ❿ ❺❾ ❿ ❿ ❸❿❿ ❾❿ ❸ ❽ ❺❶❾ ❾❸ ❿❶❷❾❻ ❽ ❿ ❿❷❷ ❶ ⓿ ❶ ❿❾ ❿ ⓿ ❿❶ ❿ ⓿ ❶❷ ❿⓿ ❿ ⓿ ⓿❿ ❿⓿ ❾ ❿ ⓿ ❼ ❿ ❿❾ ❿ ❽ ❻ ❿❼ ❻ ❿❽ ❻ Propanol ❷ of the table represent » Hexane ¿ ¹ the same ❷¶ gas concentration of various tested ¾ RT—N/A RT—N/A RT—N/A Hexane ❽❾ ❽ Propanol analytes. N/A:❻ data not❿ ❹ ❶ RT: room temperature. available, ❾ 4M2P Propanol Acetone 4M2P Acetone ❸ ❷Propanol ❾ ❸ ❿ Ethanol ❷ ❷ ❾❷ ❿ ❿ ❾ ❾❷ ⓿ ❽❷ ❿ ❹❿ ❺ ❺❻ ⓿ ❿❷ ❺❿ ❺ ❿ ❻ ❹ ❺❿ ❿❶ ❷ ❿ ❿ ❿ ❺ ❿ ❹❿ ❸ ❶ ❿ ❾ ❸ ❺❶❾❸❶ ❾ ❾ ❽ ❿❶ ❷ ❶ ❿ ❿ ❾ ❿ ❿ ❶❷ ❿ ⓿❿ ❿ ❿ ⓿ ❿ ❿ ❿ ❾ ❼ ❽ ❻ EtherBenzene Methanol ❻❿ ❿Ether ❿❹ ⓿❿ ❸ ¸❺ ❸ ❿ ❸❶ ❷❽⓿ ¶ ❾❷❻ ❶❷ ❷❾ ❶ ❶⓿ ⓿ ❻ Benzene ❷ ❻ Formaldehyde ⓿ ❸❷ ❸ ❽ ❶ ⓿ ❸❶ ❶ ❷ ❸ ❶ ❸❹ ❶ ❽ ❷ ❽⓿ ⓿ Furan ❾❾❽❹ ❺❾❺❺❿ 4M2PFormaldehyde Ether Propanol 4M2P · Furan Ether ❽ ❽ ❻ ❿ ❾ Chlorobenzene Ethanol ❶❿ ❶ ❸ ❾❽❿❿❿❶ ⓿⓿ ❿ ❾ The❼ color Chlorobenzene ❽❻ ❶❶ Furan Table 7. Cross-responses to different VOCs recorded using ❷POMs and CbMs. codes and numbers in each column Ethylacetate Formaldehyde Ethylacetate Furan Formaldehyde Ether ❹❷ ❸ ❹ ❺❶ ❷ ❺ ❸ ❷ ❸ ❽ ❷ ❽⓿ ⓿ Hexane Ethylbenzene Propanol ❻ of the table represent ❿load the ❹ of❶response for · ❾ materialEthylbenzene the referenced towards the❾ same gas concentration of various Methanol tested ❾ RT—N/A 250 °C—320 ppm 250 °C—320 ppm ppm ppm ppm 180 °C—100 ppm 300 °C—150 ppm ❿ ❿ ❿ ❹ ❿ ❸ ❺ ❿ ❾ ❷ ❻ ❷ ❾ ❶ ⓿ ⓿❻ °C—500 ppb ❷ ❹ ❷❷ ❹ ❺❷ ❷ ❺ ❺ ppm Hexane Acetic acid Ethylacetate Acetic Hexane acid Hexane Ethylacetate Furan ❺ ❷ RT—5000 ppm Toluene Ether ❿❸ analytes. ❶ N/A: data❶ not available, RT: room temperature. Benzene ❸ ❶ ❿ » ⓿❿ ❿❿⓿⓿❹❹❹ ❸❽ Toluene ❸ ❿❶❿ ⓿❿❶ ¸ · ½ ❿ ❿ ⓿ ⓿ ❹ ❹ ❹ ❸ ¶❽ · ❾¶ · ppm ppm ❾ ❾ ❽ ❹ RT—250 ppm Benzene ❻ ⓿ ❸❷❽⓿ ❶❷ ❶ Ethanol ❷Acetonitrile Acetonitrile Benzene Benzene ❻ ❺ ❾ ❻ ⓿ ❺ ❺ ❷⓿❺ ❿ ❿ ❺ ❸ ❾ ❽ ❿ ❸ ❷ ❽❸ ❿ ❿ ⓿❷ ❽ ❶ ❶⓿⓿ ❷ ⓿❶ ❿ ❶❷ ❼❶ ❽❻❷ ❷ ppb ppm ppm ppm RT—5 ppmRT—20 ppm RT—10 ppm Acetic acidAcetic acid Hexane ❺ ❺ °C—150 °C—200 °C—200 RT—5 ppmRT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm RT—5 ppm Furan ❷ N/A—1000 Chlorobenzene Xylene ❶❶ ⓿ Xylene ❿ ¶Chlorobenzene Propanol ¶Chlorobenzene ❺ ❻ ❿ ❺⓿ ❹❶ ❶❶ ❶❿ ❶❸❷ ❾ Chlorobenzene Triethylamine Triethylamine N/A—1000RT—N/A RT—N/A RT—N/A RT—N/A Acetonitrile Acetonitrile Benzene ❻ ❺ ⓿❺ ❽ ⓿ ❶ ❷ ❶ ❷ RT—200 Acetone Hexane ❸ ❷ ❾ ❿ ❷ ❷ ❿ ⓿❷ ❿❺ ❷❿❿❿❺❸❾❶❶ ❷❿❿❿ ❿ Acetone ❸❷❾❿❷❷❿⓿ ❿❺ ❷❿❿❿ ❺ ❸ ❾❿ ❶❶ ❿ ❷❿❿ ❿❿ RT—10 RT—10 RT—20 Ethylbenzene Ethylbenzene NH 3❾ Ether NH Ethylbenzene3 Ethylbenzene ❸ ❶ ⓿ ⓿ ❿ ❿ ❾ ❾ ¾ 250 °C—320 ppm 250 °C—320 ppm ppm ppm ppm ppm Triethylamine TriethylamineChlorobenzene ❺ ❺ ❶❶ ppm 4M2P Benzene ❻ ⓿ ❽ ❸❷❽⓿ ❶❷ ❶4M2P ❷ ❽ RT—5000 ppm Toluene Chlorobenzene ¸ ❸ ❿ ❶ ❿ ¿ ⓿ ❿ ❶ ¶ ¿ ¿ NH¶ Furan ❷ ¿ ¿ ❹ ¹ ¹ ¹ ¸ ½ 300 ❿ ❿ ⓿ 300 ⓿ 450 ❹ 450 Toluene ❹ ❹ ❸ H ❽ H Toluene Toluene ❸ ❸❿ ❶ ❿ ❿ ❶⓿ ❿ ❿ ❶ ⓿ ❿ ❶ ❿ ❿ ⓿ ❿⓿ ❿❹ ⓿ ❹ ⓿ ❹ ❹ ❸ ❹ ❽ ❹ ❸ ❽ RT—200 ppm ppm 2 2 NH3 Ethylbenzene ❿ ❿ ⓿ ⓿ ❿ ❿ ❾ ppm ppm RT—20 ppm RT—20 ppm RT—10 ppm 3 ❸ ❶ ❶ ❽ ⓿ ❸ ❽ ⓿ 300 °C—500 °C—150 °C—200 450 °C—200 180 °C—100 °C—150 Formaldehyde Formaldehyde ❷ RT—5 ppm RT—5 ppm ppm ppm Xylene ❿❾⓿❾ ❷ ❻ ❷ CO ❿❶HexaneCO Xylene Xylene ❺ ⓿ ❹❿ ⓿ ⓿ ❺ ❿❹ ❸ ❿ Xylene Methanol ❿❿❿❹❿ ❸ ❺ H2❾ ⓿H2⓿ ❻ Toluene ❸ ❿❶❿ ⓿❿❶ ❿ ❹❹ ❹ ⓿ ❽ ¿ RT—N/A RT—N/A RT—N/A Ethylacetate Ethylbenzene ❹❷ ❺ Ethylacetate ❹ Benzene ❷ ❺ RT—250 Acetone ❸❷❾❿❷ ❷ ❿ ⓿ ❿ ❺ ❷ ❿ ❿ ❿ ❺ ❸ ❾ ❶ ❶ ❷ ❿ ❿ ❿ ❿ Water Water Acetone Acetone❸ ❷ ❻ ❾ ❸ ❿ ❷❷ ❾❷ ❿⓿ ❺ ❿ ❷⓿ ⓿ ❷ ❿❷ ❿❺ ⓿ ⓿ ❿❷ ❺ ❿ ❸ ❿ ❷ ❷ ❿ ❽ ❿ ❺ ❿⓿ ❸ ❿ ❾ ❺❶ ❶ ❷ ❸ ❶ ❾ ❶❶ ❷ ❶❿ ❿ ❷ ❿ ❿ ❿ ❷❿ ❿ ❿ RT—10 RT—10 ❾ ❾ ❽ ❹❿ Ethanol ¿ ❺❿ ❾⓿ ❺⓿ ❺❹ ❿❹ ¿❿❹ ❸ ❸❽ ❾ ❽ ❿ ❿ CO❿ ❶ ⓿ CO⓿ ❿Xylene ❾❼❽❻ ❺ ·⓿ ❺ ❿ RT—5 RT—5 Acetone Acetic acid Toluene ¸ 4M2P ❸· ❿¾ ❶❺❿ ¿⓿ ❿ ❶ · · Acetic acid º ·Chlorobenzene¿❺ 4M2P ¿ ¿ º ¸ ❽ ❶¾❶❽ ¶ ¶ ¿ ¿ ¿ ¿ [57] [57] 3O4 [116] [116] [123] [109] [123] [123][124] [57][109] [124][111] [124] [55] [55][111] [105] [55] [55][104] [58][105] [104] O4 [122] Au [122] [58] [58] 2 [107] [107][107] [54] [54] [54] [108][120] [120][120] [54] [54] [106][121] [117][121] [117] [110][117] [55][104] 2 [104] [54][106] [54][106] [106] [123][106] [110] [57][111] [55][110] [112] [104][107] [58][108] [112] [108][107] [108] [108] [108][108] [55] [105][111] [55] Propanol ❽ ❻ ❿ ❹ ❶❿ Water 4M2P ❾ Water Acetone ❸❷❾❿ ❺❷ ⓿❷ ❷❿ ❺⓿ ⓿❿ ❷❺ ❷❿❿❿❺❸❾❶❶ ❷❿❿❿❿ 300 450 300 4M2P Acetonitrile Xylene ❺ ⓿ Acetonitrile ½ Ethylbenzene ❺ ❾ Formaldehyde ❸ ❽ ⓿ Formaldehyde Formaldehyde ❸ ❸ ❽ ❽ ⓿ ⓿ 3O4 [116] [116] [109] [124] [109] [111] [111] [104] [105] 2 [107] O4 [122] Au [122] [120] [54] [121] [121] [117] [104] 2 [104] [106] [106] [106] [111] [110] [111] [112] [107] [108] [112] [107] PEDOT-PSS/UL-GO PEDOT-PSS/UL-GO Pt@MWCNTs Pd@MWCNTs Pt@MWCNTs Pd@MWCNTs MWCNTs/Au MWCNTs/Au/MHDA MWCNTs/Au MWCNTs/Au/MHDA PPy/rGO PPy/rGO ❺❿Ether ❸ ❶❶❿ ❷❾ 4M2P ❽ Ti-PTFE Ti-PTFE NiO/MWCNTs FeO/MWCNTs NiO/MWCNTs FeO/MWCNTs 2/GO 2/GO GO/ZnO GO/ZnO Ag/PPy Ag/PPy SWCNTs/CdA SWCNTs/CdA 3Au MWCNTs MWCNTs wt.%) Ag-BC-bsh Au-DT Ag-BC-bsh Au-DT wt.%) wt.%) MOF-ZnO wt.%) MOF-ZnO PPy PPy PPy PPy PPy PPy SWCNTs-calixarene SWCNTs-calixarene GO/ZnO/GO GO/ZnO/GO (1:10) (1:10) TriethylamineAcetone ❸ ❷ ❾ ❿ Methanol ❷❷❿ ❺⓿ ❿❿ ❿❷❹ ❿❿ ❿ ❿ ❺❸ ❸❾ ❺ ❶ ❿❷ ❿❻ ❿❷ ❿❾ ❶ ⓿ ⓿ ❻ ¸❺ rGO/SnO rGO/SnO Formaldehyde Triethylamine ½❺ Ethylacetate Furan ❹❷ ❺ ❷ Toluene Ethylacetate Ethylacetate ❸ ❿❶ ❹❿ ❷ ❹⓿ ❺❷ ❿❶ ❸ ❿❿⓿⓿❹❹❹❸❽ ❽ ⓿ ❻ Formaldehyde PEDOT-PSS/UL-GO PEDOT-PSS/UL-GO Pt@MWCNTs Pd@MWCNTs Pt@MWCNTs Pd@MWCNTs MWCNTs/Au [124] MWCNTs/Au/MHDA MWCNTs/Au [55] MWCNTs/Au/MHDA PPy/rGO PPy/rGO PEDOT-PIL@Fe PEDOT-PIL@Fe 4M2P ❾ ❾ ❽ ❽❹❺❾❺❺❿ ❿ ❸ ❾ ❽3 ❿❿❿❶⓿⓿❿❾❼❽ DT-capped DT-capped NH3 Ethanol❿ ⓿ ❿ NH ❿ ⓿ ❿ Ti-PTFE Ti-PTFE NiO/MWCNTs FeO/MWCNTs NiO/MWCNTs FeO/MWCNTs 2/GO 2/GO GO/ZnO GO/ZnO Ag/PPy Ag/PPy SWCNTs/CdA SWCNTs/CdA 3Au MWCNTs MWCNTs wt.%) Ag-BC-bsh Au-DT Ag-BC-bsh Au-DT wt.%) wt.%) MOF-ZnO wt.%) MOF-ZnO PPy PPy SWCNTs-calixarene SWCNTs-calixarene GO/ZnO/GO GO/ZnO/GO (1:10) (1:10) Ethylacetate ¹ · º Xylene ⓿ ❿ rGO/SnO rGO/SnO Ag/SnO Ag/SnO Acetic acid Hexane ❺ ❷ Acetic acid Acetic acid Ethylacetate ❹❺❷ ❺ ❺ CdTe/PPy CdTe/PPy ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 H2 Formaldehyde Propanol ❸ ❻ ❽❿ ❹ ❶ ⓿ ❹ H2 ❾ ❹ PEDOT-PIL@Fe PEDOT-PIL@Fe ❸❷❾❿❷❷❿⓿❿❺ ❷❺ ❿❿❿❺❸❾❶❶ ❷❿❿❿❿ DT-capped DT-capped Acetic acid Ethylacetate Acetonitrile ❺ ❻ º Acetone Acetonitrile Acetonitrile ❺ Ag/SnO Ag/SnO Ether❹ ❷ ❺ Benzene ❸❶ ⓿ ❸ ❷ ❽ ⓿ ❶ ❷ ❶ Acetic acid ❷ ❺ CdTe/PPy CdTe/PPy ❺ CO ❺ ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 CO 4M2P ❽ Acetonitrile Acetic acid Triethylamine Furan❺ ⓿ ❷Chlorobenzene ❺ ❺ ❷ Water ❶ º❶ Triethylamine Triethylamine Acetonitrile ❺⓿❷ ❺ ❺ ❺ Water Formaldehyde ❸ ❽ ⓿ Triethylamine Nanomaterials 2021, 11, 552 NH3 Ethylbenzene ❷ ❿ ⓿ 18 of 34 ❿º NH3 Triethylamine NH3 ❾ ❿ ❿ ⓿ ❺ ⓿ ❿ ❿ [57] PEDOT-PSS/UL-GO [57] PEDOT-PIL@Fe3O4 [116] ZnO/MWCNTs(0.05 wt.%) [106]SWCNTs/CdA [123] Ti-PTFE [109] FeO/MWCNTs [104] SWCNTs/CdA [123] Au-DT [124] GO/ZnO [111] 2/GO [110] MWCNTs/Au [55] NiO/MWCNTs [105] Ti-PTFE [109] Au-DT [124] GO/ZnO [111] MWCNTs/Au [55] NiO/MWCNTs [105] FeO/MWCNTs [104] Ag/PPy [107] DT-capped Au [122] [58] Ag/PPy [107] PPy [107] 3O4 [116] Pd@MWCNTs [54] [120] [108] Au [122] PPy/rGO [58] SWCNTs-calixarene [120] Pt@MWCNTs [54] MOF-ZnO [121] [117] MWCNTs [104] [54] Ag/SnO2/GO [110] rGO/SnO2 [112] [107] [108] CdTe/PPy (1:10) [108] [54] MOF-ZnO [121] Ag-BC-bsh [117] MWCNTs [104] Ag/SnO2/GO [110] rGO/SnO2 [112] PPy [107] PPy [108] CdTe/PPy (1:10) [108] ZnO/MWCNTs(0.1 wt.%) [106] wt.%) [106] GO/ZnO/GO [111] [55] PPy [108]wt.%) [106] ZnO/MWCNTs(0.05 wt.%) [106] GO/ZnO/GO [111] MWCNTs/Au/MHDA [55] Acetonitrile Hexane ❺ Ethylacetate ❹ ❷ ❺ H2 ❹ H2⓿ ⓿ NH H2 3❹ ❹ ❸ ❽ ❿ ❹ ❹ NH3 Triethylamine Benzene ❺ ❻ Toluene ⓿ ❸ ❿ ❶ ❿ ❸❷❽⓿ ⓿ ❿ ❶ ¿ ❶❷ ❶ ❿ ❿ ❷ ❹ ❿ ⓿ ¿ Ti-PTFE PEDOT-PSS/UL-GO Pt@MWCNTs Pd@MWCNTs MWCNTs/Au/MHDA PPy/rGO CO ❺ Acetic acid CO ⓿ CO ❺ ❺ ❺ [121] Ag-BC-bsh PPy PPy PPySWCNTs-calixarene H NH3 Chlorobenzene ❿ Xylene⓿ ❶ ❿❶ H2 ❿ ❹ ¹ Table27. Cross-responses to different VOCs recorded Water using POMs and CbMs. The color codes and❺❷ ⓿❾ numbers❷❿ ❷ ❷ ❿ ⓿ ❿ ❺ in each column Acetonitrile ❶Water ❶ ❾❷Water ❺ ❺ ⓿ ❷❺ ⓿ ❷ PEDOT-PIL@Fe ❸ ❹ ❷ ❿ ❿ ❿ ❺ ❸ ❾Triethylamine ❿ CO❿ ❿ ❿ DT-capped Acetone ❺ ofCO Hload 2 Ethylbenzene º [57] [57] [57] [109]3O4 [116] [123] [109] [124] [111] [55] [108] [105] (1:10) [108] [104] [107] 3O4 [116] [116] SWCNTs/CdA [123] [109] [123] ❺ [123][124] [109][109] [124][111] [55] [111][111] [105] [55] [55] [55][104] [105][105] [104] [122] [58] [58] [58] 2 [107] [107][107] [54] [120] DT-capped Au [122] Au [122] Pd@MWCNTs [54] [54] [108][120] [120][120] [54] [106] [121] [117] [107] [104] 2/GO [110] Pt@MWCNTs [54] [54] [106][121] [55] [121][121] [117] [117][117] MWCNTs [104] MWCNTs [104] 2 [104] [110] wt.%) [106] wt.%) [106] [111] 2 [112] [55] [54][106] [54][106] [106] [106] [57][111] [110][110] [111][111] [112] [104][107] [58][108] [112] [107][107] [108] [108] [108][108] [55] the table represent the of response for the referenced material towards the same gas concentration of various tested ZnO/MWCNTs(0.05 [124]ZnO/MWCNTs(0.1 4M2P ❽ ❸3❽ Water ❺⓿❷ Water analytes. N/A: data notCO available, RT: room temperature.Toluene ❸ ❿❶❿ ⓿❿❶ ❺ º❿ ❿ ⓿ ⓿ ❹· ❹ ❹NH Response load in % load0in %10 ❿ Response 020 1030 ⓿2040 3050 4060 5070 6080❿7090 80100 90 100 [116] Au [122] [104] 2 [112] [106] PEDOT-PSS/UL-GO PEDOT-PSS/UL-GO PEDOT-PSS/UL-GO Pt@MWCNTs Pd@MWCNTs Pt@MWCNTs Pd@MWCNTs MWCNTs/Au MWCNTs/Au/MHDA PPy/rGO MWCNTs/Au MWCNTs/Au/MHDA MWCNTs/Au MWCNTs/Au/MHDA [58] PPy/rGO PPy/rGO Formaldehyde ❸ ⓿ ❽ ⓿ ppm Ti-PTFE NiO/MWCNTs CdTe/PPyFeO/MWCNTs Ti-PTFE Ti-PTFE NiO/MWCNTs FeO/MWCNTs NiO/MWCNTs FeO/MWCNTs 2/GO 2/GO GO/ZnO Ag/PPy GO/ZnO GO/ZnO Ag/PPy Ag/PPy SWCNTs/CdA 4 SWCNTs/CdA [57]Au Ag-BC-bsh RT—10 ppm Au-DT MWCNTs Ag-BC-bsh Au-DT MWCNTs MOF-ZnO SWCNTs-calixarene wt.%) Ag-BC-bsh Au-DT PPy PPy PPy [120] PPy PPy SWCNTs-calixarene SWCNTs-calixarene GO/ZnO/GO wt.%) [121]MOF-ZnO wt.%) wt.%) MOF-ZnO CdTe/PPy (1:10) GO/ZnO/GO GO/ZnO/GO (1:10) (1:10) Water Xylene ❺⓿❷ H2 ❿ O rGO/SnO rGO/SnO rGO/SnO Number % load⓿ Number 0in %❶10 ⓿ 0❷ 20 ❶ ❸30 ❷ ❹ 40 ❸❺50 ❹❻60 ❺❼70 ❻❽80 ❼ 70❾ 90 ❽ ❿ ❾ ❹ 100❿ [106] Response load Response in 10 20 30 40 50 60 80100 90 3 ❸Ethylacetate ❷❹ ❿❷ ❿ ❿❺ 4 [116] SWCNTs/CdA [123] [124] [111] MWCNTs/Au [55] [105] FeO/MWCNTs [104] RT—N/A Au [122] [58] [54] SWCNTs-calixarene [120] °C—320 ppm[54] [117] MWCNTs [104] 2 [112] PPy [107] RT—N/Awt.%) [106] [111] [55] PEDOT-PSS/UL-GO wt.%)Pt@MWCNTs Pd@MWCNTs FeO/MWCNTsMWCNTs/Au MWCNTs/Au/MHDA PPy/rGO PEDOT-PIL@Fe PEDOT-PIL@Fe PEDOT-PIL@Fe ❷❾❿❷❷❿⓿❿❺ ❺❸❾❶❶ ❷ ❿ ❿ ❿ CO ❿ DT-capped DT-capped Acetone Ti-PTFE NiO/MWCNTs FeO/MWCNTs 2/GO 4 GO/ZnO Ag/PPy SWCNTs/CdA wt.%) Ag-BC-bsh GO/ZnO [111]Au-DT MWCNTs wt.%) MOF-ZnO [108] PPy PPy SWCNTs-calixarene GO/ZnO/GO (1:10) ❺ ❿ O rGO/SnO Ag/SnO Ag/SnO Ag/SnO Number Number⓿ ❶ ⓿ ❷ ❶ ❸ ❷ ❹ ❸ ❺ ❹ ❻ ❺ ❼ ❻ ❽ ❼ ❾ ❽❿ ❾ 250 °C—320 ppm ppm wt.%)ppm ppm ppm ppm ❺ PEDOT-PSS/UL-GO [57] [116] Acetic acid CdTe/PPy (1:10)CdTe/PPy CdTe/PPy SWCNTs-calixarene [120] ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 [120] ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 ppb 3 ppm 4M2P ❽ PEDOT-PSS/UL-GO Pt@MWCNTs Pd@MWCNTs MWCNTs/Au/MHDA RT—5 PPy/rGO PEDOT-PIL@Fe Water ❺⓿❷ DT-capped CdTe/PPy (1:10) [108] O4 ppm wt.%) NiO/MWCNTs GO/ZnO Ag/PPy 450 Ag-BC-bsh Au-DT 3MOF-ZnO GO/ZnO/GO [105] [104] 300 °C—5003O [123] rGO/SnO Ag/SnO Acetonitrile ❺ [122] ppm ppm [110] [111] MWCNTs/Au/MHDA ❸ ❽ ⓿ CdTe/PPy [55] [54] Formaldehyde [57] 3O4 [116] SWCNTs/CdA [123] [109] [124] [55] [105] [104] Ag/PPy [107] DT-capped Au [122] Ag/PPy [107]Pd@MWCNTs [54] [111][54] Ag-BC-bsh [117] Ag/SnO2/GO [110] [106] [106] [111] rGO/SnO2 [112] [107] [108] [55] RT—10 ppm RT—10 ppm RT—20 ppm ppm ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 Pt@MWCNTs [54] 300 °C—150 °C—200 450 °C—200 180 °C—100 300 °C—150 PEDOT-PIL@Fe Au ppm ppm ppm ppm ppm [112] [117] N/A—1000 DT-capped [121] Triethylamine ❺ [104] Ag/SnO ❹❷ ❺ RT—5000 Ethylacetate PPy/rGO [58] RT—N/A RT—N/A Ag/PPy [107] [109] RT—200 RT—250 PEDOT-PSS/UL-GO [108]Pt@MWCNTs MWCNTs/Au MWCNTs/Au/MHDA PPy/rGO ZnO/MWCNTs(0.1 ❿ ⓿ ❿ RT—20 NH3 Ti-PTFE NiO/MWCNTs FeO/MWCNTs Au-DT wt.%) MOF-ZnO PPy rGO/SnO2PPy SWCNTs-calixarene GO/ZnO/GO NiO/MWCNTs MWCNTs/Au Pd@MWCNTs [110] SWCNTs/CdA Acetic acid ❺ PPy [107] [108] RT—5 RT—5 RT—5 RT—5 RT—5 /GO GO/ZnO/GO ZnO/MWCNTs(0.05 ZnO/MWCNTs(0.1 PEDOT-PIL@Fe 10 ❹ CdTe/PPy (1:10)PEDOT-PIL@Fe 20 H230 Ag-BC-bsh Response loadAcetonitrile in % 0 10 40 50 60 ❺ 70 80 90 100 Response load in % 0 20 30 40 50 60 70 80 90 100 250 Ag-BC-bsh [117]wt.%)MOF-ZnO MWCNTs PPyGO/ZnO Ti-PTFE [121] DT-capped ❺ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 ⓿ ❶ ❷ CO❸ ❹ ❺ ❻ ❺ Au-DT ❼ ❽ ❾ ❿ ⓿ ❶ ❷ Methanol ❿ ❿ ❿ ❹ ❿ Number ❸Triethylamine ❺ ❿ ❾❷❻❷❾❶⓿⓿❻ Number Ag/SnO 2 Water ❺ ⓿ ❷ Ethanol ❾ ❾ ❽ ❹ ❺ ❾ ❺ ❺ ❿ NH ❿3 ❸ ❾ ❽ ❿ ❿ ❿ ❶ ⓿ ⓿ ❿❿ ❾ ❼ ❽⓿ ❻ ❿ [57] [109] 3O4 [116] [123] Ti-PTFE [109] Au-DT [124] [111] [55] [105] [104] [57] Au [122] [58] [54] [54] [121] [117] MWCNTs [104] 2/GO [110] [106] Au-DT [124]wt.%) [106] [111] rGO/SnO2 [112] PPy [107] [108] [55] Propanol ❻ ❿H2 ❹ ❶ Response load in ❾% 0 10 20 30 40 50 60 70 80 90 100 ❹ Response load Response in % load0in %10 020 1030 2040 3050 4060 5070 6080 7090 80100 90 100 PEDOT-PSS/UL-GO Pt@MWCNTs Pd@MWCNTs MWCNTs/Au MWCNTs/Au/MHDA PPy/rGO CO ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ ❺ NiO/MWCNTs FeO/MWCNTs GO/ZnO SWCNTs/CdA Ag-BC-bsh Ag/SnO2/GO MOF-ZnO SWCNTs-calixarene GO/ZnO/GO Ether ❸❶ Number Number load⓿ Number Response in %❶ ⓿ 0❷ ❶ ❸ ❷ 10 ❹ ❸ 20 ❺ ❹ 30 ❻ ❺ 40 ❼ ❻ 50 ❽ ❼ 60 70❾ ❽80❿ ❾ ❿ 90 100 Water ❺⓿❷ PEDOT-PIL@Fe Furan ❷ DT-capped Number ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ Ag/SnO Response load in % 0 10 20 30 40 50 60 70 80 90 100 PEDOT-PIL@Fe3O4 [116] SWCNTs/CdA [123] [111] MWCNTs/Au [55] [105] FeO/MWCNTs [104] DT-capped Au [122] PPy/rGO [58] Ag/PPy [107] Pd@MWCNTs [54] SWCNTs-calixarene [120] [54] MWCNTs [104] 2 [112] PPy [107] PPy [108] [108] [106] ZnO/MWCNTs(0.05 wt.%) [106] GO/ZnO/GO [111] MWCNTs/Au/MHDA [55] ZnO/MWCNTs(0.1 ZnO/MWCNTs(0.05 Hexane ❷ Number ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ PEDOT-PSS/UL-GO Pt@MWCNTs Benzene ❻ ⓿ ❸❷❽⓿ ❶❷ ❶ ❷ Ti-PTFE NiO/MWCNTs GO/ZnO ZnO/MWCNTs(0.1 wt.%) MOF-ZnO CdTe/PPy (1:10) rGO/SnO Chlorobenzene ❶❶ Response load in % 0 10 20 30 40 50 60 70 80 90 100 Ethylbenzene Response load in% 0 10 ❾ 20 30 40 Number 50 ⓿ ❶ ❷ 60 ❸ ❹ ❺ 70 ❻ ❼ ❽ ❾ 80 ❿ 90 100 Toluene Number ❸ ❿❶❿ ⓿❿❶ ❿ ❿ ⓿ ⓿ ❹ ❹ ❹ ❸¶ ❽ · ¸ ¹ º » ¼ ½ ¾ ¿ Xylene ⓿ ❿ Acetone ❸❷❾❿❷❷❿⓿❿❺ ❷❿❿❿❺❸❾❶❶ ❷❿❿❿❿ 4M2P ❽ Response load in % 0 10 20 30 40 50 60 70 80 90 100 Formaldehyde ❸ ❽ ⓿ Number ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ Ethylacetate ❹❷ ❺ Acetic acid ❺ Response load in % 0 10 20 30 40 50 60 70 80 90 100 Acetonitrile ❺ Number ⓿ ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❾ ❿ Triethylamine ❺ NH3 ❿ ⓿ ❿ H2 ❹ CO ❺ Nanomaterials 2021, 11, 552 17 of 33 Table 8. Summary of MOXs for VOCs sensing, their features and corresponding synthesis method and integration. Material Morphology Features, nm Method Integration Principle Ref. ZnO PF 27 PS WCS Transfer Resistive [36] 200–400 S ZnO NShs WCS Transfer Resistive [39] 10–60 T ZnO NRgs 180 OØ 120 IØ WCS Transfer Resistive [40] 1000–1500 L ZnO NShs (NFls) 1000 S WCS Transfer Resistive [37] ZnO PF 33.8 CS Sputtering Direct Resistive [38] ZnO NTPs (NRds) 1000 L- NRds PVD Transfer Resistive [76] 9 Ø- NRds Au@ZnO NPs@NShs 9 Ø-NPs WCS PhA Transfer Resistive [65] Pd@ZnO NPs@NRds 200 Ø-NRds WCS Direct QCM [71] SnO2 PF 6–15 Ø-Pores WCS Transfer Resistive [46] 150 Ø-Pores SnO2 PF WCS MA Transfer Resistive [45] 20 Ø-NPs SnO2 NShs 6.3 CS WCS Transfer Resistive [47] Ce-doped SnO2 PF 10.3 CS-SnO2 WCS Transfer Resistive [73] WO3 NNs 100 T 10,000 L CVD Direct Resistive [48] Au@WO3 NPs@NNs 100 T 10,000 L CVD Direct Resistive [48] 3–10 S-NPs Pt@WO3 NPs@NNs 100 T 10,000 L CVD Direct Resistive [48] 1–5 S-NPs 5–100 Ø-NNs Fe2 O3 @WO3 NPs@NNs 10,000 L-NNs CVD Direct Resistive [49] 4–15 Ø-NPs TiO2 NShs (NFls) 120 L 12–23 T WCS Direct Resistive [51] 20 Ø-NPs 100 Pd@TiO2 NPs@NRds Ø-NRs CVD + WCS Direct Resistive [52] 4500 L-NRs In2 O3 NShs-hMSp (NFls) 20 T WCS Transfer Resistive [50] CuO NShs (NFls) 2000 50 S T WCS Transfer Resistive [74] Co3 O4 cNCs 200 S WCS Transfer Resistive [69] Co3 O4 ML 17.6 CS WCS Transfer Resistive [72] SiO2 NPs - - Transfer CTL [53] CeO2 NPs - - Transfer CTL [53] Y2 O3 NPs - - Transfer CTL [53] La2 O3 NPs - - Transfer CTL [53] Au@La2 O3 NPs@NRds - WCS Transfer CTL [119] TiO2 -SnO2 PF - WCS Transfer CTL [102] 150 Ø−Pores CuO/SnO2 PF WCSMA Transfer Resistive [45] 20 Ø-NPs ZnO-CuO NShs 20–25 T PVD Direct Resistive [75] SiO2 /Fe3 O4 NPs - - Transfer CTL [53] ZnCo2 O4 HSp 72 Ø WCS Transfer Resistive [70] Au NPs 36 Ø WCS Transfer LSPR [66] Ag NPs 51 Ø WCS Transfer LSPR [67] PF: porous film, NShs: nanosheets, NRgs: nanorings, NTPs: nanotetrapods, NFls: nanoflowers, NPs: nanoparticles, NRds: nanorods, NNs: nanoneedles, hMSp: hierarchical microsphere, cNCs: concave nanocubes, ML: monolayer, HSps: hollow spheres, PS: pore size, S : size, T : thickness, Ø : diameter, OØ : outer diameter, IØ : inner diameter, L : length, CS : crystal size, WCS: wet chemical synthesis, PhA: photo- assisted, MA: microwave assisted, CVD: chemical vapor deposition, PVD: physical vapor deposition, QCM: quartz crystal microbalance, CTL: cataluminescence, LSPR: localized surface plasmon resonance. Nanomaterials 2021, 11, 552 18 of 33 Table 9. Summary of POMs and CbMs for VOCs sensing, their features and corresponding synthesis method and integration. Material Morphology Features, nm Method Integration Principle Ref Pt@MWCNTs NPs/MWCNTs 2 Ø /Up to 50,000 L CVD Transfer Resistive [54] Pd@MWCNTs NPs/MWCNTs 3 Ø /Up to 50,000 L CVD Transfer Resistive [54] PEDOT- CSh (LsPEDOT – PIL@Fe3 O4 15 S-NPs WCS Transfer Resistive [116] MNPsPIL@Fe3O4 ) ZnO/MWCNTs HSpsZnO - 300–350 Ø-HSps WCS Transfer Resistive [106] MWCNTs 30–40 T-HSps DT-capped Au MLShsDT 100–300 FT WCS Transfer Resistive [122] NPsAu 2–5 Ø- NPs MOF-ZnO NCgs 100 S-NPs 60 CØ 25 PST WCS Transfer Resistive [121] 1–5 Ø SWCNTs/CdA SWCNTs WCS Transfer QCM [123] 1000–10,000 L SWCNTs- SWCNTs - WCS Resistive calixarene Transfer [120] PANI-CD NPs 100 WCS Transfer Resistive [68] Ti-PTFE Cls 10–30 NPs PVD Transfer Resistive [109] 10 ± 7 Ø-NPs Ag-BC-bsh NPsAg 45 ± 10 FØ WCS Transfer LSPR [117] NPp >10,000 L-NPp NPsAu Au-DDDT Ls 4 Ø-NPs /Up to 60 T-Ls LBL-SA Transfer Resistive [124] PEDOT-PSS/UL- NShsUL-GO GO PF 10,000–300,000 S-UL-GO PVD Transfer Resistive [57] Ag/SnO2 /GO NPsAg - WCS Transfer Resistive [110] NRdsZnO GO/ZnO NShsGO 640 L-ZnO WCS Transfer Resistive [111] NRdsZnO GO/ZnO/GO NShsGO 640 L-ZnO WCS Transfer Resistive [111] Up to 50,000 L-MWCNTs MCWNTs- MWCNTs/Au NPsAu 3–15 OØ-MWCNTs CVD/WCS Transfer Resistive [55] 3–7 IØ-MWCNTs 2 Ø-Au Up to 50,000 L-MWCNTs MWCNTs/Au/ MCWNTs- Resistive MHDA NPsAu -MLMHDA 3–15 OØ-MWCNTs CVD/WCS Transfer [55] 3–7 IØ-MWCNTs 2 Ø-Au NiO/MWCNTs NPs/MWCNTs 25 Ø-NPs WCS Transfer Resistive [105] 20–35 Ø-MWCNT 50,000 L-MWCNTs O2 /MWCNTs MWCNTs 3–15 OØ-MWCNTs CVD Transfer Resistive [104] 3–7 IØ-MWCNTs 50,000 L-MWCNTs FeO/MWCNTs MWCNTs 3–15 OØ-MWCNTs CVD Transfer Resistive [104] 3–7 IØ-MWCNTs PPy/rGo NPs−PPy /NShs−rGO 80 Ø-NPs WCS Transfer Resistive [58] rGO/SnO2 NShs−rGO /NPs−SnO2 6–10 Ø-NPs WCS Transfer Resistive [112] Ag/PPy NPs 17 ± 3 S-Au-NPs WCS Transfer SAW [107] 44 ± 10 S-PPy-NPs 3.1 ± 0.7 S-QDs CdTe/PPy QDs−CdTe /NPs−PPy 35–55 S-NPs WCS Transfer SAW [108] MWCNTs: multi-walled carbon nanotubes, PEDOT: Poly(3,4-ethylenedioxythiophene), PIL: polymerized ionic liquid, DT: decanethiol, MOF: metal-organic frameworks, SWCNTs: single-walled carbon nanotubes, CdA: cadmium arachidate, PANI: polyaniline, CD: cyclodex- trin, PTFE: polytetrafluoroethylene, Ag-BC: Ag nanoparticles embedded in bacterial cellulose nanopaper, bsh: blue shift, DDDT: 1,12- dodecanedithiol, PSS: poly(styrenesulfonate), UL-GO: ultra-large graphene oxide, GO: graphene oxide, MHDA: 16-mercaptohexadecanoic acid, PPy: polypyrrole, rGO: reduced graphene oxide, NPs: nanoparticles, CSh: core shell, Ls: layers MNPs: magnetic nanoparticles, HSps: hollow spheres, MLShs: monolayer shells, NCgs: hierarchical hollow nanocages, Cls: clusters, NPp: nanopaper, NShs: nanosheets, PF: porous film, NRds: nanorods, ML: monolayer, Ø: diameter, L : length, S : size, T : thickness, FT : film thickness, CØ : cavity diameter, PST : porous shell thickness, FØ : fiber diameter, OØ : outer diameter, IØ : inner diameter, QDs: quantum dots, CVD: chemical vapor deposition, WCS: wet chemical synthesis, LBL-SA: layer-by-layer self-assembly, PVD: physical vapor deposition, QCM: quartz crystal microbalance, SAW: surface acoustic wave. Nanomaterials 2021, 11, 552 19 of 33 • Aromatic hydrocarbons Aromatic hydrocarbons are generally associated with compounds containing the benzene ring. These compounds exhibit aromaticity, most of them characterized by sweet or pleasant odor. The reports on sensitive materials for aromatic hydrocarbons are not as extensive as those for oxygenated hydrocarbons. In fact, a search of the literature in terms of their selectivity indicates frequently poor load of response for these compounds among oxygenated hydrocarbons, as well as esters, ethers, and aldehydes. According to Tables 6 and 7, the strongest selectivity to benzene, in front of various alcohols, and other aromatic compounds such as toluene and xylene, were recorded for modified La2 O3 nanorods with Au nanoparticles [119] and coral-like TiO2 /SnO2 porous film [102] implemented using CTL concept. Partial selectivity to benzene was also noticed for MWCNTs decorated with Au nanoparticles [55], especially in comparison to acetone. Materials based on modified Fe2 O3 /WO3-x nanoneedles [49] and porous flower-like SnO2 nanosheets [47] also appear to be a good option to attain partial selectivity to toluene among ethanol, methanol, and acetone. However, the cross-response to benzene and formaldehyde could still be an issue. As for xylene, the selectivity to this compound among acetone, methanol, isopropanol, and chloroform seems to be greatly improved for Pd-functionalized ZnO nanorods [71] integrated into quartz crystal microbalance (QCM). Results for CuO/SnO2 , in contrast, show higher interferences for xylene in front of alcohols and other aromatic compounds, though the interferences could be reduced towards formaldehyde and ammonia. In the same gas family, a strong selectivity to xylene among ethanol, benzene, formaldehyde, and carbon monoxide has been reported using monolayers of periodic porous Co3 O4 inverse opal thin films [72]. Although calixarene functionalized SWCNTs has shown good potential for xylene detection, high interferences from toluene and ethylbenzene are noticed [120]. • Carbonyl compounds Carbonyl compounds can be also classified as oxygenated hydrocarbons, since they contain oxygen atoms in their structure, but unlike simple oxygenated hydrocarbons, carbonyl compounds come from carboxylic acids. This group includes ketones, aldehydes, carboxylic acids, acid anhydrides, esters, amides, and acid halides. As far as acetone detection is concerned, the reports indicate a strong selectivity to this compound among methanol, butanone, isopropanol, and toluene for hierarchical 3D TiO2 nanoflowers operating at low temperature (60 ◦ C) [51]. Other materials, based on unmodified or Au-modified WO3 nanoneedles [48], also point toward good selectivity to acetone among ethanol, methanol, and toluene, contrary to Ce-doped SnO2 , which revealed higher interferences for acetone among methanol, ethanol, and formaldehyde [73]. The use of a mixture based on ZnO-CuO has also shown the potential to discriminate acetone among ethanol and typical toxic gases including ammonia, nitrogen dioxide, and hydrogen sulfide [75]. In2 O3 [50] and Y2 O3 (with CTL-based signal) [53] present themselves as other unmodified MOXs with prospective partial selectivity to acetone, the first suggesting good or moderate discrimination from methane, formaldehyde, and chloroform, whereas the second showing good discrimination from various alcohols, aldehydes, and aromatic com- pounds. ZnO/MWCNTs hybrid material with MWCNTs content of 0.1 wt.% also showed to be selective to acetone among ethanol, methanol, and ether [106]. Other materials such as metal-organic frameworks derived hierarchical hollow ZnO nanocages (MOF-ZnO) displayed good acetone discrimination among ethanol, benzene, toluene, and ethylac- etate [121]. In the same line, Ag/SnO2 /GO ternary nanocomposites proved relevant for acetone detection among furan, formaldehyde, chlorobenzene, and ammonia [110], while other graphene-based materials such as GO/ZnO/GO showed moderate interferences to ethanol but good selectivity among methanol and benzene [111]. One of the best selectivities to formaldehyde among several gases (ammonia, nitrogen dioxide, and carbon monoxide) and vapors (benzene, acetone, and methanol) all at ppb level has been identified for nonstoichiometric Co-rich ZnCo2 O4 hollow nanospheres [70]. Other candidates to achieve partial selectivity to formaldehyde suggesting potential low Nanomaterials 2021, 11, 552 20 of 33 interferences to butanol, methanol, acetone, and ammonia involve nanosheets of SnO2 prepared by heat treating Cu3 SnS4 nanostructures [47]. NiO/MWCNTs [105] have also shown good sensitivity to formaldehyde, although the selectivity among other tested gasses, including methanol, ethanol, propanol, and acetone, was poor. The literature also shows good responses at sub-ppm concentrations for acetalde- hyde by employing ZnO nanostructures [39,40], however, their performance in terms of selectivity in respect to formaldehyde have proved to be poor. The information in Ta- ble 6 for ethylacetate shows excellent results for CTL sensors based on SiO2 and CeO2 nanoparticles [53] with moderate interference to acetone in the case of CeO2 and low interference to ether, tetrahydrofuran, ethanol, methanol, aldehydes (e.g., formaldehyde and acetaldehyde), and halogenated hydrocarbons (e.g., chlorobenzene and chloroform). Humidity Effects—The Uninvited Guest The relative humidity (RH) in the atmosphere is one of the most common interfering species that has a great impact on the sensing properties of the material and its operation in a “real” environment. This is due to water molecules being sorbed at the surface (e.g., in MOXs) or in the bulk (e.g., in POMs) and abruptly changing the properties (e.g., electrical or mechanical) of the gas sensitive material. In MOXs, for instance, the adsorption of water at temperatures below 150 ◦ C is attributed to physisorption or hydrogen bonding. However, at high temperatures (150–500 ◦ C) the literature proposes various possible mechanisms behind the surface conductivity changes in the presence of water vapor [1]. One of these mechanisms, for instance, suggests that the OH− side of water molecule can react with + + the Lewis acid sites (metal cation (Mmetal )) forming Mmetal −OH− , while the dissociated hydrogen from water can react with the lattice oxygen from the Lewis base or with oxygen adsorbed on surface. This mechanism involves the release of electrons (e− ). As a result, the changes of conductivity (e.g., in resistive type sensors) or mass (e.g., in QCM type sensors) produced by the target analytes are hindered and the whole performance of the sensor is affected [71]. The possibility for hydrogen bonding interactions with water molecules at room temperature is also presented in the case of POMs, due to the polar groups on their backbone (e.g., Nδ- –Hδ+ in PPy and PANI). This can further result in sorption of the water molecules within the POM, which fill the free volume fraction in the polymer (e.g., swelling) and reduce the gas permeability [108]. The adsorption and permeation of water molecules within the CbMs such as graphene oxide also respond to the hydrogen bonding between water and hydrophilic groups on graphene oxide (e.g., hydroxyl groups), which can cause direct interference with gas sensing [125]. Usually, not all the reports in the literature related to VOCs sensitive materials analyze the effects of humidity ambient on the sensing performance. In fact, only a few reports of those summarized in Tables 5 and 6 show humidity interference tests. From these results, specifically, the analysis of Co-rich ZnCo2 O4 hollow spheres in 80% RH ambient shows that the response of this material to 100 ppb of formaldehyde is relatively stable with a loss of response of only 28% compared to the response registered in dry ambient [70]. Similar studies for ZnO modified with low (5%) amounts of Au to various VOCs (isoprene, ethy- lacetate, heptane, chlorobenzene, benzene, and formaldehyde) reported minor fluctuations of the response for RH below 35% [65]. The effect of humidity on the sensing performance of hierarchical 3D TiO2 nanoflowers to acetone was also tested. These studies showed that the response to acetone in 75% RH deviates only ± 4.5% from that in dry air [51]. Tests of unmodified SnO2 and rGO modified SnO2 to ethanol in a humid atmosphere (from 24 to 98% RH) registered a loss of response for both materials, although the losses were superior for SnO2 than for rGO-SnO2 [112]. Evaluation of FeO-MWCNTs and oxygen- treated MWCNTs in moisture also revealed a decrement of the response for both materials. This drop of response turned out to be larger when the humidity changed from 0 to 20% RH than when the humidity increased further from 20 to 50 or 80% RH. Moreover, the loss of response to the step changes of moisture proved to be greater for the oxygen treated MWCNTs than for the FeO-MWCNTs based sensors [104]. Nanomaterials 2021, 11, 552 21 of 33 In the literature, one can also find different strategies to avoid the high impact of humidity in VOCs sensitive materials. The most common solutions encompass the use of external components in the sensor device as filters or dehydration elements [126]. Other strategies to reduce the impact of humidity typically include the incorporation of intentional humidity-insensitive additives (surface decoration), such as NiO [127,128], CuO [129,130], or SiO2 [131]. Previously, it has also been proven that the application of high-humidity (90% RH) aging treatment in MOXs such as WO3 [132] and SnO2 (the last loaded with Pd and Au) [133] significantly reduces the humidity effects at the surface while sensing toluene or a specific group of total VOCs in a range of 25 and 75% RH. Similar treatment was performed for Pt-loaded SnO2 films, although the humidity interference towards total VOCs for this system was found ineffective [133]. Recent studies also indicate the possibility to attenuate the humidity interference in MOXs, such as ZnO, by tuning their morphology, structural, and surface properties to increase the hydrophobicity of the surface not only at the structural level but also at the chemical level [134]. These findings are linked with those reported on oxygen treated MWCNTs, as the hydrophilic MWCNTs surface stimulated by oxygen plasma treatment made the MWCNT more sensitive to humidity compared to non-oxygen treated FeO- MWCNTs [104]. Similarly, the tests of MWCNTs/Au and MWCNTs/Au/MHDA in a humid ambient suggested that the hydrophilic nature of the carboxyl terminal from the MHDA molecules grafted to the MWCNTs/Au binds the water molecule keeping it far away from the MWCNTs, thus, providing more resilience to the MWCNTs to humidity as compared to the unmodified MWCNT [55]. 5. Enabling the Material Properties for Their Practical Use 5.1. Transducing Platforms—On the Capture of the Sensor Response The sections above focused on the materials with the ability to sense VOCs and their main sensing properties. In practice, however, the enabling of these materials for their use in advanced applications demands their coupling with appropriate transducing platforms, which allow measuring the electrical or optical changes induced by the physicochemical phenomena experienced in the material during the gas-solid interactions. These platforms can be based on different working principles: mass-sensitive (e.g., resonating cantilevers), thermal (e.g., pellistors, thermoelectric, or Seebeck-effect-based sensors), optical (e.g., phosphorescence/fluorescence or chemiluminescence sensors), or electrochemical (e.g., conductometric and potentiometric sensors) [135]. Figure 5 displays examples of trans- ducing platforms for measuring resistive and mass-load changes. Electrochemical-based transducing platforms are the most common approach in the literature and commercial field, probably due to their relatively simple fabrication process and characterization. Within this group, chemoresistive sensors are generally the most representative and this fact is reflected in Tables 8 and 9, in which most of the accounted materials for VOCs report the operation under the resistive principle. One can see in these tables that VOCs sensitive materials are also enabled by monitoring optical parameters such as CTL [53,102,119] or LSPR [66,67], and mass-load changes mainly using QCM [71], although with less frequency. From this, it is concluded that the properties of VOCs sensitive materials can be exploited using various physical changes so the systems are not limited to only resistive changes. Enabling the signal capture of the different physical changes, separately or in conjunction, would allow for VOCs sensing systems with better selectivity. Back in the past, most of the technology for gas sensing transducing platforms relied on thick film technology, particularly using silica or alumina substrates. Currently, however, the transducing platforms exploit micro/nano fabrication technologies (usually based on silicon as substrate), which makes it possible for their incorporation into integrated circuits (IC) at micro/nano scale in a single chip [135,136]. During the past decades, various types of gas/vapor sensors (resistive, thermal, mass-sensitive, optical) have been reported in the literature using standard micro-electro-mechanical systems (MEMS) technology. This technology for instance has been shown to facilitate the formation of thermally well- Nanomaterials 2021, 11, 552 22 of 33 isolated structures for more efficient conductometric and Seebeck-effect based sensors or the formation of miniaturized cantilevers for mass-sensitive based sensing. Recently, miniaturized light platforms for the photoactivation instead of thermoactivation of gas sensitive material have also been developed [137]. Another significant advantage of using micro/nano fabrication technologies is the possibility to integrate several different transducers on a single chip along with the driving and signal conditioning circuitry or other smart features (e.g., wireless communication) Nanomaterials 2021, 11, x FOR PEER REVIEW 25 of 36 to build electronic noses with potentially low cost via mass production [135]. A good approximation of this concept, in which various transducing principles were implemented monolithically in a microsystem to operate simultaneously, was developed previously control, using as well as emerging micro/nano fabricationniches dedicated technology to the [138]. food industry, Recently, agriculture, other emerging and med- technologies icine. Most based of these on flexible fields are substrate keen tosuch materials implement solutions as polymers, in line textiles, andwith theare paper digital future positioning well in electronics and thus in sensing systems (including VOCs sensors) [139,140]. be and the smart anything everywhere (SAE) concept, in which VOCs sensors could the These next family of sensors for smart portable devices. Hence, there is a need to go novel technologies can introduce biocompatibility, reusability, and/or biodegradability to further in the effective consumer integration electronics, and synthesis alleviating of sensitive environmental issuesmaterials into miniaturized and reducing systems significantly the costs with low power associated consumption with recycling and reduced fabrication costs at a large-scale. operations. Figure 5. Figure 5. Schematic Schematic illustration illustrationof oftransducing transducingplatforms: platforms:(a) (a)micromachined micromachinedresistive, resistive,(b) (b)quartz quartz crystal microbalance, and (c) micromachined cantilever. From references [71,141]. crystal microbalance, and (c) micromachined cantilever. Adapted from [71,141], with permission from Elsevier B.V., 2018 and 2015, respectively. 5.2. Synthesis of Materials—Path to the On-Chip Integration Therefore, micro/nano The integration technologies of transducing haveand platforms thematerial potentialsynthesis to contribute andisenable methods im- an essen- proving tial phasefurther for VOC thesensors’ properties of materials scalability for different and large production.applications. VOCs monitoring Such integration can mostly systems builtby be achieved bydirect micro/nano technologies or transfer may have The methods [140,142]. an impact in various first (direct fields method) and cover involves the the current selective demands of deposition of traditional the material markets focused over the on the industry transducing platform.andDirect environmental methods con-can trol, reduceas well as emerging the processing niches time dedicated and steps of thetowhole the food industry, sensor agriculture, assembling process,and medicine. particularly Most when using bottom-up approaches (i.e., synthesis of materials through assembling ofand of these fields are keen to implement solutions in line with the digital future at- the omssmart anything derived everywhere from chemical (SAE) concept, precursors) in which rather than VOCs approaches top-down sensors could bebased (i.e., the next on family carving,ofslicing, sensorsorfor smart aportable etching devices. macroscale Hence, material thereThe source). is a second need to(transfer go further in the method) effective integration relies on the and synthesis of use of pre-synthesized sensitive materials materials and their dry into miniaturized or wet systems with transfer (re-deposition) low power consumption and reduced fabrication costs at a large-scale. over the transducing platform. Transfer methods can facilitate a broad choice of materials and modifications and can be especially useful when integrating aligned single 1D struc- 5.2. Synthesis of Materials—Path to the On-Chip Integration tures into a transducing platform, although one must be aware of the surface contamina- The integration tion issues of transducing which entail platforms most of the transfer and material procedures synthesis [110]. methods is [111] Dielectrophoresis an essen- and tial phase for VOC sensors’ scalability and large production. Such integration contact printing using polydimethylsiloxane (PDMS) [112] are two examples of the can mostly wet be achieved by direct or transfer methods and dry transfer approach, respectively. [140,142]. The first (direct method) involves the selective deposition of the material over the transducing platform. Direct methods can Transfer methods are by far the most used approaches in the literature for integrating reduce the processing time and steps of the whole sensor assembling process, particularly the materials into the transducing platforms. Tables 8 and 9 evidence this fact, indicating when using bottom-up approaches (i.e., synthesis of materials through assembling of atoms a preference for developing VOCs sensitive materials by wet chemical synthesis (WCS) derived from chemical precursors) rather than top-down approaches (i.e., based on carving, and their subsequent transfer (or re-deposit) over the transducing platforms. The pre- slicing, or etching a macroscale material source). The second (transfer method) relies on ferred WCS methods for VOCs sensitive MOXs, POMs, or CbMS include sonochemical process [39,40], hydrothermal process [36,50–52,46,47,73,102], one-pot wet-chemical method [18], photodeposition [64], sol-gel method [47], microwave-assisted approach [45,112,116], precipitation [69,70,105,122], calcination of precursors [88], seed mediated growth method [82], pyrolysis [91], spray deposition [83], Langmuir–Blodgett method Nanomaterials 2021, 11, 552 23 of 33 the use of pre-synthesized materials and their dry or wet transfer (re-deposition) over the transducing platform. Transfer methods can facilitate a broad choice of materials and modifications and can be especially useful when integrating aligned single 1D structures into a transducing platform, although one must be aware of the surface contamination issues which entail most of the transfer procedures [142]. Dielectrophoresis [143] and contact printing using polydimethylsiloxane (PDMS) [144] are two examples of the wet and dry transfer approach, respectively. Transfer methods are by far the most used approaches in the literature for integrating the materials into the transducing platforms. Tables 8 and 9 evidence this fact, indicating a preference for developing VOCs sensitive materials by wet chemical synthesis (WCS) and their subsequent transfer (or re-deposit) over the transducing platforms. The preferred WCS methods for VOCs sensitive MOXs, POMs, or CbMS include sonochemical process [39,40], hydrothermal process [36,46,47,50–52,73,102], one-pot wet-chemical method [37], photode- position [65], sol-gel method [74], microwave-assisted approach [45,112,116], precipita- tion [69,70,105,122], calcination of precursors [119], seed mediated growth method [66], pyrolysis [121], spray deposition [116], Langmuir–Blodgett method [123], chemical bath deposition [111], electrochemical methods, and chemical oxidation polymerization [58]. The transfer methods are generally based on the formation of pastes or suspended solu- tions using common solvents (e.g., ethanol, terpineol) for their subsequent printing or drop coating. Previously, it was found that the synthesis and the post-treatment process of materials (e.g., MWCNTs) can also facilitate the transfer procedures by improving the solubility or dispersion of materials [104]. The reports centered on WCS synthesis concentrate regularly on tuning conditions and finding their correlation with the material functionality towards common VOCs. An example of the synthesis of ZnO shows that different salts (e.g., zinc acetate, zinc chloride, or zinc sulfate) in hydrothermal synthesis influence the morphologies and microstructural features of the porous ZnO products. Measurements on the gas sensing properties of the porous products to VOCs (e.g., acetone, chlorophenol, and formaldehyde) revealed improved sensing characteristics for porous ZnO synthesized using zinc sulfate compared to those obtained using zinc chloride and zinc acetate. A similar example on the synthesis of SnO2 shows that the use of a starch template for the hydrothermal synthesis of worm- like nanostructured SnO2 and the variation of starch content has a great impact on the microstructure of the final product. The gas-sensing tests of this study exhibited better sensing properties towards butanol for the material processed with higher content (2 g) of starch [46]. In the same way, the sensor fabricated with 0.05 wt.% of MWCNTs showed to be selective to ethanol and methanol while the sensor with 0.1 wt.% of MWCNTs demonstrated selectivity to acetone and ether among the other tested VOCs [106]. These examples and other similar ones in the literature often attribute the improvements to the differences in morphology and/or surface area [36]. The effect of the impurity levels derived from the synthesis and transfer-processing steps are generally less discussed, despite there being evidence that residual ions (e.g., chlorine, sulfur, alkali and alkaline earth metals) account for the widespread of physical and chemical properties of similar oxide materials [145]. Direct methods for integrating VOCs sensitive materials into transducing platforms are a minority in Tables 8 and 9. Among the representative techniques for direct integration are sputtering [38], chemical vapor deposition (CVD) [48,49], and hydrothermal [51] and electrochemical deposition [71]. These techniques allow for the selective integration of films and structures by implementing masks or patterns on desired locations. Even though these techniques are not exempt from introducing residual impurities during material processing, the in-situ integration approach with no further manipulation or extra steps diminishes considerably the contamination of the material surface. An example of a direct method using CVD shows that the integration of one-dimensional WO3 structures modified with Au or Pt either over MEMS- or polymer-based transducing platforms is feasible in a single step. These structures reported enhanced sensing properties to acetone or methanol as a function of the surface modification with Au or Pt, respectively. Another example of the Nanomaterials 2021, 11, 552 24 of 33 use of the direct method shows the integration by electrodeposition of ZnO rods modified with Pd over QCM-based transducing platforms [71]. As for the previous example, this report also emphasized the improvement of sensing properties to ethanol and xylene by modifying the ZnO structures with Pd. Overall, the literature search suggests that there is still a lot of room for investigating synthesis methods for direct integrations of tuned VOCs sensitive materials. 5.3. Machine-Learning—Mimicking the Human Olfactory Systems Another essential phase in the practical use of gas/vapor sensitive nanomaterials and systems is their coupling with appropriate machine learning algorithms. The ensemble of these elements, i.e., hardware and software, is usually known as electronic nose (e- nose) technology, in reference to their approximation to reproduce the human olfactory system [146,147]. In e-noses, the selection of suitable materials and transducers for the sensor array is accompanied by the selection of proper machine-learning algorithms to comprehend the large amount of data delivered by the sensor array and identify/classify the information with the aim to make the system more selective and precise for practical implementations. Figure 6 displays a summarized representation of the main constituents of an e-nose system. The figure shows the hardware stage (i.e., materials, transducers, and sensor arrays) and the machine-learning stage, which involves the data collection, modeling, training, and evaluation steps. So far, this review has focused on the hardware stage, and in particular on VOC sensitive materials, whereas this section aims to provide the reader with a complementary overview of the basic attributes of e-noses and their application. As the scope of this topic is vast, the section is not intended to be exhaustive and for deeper insight into e-noses, their principles, and application, we invite the reader to revise recent reviews, for instance, [148–152]. Figure 6. Block representation of the main constituents of an e-nose system. The hardware block includes the sensitive materials, transducers, integrated sensor arrays, and signal processing elements. The software or machine learning block includes the data collection and preprocessing, featuring extraction, pattern recognition, and classification algorithms. Amongst a host of machine-learning algorithms, those based generically on pattern recognition methods, i.e., algorithms that recognize (ir)regularities or specific patterns in the data to subsequently classify them by means of training models, are frequent in e-nose applications. In this context, methods such as principal component analysis (PCA), discriminant analysis (linear—LDA, function—DFA, stepwise—SDA), regressions (partial least squares—PLSR, generalized least squares—GLSR, multiple linear—MLR), support vector machines (SVMs), and artificial neural networks (ANN) are the most sounded data analysis methods [148]. Typical e-nose applications, in which VOC analytes are potentially involved, include the discrimination of samples (not necessarily specific analytes) by finding differences in the patterns to identify, for instance, the meat origin [153], rice aging [154], beverage brands [155], coffee beans [156], or controlled populations of healthy and unhealthy sub- jects [157–159]. However, there are also other studies, which target the discrimination of more specific analytes and their concentrations. Table 10 summarizes a few exam- ples in this context with focus on e-noses with (nano)materials-based sensor arrays. In terms of algorithmic tools, the works make references especially to PCA, LDA, and ANN methods [160–164]. Whilst PCA and LDA methods are useful tools for reducing the di- mensionality of measurement space and extracting the information for pattern recognition, Nanomaterials 2021, 11, 552 25 of 33 ANN methods are usually employed for classification tasks, although ANN can also be trained to learn sample patterns and predict responses for unidentified samples. The use of deep learning tools with basis on ANN methods to automatically detect and classify information in raw datasets (without pre-processing) are also relatively new alternatives in e-noses [148,155]. As noticed in Table 10, the sensor elements used in e-noses seems to be characterized specially for the use of resistive sensors based on MOXs. The justification for this is frequently supported by the broad gas/vapor sensitivity of these components, their low cost, and the need of simple drive circuits, amongst other characteristics such as fast and stable responses and long life [146,157,161,165,166]. Table 10. A few literature examples of the application of e-noses in the area of air, water, and food quality, health care, and medicine. The table shows the number of sensors in the array, the targeted analytes, and data processing algorithms employed in each report. Area Objective Sensor Elements Number Analytes Algorithms Ref. Indoor identification Commercial of low formaldehyde 5 Formaldehyde BP-ANN [167] Air quality and security MOX -Resistive concentrations Identification of binary mixture: Experimental DMMP (ppb level) MOX-Resistive 4 DMMP, Methanol PCA, ANN [160] and methanol (sub MOX-SAW ppm level) Detection of mixed Commercial Methane, Formaldehyde, PCA, LDA, indoor hazardous 6 [161] MOX-Resistive CO, Hydrogen BP-ANN gases Discrimination Commercial Propanol, Phenol, PCA, HCA, polluted from clean 6 [165] MOX-Resistive TFB, Benzene SVM Water quality water Ethanol, Acetone, PCA, ANN, Identification of Commercial 8 Toluene, Ammonia, RBF-ANN, [162] pollutants in water MOX-Resistive Ethylacetate BP-ANN Identification of Commercial Chloroform N/A PCA, LDA [166] pollutants in water MOX-Resistive Ammonia Off-flavors detection Ethylacetate, TCA, 4-EP, N/A in alcoholic beverage 18 Hexanol, Octenol, PCA, DFA [168] MOX-Resistive (wine, beer) Diacetyl, BD Food quality Rice quality Cotane, 2-pentylfuran, assessment—early Commercial PCA, LDA, 12 Dodecane, Toluene, [169] detection of fungal MOX-Resistive SVM, PLS Decane, Ethanol infection Fruit ripeness Commercial 7 Alkanes, Alcohols, Esters PCA, LDA [163] classification MOX-Resistive * Commercial Discrimination of Acetone, Formaldehyde, Healthcare/medicine MOX-Resistive pathogenic bacterial 6 Ammonia, Ethanol, PCA, CA [170] Experimental VOCs Ethylacetate, Acetic acid OINCs-Resistive Lung cancer and Experimental Acetone, Isoprene, renal failure 8 PCA, LDA [164] CbMs-Resistive Ammonia, Hydrothion diagnosis Lung cancer Experimental 4 Formaldehyde MVLR [171] diagnosis MOX-Resistive * The classification of fruit ripeness is assisted by a digital camera system, OINCs: organic-inorganic nanocomposites, N/A: data not available, DMMP: dimethyl methylphosphonate, TFB: 1,3,5-trifluorobenzene, TCA: 2,4,6-trichloroanisole, EP: ethylphenol, BD: 2,3- butanedione, BP: back-propagation, ANN: artificial neural network, PCA: principal component analysis, LDA: linear discriminant analysis, HCA: hierarchical cluster analysis, SVM: support vector machine, RBF: radial basis function, DFA: discriminant factor analysis, PLS: partial least squares, CA: cluster analysis, MVLR: multivariate linear regression. Nanomaterials 2021, 11, 552 26 of 33 Certainly, the application of machine learning for artificial olfactory systems has demonstrated further solving the selectivity issues of (nano)material-based gas/vapor sensors. In fact, the reports on e-noses show that partial selective commercial sensors can provide sufficient data to discriminate and classify samples [161–163,165–167,169]. However, despite e-noses technology apparently being resolved by the existing sensors, there is still a continuous demand in the field for more miniaturized sensor components with broader gas/vapor sensitivity, lower detection limits (at ppb-ppt level), low power consumption, low cost, and operation without consumables; features which are still not fully available in current commercial sensors [172]. These new characteristics would allow for even more generalized use of e-noses in the fields in which they have already shown their potential. Hence, new developments in materials and system integration as the primary components of the value chain are still timely to pave the way towards advanced artificial olfactory systems. 6. Outlook and Conclusions Nanomaterials based on metal oxides, conductive polymers, and carbon-based ma- terials are representative in VOCs sensing. Their optimization and enhanced properties generally involve the control of morphology, size, and composition. The most successful systems include structured morphologies, particularly hierarchical structures at the nano- metric scale with intentionally introduced tunable “decorative impurities” or well-defined interfaces forming bilayer structures. These groups of modified or functionalized structures, in which metal oxides are still the main protagonists either as host or guest elements, have proved improvements in VOCs sensing. Whilst the main improvements usually include better and faster responses, lower operating temperatures, or better stability, the advances in terms of selectivity are still blurred. First, because most reports in the literature concen- trate on studying a reduced number of VOCs, among them especially simple oxygenated hydrocarbons, and second because the selectivity is evaluated by the response comparison of single analytes often with incomparable concentrations among them. Overall, the litera- ture survey points out the need for investigating more in depth the material functionality to larger groups of VOCs as well as the convenience of probing other transducing principles rather than resistive for nanomaterial-based sensors. In terms of materials, the needs are directed overall to explore new hybrid material combinations that involve the formation of nanoscale interfaces between (i) inorganic and organic semiconductors (e.g., MOXs and POMs), (ii) inorganic and carbon-based materials (MOXs and graphene), and (iii) organic and carbon-based materials (e.g., POMs and graphene) to exploit the synergistic effects of these combinations. It is worth mentioning the availability of a broad group of emerging materials with less tradition in gas sensing, but with promising results to operate alone or in combination with traditional gas-sensitive materials. Within this group, the explo- ration of (i) perovskite and spinel oxides with multications variations; (ii) transition metal dichalcogenides (TMDs) and transition metal carbides/nitrides (MXenes); and (iii) nitrides, and phosphorene, silicene, or germanene are attractive. For both groups (i.e., traditional and emerging gas-sensitive materials), the new research efforts need to be invested in (i) controlling the load of constituents and their correlation with the VOC sensing patterns (information rarely found in the reports), (ii) finding dedicated materials for detecting compounds from the group of aliphatic hydrocarbons and carbonyl compounds, (iii) ex- ploring the possible multifunctional properties in the material for exploiting mixed output concepts (e.g., electric, optic, mechanic), and (iv) developing synthetic methods for a more realistic use of the materials in miniaturized integrated systems and their projection into the application market. The solution to those needs will pave the way to the selective detection of specific VOCs in the future and the generalized use of artificial olfactory systems adapted to the society demands. Author Contributions: Writing—original draft preparation, M.T. and S.V.; writing—review and editing, M.T., M.Š., L.V., and S.V.; funding acquisition, S.V. All authors have read and agreed to the published version of the manuscript. Nanomaterials 2021, 11, 552 27 of 33 Funding: This work has been funded by the Czech Science Foundation (GAČR), via Grant Nº 20-20123S, and the Spanish Ministry of Science and Innovation, via Grant PID2019-107697RB-C42 (AEI/FEDER, EU) and the Ramón y Cajal programme. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: We acknowledge the support of the Czech Science Foundation (GAČR), via Grant Nº 20-20123S, and the Spanish Ministry of Science and Innovation, via Grant PID2019-107697RB- C42 (AEI/FEDER, EU) and the Ramón y Cajal programme. Conflicts of Interest: The authors declare no conflict of interest. References 1. Barsan, N.; Weimar, U. 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