nanomaterials Article Graphene for the Building of Electroanalytical Enzyme-Based Biosensors. Application to the Inhibitory Detection of Emerging Pollutants Marta Bonet-San-Emeterio , Noelia Felipe Montiel and Manel del Valle * Sensors and Biosensors Group, Department of Chemistry, Universitat Autònoma de Barcelona, Edifici CN, 08193 Bellaterra, Spain; marta.bonet@uab.cat (M.B.-S.-E.); Noelia.FelipeMontiel@uantwerpen.be (N.F.M.) * Correspondence: manel.delvalle@uab.es; Tel.: +34-93-5813235 Abstract: Graphene and its derivates offer a wide range of possibilities in the electroanalysis field, mainly owing to their biocompatibility, low-cost, and easy tuning. This work reports the development of an enzymatic biosensor using reduced graphene oxide (RGO) as a key nanomaterial for the detection of contaminants of emerging concern (CECs). RGO was obtained from the electrochemical reduction of graphene oxide (GO), an intermediate previously synthesized in the laboratory by a wet chemistry top-down approach. The extensive characterization of this material was carried out to evaluate its proper inclusion in the biosensor arrangement. The results demonstrated the presence of GO or RGO and their correct integration on the sensor surface. The detection of CECs was carried out   by modifying the graphene platform with a laccase enzyme, turning the sensor into a more selective and sensitive device. Laccase was linked covalently to RGO using the remaining carboxylic groups Citation: Bonet-San-Emeterio, M.; of the reduction step and the carbodiimide reaction. After the calibration and characterization of Felipe Montiel, N.; del Valle, M. Graphene for the Building of the biosensor versus catechol, a standard laccase substrate, EDTA and benzoic acid were detected Electroanalytical Enzyme-Based satisfactorily as inhibiting agents of the enzyme catalysis obtaining inhibition constants for EDTA Biosensors. Application to the and benzoic acid of 25 and 17 mmol·L−1 , respectively, and a maximum inhibition percentage of the Inhibitory Detection of Emerging 25% for the EDTA and 60% for the benzoic acid. Pollutants. Nanomaterials 2021, 11, 2094. https://doi.org/10.3390/ Keywords: graphene; biosensing; inhibition; phenols; laccase enzyme nano11082094 Academic Editors: Daniela Iannazzo and Run Zhang 1. Introduction A chemical sensor is defined by IUPAC as a device that transforms chemical infor- Received: 18 June 2021 mation into an useful analytical signal [1]. The use of sensors has progressed over the Accepted: 13 August 2021 Published: 18 August 2021 years with the constant development of new technologies and devices. Researchers try to improve the selectivity of these designs with a wide range of techniques, such as the Publisher’s Note: MDPI stays neutral modification of the transducer material, the use of chemometric data processing, or the with regard to jurisdictional claims in inclusion of sample pretreatment. An example of the former is the concept of biosensors. published maps and institutional affil- A biosensor may be described as a sensor combined with a recognition element from the iations. biological world, which is the one responsible for conferring paramount selectivity. Actu- ally, this is what makes the difference from common sensors and grants their success. Since the discovery of graphene and its derived products, they have been gaining importance in the sensors and biosensors field [2]; one of the reasons for this is due to their synergistic effect when they are implemented as transducing materials [3,4]. It can be proven that Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. the usage of graphene is becoming widespread when you search how many publications This article is an open access article exist with “graphene” as a keyword, since 2004, when graphene was strictly discovered. distributed under the terms and Almost 150,000 publications can be listed in total; from these, 14,117 can be found using conditions of the Creative Commons the “graphene + sensor” keyword combination (data according to Scopus database). Attribution (CC BY) license (https:// Among others, graphene and its derivates have gained popularity in electroanalysis creativecommons.org/licenses/by/ due to their interesting inherent properties, such as biocompatibility, low-cost raw materials, 4.0/). and their easy tuning [3,4]. In this work, graphene is used not only as a key material to Nanomaterials 2021, 11, 2094. https://doi.org/10.3390/nano11082094 https://www.mdpi.com/journal/nanomaterials Nanomaterials 2021, 11, 2094 2 of 20 enhance the response of a bare electrochemical sensor, but also to show its properties as a linkage platform for enzyme immobilization [5–7]. Graphene allows to avoid sensor pretreatment or modifications that can be costly, time-consuming, and complex. One example is electrochemical grafting, where the carboxylic groups of an organic layer are attached to a conductive surface [8–10]. Graphene synthesis can be classified into two groups: top-down, and bottom-up synthesis. Furthermore, new strategies may be mentioned in the state of the art of the topic; for example, the cracking of multiwalled carbon nanotubes to create graphene ox- ide (GO)ribbons [11,12]. Bottom-up syntheses are the ones that start from simple carbon structures to create a pristine single-atom graphene layer with minimum defects and a sp2 configuration. Some examples of bottom-up approaches are the chemical vapor deposition (CVD) or the epitaxial growth [13,14]. Nevertheless, these synthetic pathways have impor- tant drawbacks, such as the complex adaptation to large scale production and the expensive and complex arrangements [15]. For this reason, top-down syntheses are popular when large amounts of materials are required. Hummers’ synthesis is the most frequently used method for the top-down production of graphene [16,17]. This wet synthesis uses strong oxidizing agents for exfoliating graphite to GO. As it is known, GO is a hydrophilic non- conductive material, which needs further synthetic steps to recover the pristine graphene properties, one of them the conductivity, an essential feature in electrochemistry [15,17]. Thus, a further step is needed, the GO conversion to the reduced graphene oxide (RGO). This step can be done electrochemically, chemically, mechanically, or even thermally. Even though, in the sensors study area, the most used reduction techniques are the electrochemi- cal and the chemical [18–20]. Both methods are interesting due to their reproducibility and easy applicability; however, the electrochemical approach presents several clear advan- tages over the chemical approach, e.g., low-cost, speed, environmentally friendliness and simplicity [20]. Therefore, in this work, RGO has been produced electrochemically. This method is based on the reduction of oxidized moieties present on the GO surface. As an additional advantage, the electrochemical reduction allows to easily and effectively control the oxidation degree of the RGO by simply adjusting the applied potential, which may be interesting for different final applications [21,22]. Graphene is not the unique strategic nanomaterial used to enhance the sensors re- sponse. For example, carbon dots and nanotubes have been also widely used as electrode modifiers. Carbon dots, also named graphene quantum dots or carbon nanodots, are a new trend in electroanalysis applications. Up to recent times, this material has been used mainly in photoluminescence-based determinations; but in current years it is growing strongly also in the electrochemical field [23–26]. On the other side, carbon nanotubes, considered as a rolled layer of graphene [27], have been extensively studied as a sensor modifier as well. Like other materials from the carbon family, carbon nanotubes ascribe catalytic effects and sensing improvements to the electrode behavior [28–30]. Enzymes such as laccase, tyrosinase, and glucose oxidase have been employed for a long time in bioanalytical chemistry, especially in biosensing. Their relevance is related to their ability to interact only with their matching substrates, conferring to the device one of the most desired characteristics: the specificity and the ability to distinguish the analyte in complex matrices. One of the most famous case is the glucose biosensor, already commercialized as a device for diabetic patients [31,32]. On the other side, laccase, a protein with a copper nucleus, has been extensively studied as a biorecognition element in various areas owing to its ability to oxidize a wide range of phenolic compounds, being catechol one of the standards for its activity analysis [33,34]. But not only there have been studies on the direct interaction with the substrates, but also on the interaction of the enzymes with their inhibitors. An interesting example is its use in detecting pollutants in products for human consumption, especially in drinking water. UNESCO reports as a pollutant any synthetic or naturally occurring chemical or microorganism that is not commonly monitored or regulated in the environment. This group includes pharmaceuticals, personal care products, pesticides, industrial additives, and industrial and household products [35]. Nanomaterials 2021, 11, 2094 3 of 20 New pollutants, also named emerging pollutants or contaminants of emerging concern (CECs), are appearing continuously. These chemicals have become an increasing problem over the years. Fortunately, at the same time, technology has been developed which has introduced new and improved methods to isolate and/or detect CECs in human consumption products. However, the existing commercial techniques are time-consuming and expensive, which encourages researchers to find new alternatives. Moreover, it must be considered that at this moment there are no reference methods in the European legislation to control the presence of these compounds in water. Nevertheless, different factors will force their consideration in the near future. The first reason is the conclusions from recently published articles, which determine that certain CECs, in special endocrine disruptors, interfere in the human and animal endocrine system [36–38]. The second reason is that these pollutants are not only found in water but also in soil and marine species such as shellfish and oily fish, increasing the contact of humans with these compounds with still unknown long-term effects. This work reports the preparation and characterization of a laccase-graphene biosensor for the detection of CECs (EDTA and benzoic acid) by the inhibition of the laccase enzyme. 2. Materials and Methods 2.1. Chemical and Reagents All reagents were of an analytical grade and all solutions were prepared using deion- ized water from Milli-Q system (Millipore, Billerica, MA, USA). Samples for electrochemical measurements were prepared on phosphate buffer in the presence of KCl as a background electrolyte, reagents purchased from Merck KGaA (Darmstadt, Germany). For the graphene synthesis, it was used as a graphite powder (50 µm, sulfuric acid (98%), sodium nitrate (99%), and potassium permanganate (99%), purchased from Merck KGaA (Darmstadt, Germany); hydrogen peroxide (30%) was purchased from Riedel-de Haën (Germany). Used ionic resins were strong cationic resin C100E and weak anionic resin A520E (Purolite) purchased from Merck KGaA. Ba(NO3 )2 was purchased at Acros Organics (Geel, Belgium). The material needed for the building of the electrode were: Resineco Epoxy Kit 125 resin supplied from Resineco green composites (Barcelona, Spain) and graphite powder (particle size < 50 µm) was received from BDH (BDH Laboratory Supplies, Poole, UK). For the biosensor, Laccase from Agaricus Bisporus, 7.2 U·mg−1 (EC number: 420-150-4), 1-ethyl-3-(3-dimethylaminopropyl carbodiimide (EDAC), and N-hydroxysulfosuccinimide (sulfo-NHS) (purchased from Merck KGaA) were used. Finally, catechol, acid benzoic, and EDTA were purchased from Merck KGaA. 2.2. Electrochemical Measurements Voltammetric measurements were performed on a AUTOLAB PGSTAT30 (Ecochemie, Netherlands) controlled by GPES software. Electrochemical Impedance Spectroscopy (EIS) experiments were performed on a Bas-Zahner IM6e (Kronach, Germany) potentiostat controlled by Thales software. For both electrochemical techniques a typical three electrode circuit was used. In the used assembly it can be found: the working electrode based on a graphite epoxy composite (electrode already optimized in our laboratory [39–41]), an Ag/AgCl (0.1 mol·L−1 KCl) electrode as a reference and a Pt electrode as a counter. All measurements were carried out at room temperature without stirring using as an electrolyte solution phosphate buffer 50 mmol·L−1 + KCl 100 mmol·L−1 at pH 7.4. Parameters for the cyclic voltammetry (CV) measurement were optimized for its use; at the end, a scan rate of 10 mV·s−1 and a step potential of 0.001 V was applied. The analysis of CV was performed with the GPES software, in particular using the inset tool to calculate the base line and the corresponding peak height of the recorded data. the base line and the corresponding peak height of the recorded data. 2.3. Sensor Characterization Morphological characterization of the raw materials was carried essentially by Trans- Nanomaterials 2021, 11, 2094 mission Electron Microscopy (TEM); TEM images were obtained using a JEOL 4JEM-2011 of 20 microscope. Moreover, X-ray photoelectron spectroscopy (XPS) analysis was carried out to describe the structure of the synthetized GO; a SPECS PHOIBOS 150 hemispherical 2.3. Sensor energy Characterization analyzer with monochromatic Cu–Kα radiation was used. CasaXPS software was used toMorphological acquire the fitted values. characterization of the raw materials was carried essentially by Trans- mission SurfaceElectron Microscopy roughness (TEM);was assessment TEMdone images bywere obtainedofusing processing a JEOL JEM-2011 topographic atomic force microscope. Moreover, X-ray photoelectron spectroscopy (XPS) analysis microscopy (AFM) images obtained using a Veeco Dimension 3100 AFM Microscope was carried out to describe the structure of (Bruker, Billerica, MA, USA). the synthetized GO; a SPECS PHOIBOS 150 hemispherical energy analyzer with monochromatic Cu–Kα radiation was used. CasaXPS software was used to acquire the fitted values. 2.4. Graphene Synthesis and Characterization Surface roughness assessment was done by processing of topographic atomic force A top-down microscopy synthesis (AFM) imagesbased on ausing obtained modified Hummers’ a Veeco method Dimension was used 3100 AFM for the syn- Microscope thesis of GO (Bruker, [16,18,42]. Billerica, In short, the protocol consists of the addition of 1.5 g of graphite MA, USA). and 4.5 g of NaNO3 to 35 mL of sulfuric acid (98%) cooled at 0 °C. Afterwards, 4.5 g of 2.4. Graphene Synthesis and Characterization KMnO4 in fine powder form was added in small portions over a period of 2 h, avoiding A top-down temperature synthesis from rising and based on a modified the mixture Hummers’ was stirred for an method was4used additional h. Theforresulting the synthesis of GO [16,18,42]. In short, the protocol consists of the addition of 1.5 g of graphite mixture was heated at 35 °C for 30 min and the oxidation process was completed by slowly and 4.5 g of NaNO3 to 35 mL of sulfuric acid (98%) cooled at 0 ◦ C. Afterwards, 4.5 g of adding 75 mL of deionized water. In this highly exothermic step, it is important to avoid KMnO4 in fine powder form was added in small portions over a period of 2 h, avoiding boiling. The reaction temperature finishes from rising and 15 themin afterwas mixture being kept stirred foratan 70additional °C. In order 4 h. to Theeliminate resultingunre- acted oxidizing mixture compounds, was heated at 35 Cthe ◦ forsolution was the 30 min and treated with process oxidation 300 mLwasof 10% hydrogen completed by per- oxide. Theadding slowly resulting 75 mLbrown paste (the of deionized densest water. GO) was In this highly washedstep, exothermic multiple times with it is important to de- avoid boiling. The reaction finishes 15 min after being kept at 70 ◦ C. In order to eliminate ionized water, recovering the solid by centrifugation at 6000 rpm. This process was re- unreacted peated oxidizing several compounds, times, till the solution was the pH approached treated with neutrality. With300a pHmLclose of 10% tohydrogen 7, the sample peroxide. The resulting brown paste (the densest GO) was washed of GO was treated with cation and anion exchange resins to eliminate the remainingmultiple times with salt deionized water, recovering the solid by centrifugation at 6000 rpm. This process was impurities. This was done just by placing in contact, under vigorous stirring, the sample repeated several times, till the pH approached neutrality. With a pH close to 7, the sample andofresin for 30 min. The separation was achieved by using a 50 µm sieve. Sulfate ions GO was treated with cation and anion exchange resins to eliminate the remaining salt were impurities. Thiswith eliminated was Ba(NO 3)2. Finally, the obtained slurry was dried in a vacuum oven done just by placing in contact, under vigorous stirring, the sample at 60 and°Cresin for 48 forh. 30 min. The separation was achieved by using a 50 µm sieve. Sulfate ions were eliminated with Ba(NO3 )2 . Finally, the obtained slurry was dried in a vacuum oven 60 ◦ C for Oxide 2.5.atGraphene 48 h. Modification and Immobilization of the Enzyme for the Biosensor Building The 2.5. graphite Graphene epoxy Oxide composite Modification (GEC) electrode, and Immobilization of the used Enzyme asfor base the transducer, had an av- Biosensor Building erage geometric area of 28 mm2 (Ø = 6 mm), as it is specified in the reference bibliography The graphite epoxy composite (GEC) electrode, used as base transducer, had an [39,40,43]; the composite average geometric area ofconductive 28 mm2 (Ø paste wasasadded = 6 mm), in an assembly it is specified formed in the reference by an elec- bibliogra- trical phyconnector [39,40,43];and a PVC tube. the composite Afterwards, conductive pastethe was conductive added in anmaterial assembly was hardened formed by an in an oven at 40 °C. electrical Finally, connector thea PVC and electrode was polished tube. Afterwards, thewith different conductive grainwas material sandpapers hardened inuntil a homogeneous ◦ an oven at 40and pristinethe C. Finally, surface waswas electrode obtained. polishedThewithpolishing step can different grain be applied sandpapers untilwhen a homogeneous needed and pristine to regenerate surface the surface andwas obtained. obtain againThethepolishing step canFigure bare platform. be applied when the 1 sketches needed to regenerate construction process. the surface and obtain again the bare platform. Figure 1 sketches the construction process. Figure 1. Scheme of the mechanic assembly for the construction of the base graphite-epoxy voltam- Figure 1. Scheme metric of the mechanic assembly for the construction of the base graphite-epoxy voltam- transducer. metric transducer. Once the conductive element of the proposed biosensor was ready, it was modified with GO. The selected technique to modify the electrode surface was drop casting, based on the physical adsorption of the GO to the base carbon material. Firstly, 40 µL of the GO dispersion (1 mg·mL−1 in deionized water) was deposited on the GEC electrode. It may be noted that the GO suspension must be sonicated previously for 1 h and centrifugated for Once the conductive element of the proposed biosensor was ready, it was modified with GO. The selected technique to modify the electrode surface was drop casting, based on the physical adsorption of the GO to the base carbon material. Firstly, 40 µL of the GO dispersion (1 mg·mL−1 in deionized water) was deposited on the GEC electrode. It may be Nanomaterials 2021, 11, 2094 noted that the GO suspension must be sonicated previously for 1 h and centrifugated 5 of 20 for 1 min at 800 rpm to use only the part of the dispersion that content the flakes with fewer layers. After this, the electrode was dried at 40 °C to promote physisorption. The RGO was 1 minobtained at 800 rpm bytotheuseelectrochemical only the part of the reduction dispersion of the that previous content the electrode. flakes withAsfewer it was first optimized layers. After [43], thethe this, reduction electrodewas was carried dried atout ◦ 40 viaC toCV; in particular, promote 10 cycles physisorption. Thewere RGOapplied in a potential was obtained by range the going from +1.90 electrochemical V to −2.30 reduction V at of the 0.1 V·selectrode. previous −1. As it was first optimized [43], the The laccase reduction enzyme was was carried out covalently via CV;to bonded in the particular, RGO via 10 cycles were applied carbodiimide (EDAC, N- in a potential range going from +1.90 V to − 2.30 V at 0.1 V · s −1 . (3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride) chemistry. The EDAC re- actionThe laccase enzyme was covalently bonded to the RGO via carbodiimide (EDAC, proved to be the most suitable strategy to immobilize the enzyme since it is able to N-(3-dimethylaminopropyl)-N0 -ethylcarbodiimide hydrochloride) chemistry. The EDAC react (through a free amino group) with the remaining carboxylic groups from RGO (that reaction proved to be the most suitable strategy to immobilize the enzyme since it is able were not(through to react reduceda in freethe reduction amino group)step) to obtain with the remainingthe carboxylic amide bond. Concretely, groups from RGO after the reduction, a solution of 10 mg·mL −1 of laccase in water was prepared, where 100 µL of (that were not reduced in the reduction step) to obtain the amide bond. Concretely, after EDAC solution the reduction, (1000 ppm) a solution of 10 mgand·mL50−µL 1 of of sulfo-NHS laccase in watersolution (1000 where was prepared, ppm) 100wereµL added. Once of EDACthe solution solution(1000was ppm) homogenized, and 50 µL the RGO electrode of sulfo-NHS solutionwas incubated (1000 ppm) were overnight added. in an Once the solution Eppendorf was homogenized, tube [44–47]. The last step thewas RGO toelectrode clean thewas incubated overnight non-bonded compounds in an from the Eppendorf surface of thetube [44–47]. The electrode last stepitwas by rinsing for to 1 hclean theworking in the non-bonded compounds phosphate from the buffer. surface As of the electrode Figure 2 depicts,by rinsing it for 1 coupling the EDAC h in the working agent phosphate is used tobuffer.activate the carboxylic As Figure 2 depicts, the EDAC coupling agent is used to activate the carboxylic groups groups from the RGO surface and form an intermediate complex that can react with the from the RGO surface and form an intermediate complex that can react with the primary primary amine groupamine fromgroup from the the laccase laccase chain. chain. In this Insulfo-NHS article, this article, wassulfo-NHS was additionally additionally used to used obtaintoaobtain a more more stable andstable and efficient efficient intermediate intermediate complex. complex. 2. Scheme Figure 2. Figure Schemeofof thethe laccase immobilization. laccase Step 1:Step immobilization. activation of the RGO 1: activation ofusing EDACusing the RGO (and NHS EDAC (and to increase NHS the efficiency), to increase Step 2: bonding the efficiency), Step 2:creation bonding between the between creation primary amine from the amine the primary enzymefrom and the en- the intermediate complex of the RGO surface. Finally, the catalytic reaction is described. zyme and the intermediate complex of the RGO surface. Finally, the catalytic reaction is described. 3. Results 3.3.1. Results Study of the Synthesized Graphene Oxide 3.1. Study of the Synthesized The obtained Graphene Oxide GO was characterized by TEM. Figure 3 shows some representative images Theresulting obtained from GOthiswastechnique. Herein,by characterized theTEM. main part of graphene Figure 3 showsflakes somewas thin representative and transparent enough to see the CuTEM grid through them. The presence of GO was images resulting from this technique. Herein, the main part of graphene flakes was thin corroborated when its characteristic wrinkles were identified, present, for example, in and transparent Figure enough 3A,B,D [48,49]. The to seepresence main the CuTEM of fewgrid through layers them. which of aggregates, The presence of GO was is supported corroborated because not only when theitstransparency characteristicis wrinkles achieved were identified, but also present, clear borders, is for alsoexample, noticed. in Fig- ure 3A,B,D [48,49]. The main presence of few layers of aggregates, Particularly, in Figure 3A,D can be seen clear and sharp edges, whereas in Figure 3B which is supported the because presencenotof aonly largethe transparency number of layersiscan achieved but also be deduced fromclear borders,grey the multiple is also noticed. shades at Par- the edge of ticularly, in the flake. Figure These 3A,D canhypotheses wereand be seen clear compared with the sharp edges, literature, whereas where other in Figure 3B the pres- researchers ence have of a large similarof number TEM images layers can [22,50,51]. be deduced Thefrom opaque the materials multiple in Figure grey 3C may shades at the edge be associated with the graphite residues coming from the starting synthesis of the flake. These hypotheses were compared with the literature, where other researchersreagents. have similar TEM images [22,50,51]. The opaque materials in Figure 3C may be associated with the graphite residues coming from the starting synthesis reagents. Nanomaterials 2021, 11, x FOR PEER REVIEW 6 of 19 Nanomaterials 2021, 11, 2094 6 of 20 Figure 3. TEM images of synthesized GO. The samples were suspended in ethanol absolute (≥99% and casted on a CuTEM grid. (A,C) show entire flakes of graphene oxide meanwhile in figures (B,D it is presented a specific part where graphene characteristics can be spotted. In addition to TEM, XPS analysis was carried out to study the obtained GO (Figur 4). Table 1 presents the dominating groups assigned to C 1s core level after its deconvolu tion. Different groups could be found in the material, corresponding to the wide range o oxygen functional groups from the synthetic pathway. To confirm this hypothesis, the oxygen and carbon ratio was calculated, being equal to 2.14. Normally, the approved ratio values range between 2.1 and 2.9 for GPO samples [16]. Figure 3. TEM images of synthesized GO. The samples were suspended in ethanol absolute (≥99%) Figure and 3. TEM casted on images a CuTEM ofgrid. synthesized GO. (A,C) show Theflakes entire samples were suspended of graphene oxide in ethanol absolute (B,D)(≥99%) Table 1. XPS-obtained distributions for the synthetized GO for themeanwhile in figures C 1s spectra. and casted on a CuTEM grid. (A,C) show entire flakes of graphene oxide it is presented a specific part where graphene characteristics can be spotted. meanwhile in figures (B,D) it is presented a Bond specific part where grapheneEnergy characteristics (eV) can be spotted. % Area (RSD) * In addition to TEM, XPS analysis was carried out to study the obtained GO (Figure 4). C=C 284.5 42.81 (1.30%) In 1addition Table presents totheTEM, XPS analysis dominating was carried groups assigned out to C 1s to level core studyafter theits obtained GO (Figure deconvolution. C-C 286.4 40.39 (2.1%) 4).Different groups Table 1 presents could the be found in thegroups dominating material, corresponding assigned to C 1stocore the wide levelrange afterofitsoxygen deconvolu- C-Ofrom the synthetic pathway. To 286.9 10.24 (8.3%) tion. Different groups could be found in the material, corresponding to the wideand functional groups confirm this hypothesis, the oxygen range of carbon ratio was oxygen functional C=Ocalculated, being equal to 2.14.288.2 Normally, the approved ratio 6.56 groups from the synthetic pathway. To confirm this values (9.5%) range hypothesis, the between * Relative 2.1 and 2.9 deviation. standard for GPO samples [16]. oxygen and carbon ratio was calculated, being equal to 2.14. Normally, the approved ratio values range between 2.1 and 2.9 for GPO samples [16]. Table 1. XPS-obtained distributions for the synthetized GO for the C 1s spectra. Bond Energy (eV) % Area (RSD) * C=C 284.5 42.81 (1.30%) C-C 286.4 40.39 (2.1%) C-O 286.9 10.24 (8.3%) C=O 288.2 6.56 (9.5%) * Relative standard deviation. Figure4.4. GO-Fitted Figure GO-FittedXPS XPSspectra spectraofofCC1s1sand anditsitsdeconvolution. deconvolution. Blue Blue line line corresponds corresponds to the fitted to the curve.curve. fitted Figure 4. GO-Fitted XPS spectra of C 1s and its deconvolution. Blue line corresponds to the fitted Nanomaterials 2021, 11, 2094 7 of 20 Table 1. XPS-obtained distributions for the synthetized GO for the C 1s spectra. Bond Energy (eV) % Area (RSD) * C=C 284.5 42.81 (1.30%) Nanomaterials 2021, 11, x FOR PEER REVIEW C-C 286.4 40.39 (2.1%) 7 of 19 C-O 286.9 10.24 (8.3%) C=O 288.2 6.56 (9.5%) * Relative standard deviation. Briefly, the resulting material of the modified Hummers’ method has similar proper- ties and characteristics Briefly, tomaterial the resulting comparable of theworks in the modified literaturemethod Hummers’ [17,20,22,52], reflecting has similar then properties the andgood performance. characteristics to comparable works in the literature [17,20,22,52], reflecting then the good performance. 3.2. Modification and Characterization of the Graphene Sensor 3.2. Modification and Characterization The optimization of the sensor of the Graphene assembly Sensor addressed. First, the optimal was carefully amountTheofoptimization GO solutionof tothe sensor assembly be deposited onto the was carefully electrode was addressed. studied. In First, thisthe optimal case, drop- amountwas casting of GO solution chosen as thetomodification be depositedmethod, onto thedue electrode was studied. to its rapid and easyInapplication. this case, drop- The castingofwas effect fourchosen as the different modification volumes (20, 30,method, 40, and due50 µL)to its of rapid and easy a 1 mg·mL −1 GOapplication. dispersionThe (in effect of four different volumes (20, 30, 40, and 50 µL) of a 1 mg · mL −1 GO dispersion (in deionized water) was studied. As mentioned above, GO is not conductive. For this reason, deionized after water) wasitstudied. the deposition, As mentioned was reduced to RGO toabove,analyze GOits is response not conductive. For this reason, using electrochemical after the deposition, it was reduced to RGO to analyze its response techniques. The analytical performance was studied by using a CV technique, by measur- using electrochemical techniques. ing the heightTheofanalytical performance the oxidative peak at was studied of a potential by 0.18 using Va(vs. CV Ag/AgCl technique,reference by measuring elec- the height trode) of the in the oxidative peakstandard electrochemical at a potential [Fe(CN)of 0.18 6]3-/4-V5 (vs. mmol·LAg/AgCl reference −1 (diluted electrode) in phosphate in the electrochemical standard [Fe(CN)The 3-/4- 5 mmol·L−1 (diluted in phosphate buffer buffer 50 mmol·L−1 at pH 7.4) as analyte. 6 ] goal was to find a volume that covers all the − 50 mmol·surface 1 L at pH electrode and7.4) did as notanalyte. saturateThe goal was As the response. to find can be a volume observed that covers 5A, in Figure all the the electrode surface and did not saturate the response. As can be obtained results, together with visual checking, demonstrate that smaller volumes did not observed in Figure 5A, the obtained cover the whole results, area together with visual of the electrode, but checking, at the same demonstrate time volumes thathigher smaller volumes than 40 µL did not cover the whole area of the electrode, but at the same time formed aggregates too large to be stacked in the surface for long periods of time, deterio- volumes higher than 40 µL formed aggregates too large to be stacked in the surface for rating any graphene benefits in the electrochemical responses. At the sight of these results, long periods of time, deteriorating 40 µL was theany graphene selected volumebenefits in the electrochemical for preparing responses. electrode. the graphene-modified At the sight of these results, 40 µL was the selected volume for preparing the graphene-modified electrode. Figure 5. Optimization of the critical parameters for the use of the sensor. In (A) the deposited volume of GO via drop Figure 5. Optimization of the critical parameters for the use of the sensor. In (A) the deposited volume of GO via drop casting (n = 3) (B,C) activation step of GO to RGO. (B) depiction of the 10 cycles reduction step; (C) shows the Nyquist plot casting (n = 3) (B,C) activation step of GO to RGO. (B) depiction of the 10 cycles reduction step; (C) shows the Nyquist plot after after the the reduction reduction of of GO. GO. The electrode surface was characterized also topographically via AFM, in order to The electrode surface was characterized also topographically via AFM, in order to visualize how the graphene was distributed onto the electrochemical platform. Figure 6A visualize how the graphene was distributed onto the electrochemical platform. Figure 6A shows the surface of a bare GEC electrode, and Figure 6B,C is a representative region shows the surface of a bare GEC electrode, and Figure 6B,C is a representative region of a of a GO modified GEC electrode, all images have a scanned surface of 10 × 10 µm. As GO modified GEC electrode, all images have a scanned surface of 10 × 10 µm. As it can be it can be seen in Figure 6A, the GEC electrode presents a crumpled surface with a root seen in Figure 6A, the GEC electrode presents a crumpled surface with a root mean square mean square roughness (RRMS ) of 73.5 nm; meanwhile, the surface modified with GO roughness has a RRMS(R ofRMS) of 73.5 123.3 nm.nm; meanwhile, Those the surface values, along modified with the with GOverify AFM images, has a the RRMSpresence of 123.3 nm. Those values, along with the AFM images, verify the presence of graphene of graphene flakes. Moreover, the 3D AFM image (Figure 6C) indicates the presence flakes. of Moreover, the 3D AFM image (Figure 6C) indicates the presence of multiple and multiple and separated GO flakes, some of them clearly with less height than the others. sepa- rated GO flakes, some of them clearly with less height than the others. Even in image 6C (marked with an arrow), it is possible to notice the GO wrinkles, also observed in TEM images (Figure 3). To sum up, AFM images demonstrated the correct stacking of GO, which lays in the surface increasing the rugosity, and consequently, the active sites of the electrode surface. Nanomaterials 2021, 11, 2094 8 of 20 Even in image 6C (marked with an arrow), it is possible to notice the GO wrinkles, also observed Nanomaterials 2021, 11, x FOR PEER REVIEW in TEM images (Figure 3). To sum up, AFM images demonstrated the correct 8 of 19 stacking of GO, which lays in the surface increasing the rugosity, and consequently, the active sites of the electrode surface. Figure 6. AFM images comparing (A) bare GEC electrode and (B) a GEC electrode modified with GO. (C) 3D AFM Figure 6. AFM of representation images comparing the GO-GEC (A) bare GEC electrode and (B) a GEC electrode modified with GO. (C) 3D AFM repre- electrode. sentation of the GO-GEC electrode. One essential step for the building of the biosensor is the correct reduction of GO to RGO; One essential in this work,step the for the building reduction electrochemical of the biosensorstep wasis the correctvia followed reduction CV, andofverified GO to RGO; in this work, the electrochemical reduction step was followed with EIS by the comparison of the different surfaces. In Figure 5B, the CV plot correspond- via CV, and verified with ing toEISthebyelectrochemical the comparisonreduction of the differentof thesurfaces. GO to RGO In Figure 5B, theSpecifically, is shown. CV plot correspond- the good ing to the electrochemical performance of the processreductionwas assessed of the GO tothe through RGO is shown. oxidation andSpecifically, the good reduction peaks that performance appear in the of the process range of −1.0 V was to −assessed 0.5 V (vs. through Ag/AgCl) the oxidation (see insertand reduction on Figure 5B). peaks Accordingthat appear in the range to the literature, theseof −1.0 peaks V are to −0.5 V (vs. related to Ag/AgCl) the reduction (see of insert oxygenon Figure groups5B). According present in the to the literature, deposited GO in these aqueous peaks are related medium to the pH and neutral reduction [20,53].ofAn oxygen groups present EIS technique was alsoinusedthe deposited to confirmGO thein aqueousof efficiency medium and neutral pH the electrochemical [20,53]. An reduction. TheEIS techniqueinwas differences the also used electrode surface to confirm of the the sensors efficiency canofgive the information electrochemical on the electronThe reduction. transfer and thus differences in demonstrates the electrode the increment surface in conductivity of the sensors of the RGO.onInthe can give information this case, the electron impedance transfer and thuscharacterization demonstrates experiments 3-/4- 5 mmol·L−1 between 0.5 MHz and 0.1 Hz at the incrementwere carried out inof[Fe(CN) in conductivity the RGO.6] In this case, the impedance characterization a sinusoidal voltage experiments were carried perturbation of 10 mV out in [Fe(CN) amplitude. 6]3-/4- 5 mmol·L−1 Figure between 5C shows 0.5 MHz theand Nyquist 0.1 Hz plot at for each modification phase; the bare GEC electrode, the electrode a sinusoidal voltage perturbation of 10 mV amplitude. Figure 5C shows the Nyquist plot when the GO material waseach for deposited and finally modification phase; when the last the bare GEC material, electrode, RGO,thewas obtained. electrode when Inthe theGOsame figure material is inserted was deposited the and Randles’ finallyequivalent when the last circuit, whichRGO, material, includes: (i) Rs, theInohmic was obtained. resistance the same figure ofinserted is the bulkthe solution; Randles’ (ii) equivalent Rct, the charge transfer circuit, whichresistance, includes: (i) which describes Rs, the ohmic the difficulty resistance of of the the electrochemical bulk solution; (ii) Rct, reaction; the chargeand (iii) transferCPE,resistance, a constantwhichphasedescribes element, the the difficulty component of that gives information on the double layer capacitance. Figure the electrochemical reaction; and (iii) CPE, a constant phase element, the component that 5C shows a smaller Rct for RGO-GEC gives electrode information theΩ) on(206 than for double GEC layer (1638 Ω) and capacitance. GO-GEC Figure (2256 Ω), 5C shows indicating a smaller Rctthat for the conductivity of graphene is regenerated with the electrochemical RGO-GEC electrode (206 Ω) than for GEC (1638 Ω) and GO-GEC (2256 Ω), indicating that method applied in this work. Additionally, the data demonstrate the better conductivity the conductivity of graphene is regenerated with the electrochemical method applied in of the RGO-modified electrode this work.than the non-modified Additionally, the dataone. demonstrate the better conductivity of the RGO-modi- These observations fied electrode than the non-modified allowed us one. to confirm that GO is deposited correctly onto the GECThese surface observations allowed us toit confirm using 40 µL; moreover, could bethat assumed GO is that the reduction deposited correctlymethod onto the is good enough to recover the conductive properties of graphene GEC surface using 40 µL; moreover, it could be assumed that the reduction method is and proceed to covalently immobilize good enoughthe to enzyme recover via the the EDAC reaction. conductive properties of graphene and proceed to covalently immobilize the enzyme 3.3. Characterization and via the EDAC Application reaction. of the Biosensor 3.3.1. Electrochemical Response of the Laccase Biosensor 3.3. Characterization and Application of the Biosensor Once the enzyme was immobilized, EIS was used to compare and contrast the biosen- 3.3.1. Electrochemical Response of the Laccase Biosensor sor against the already-evaluated platform. Once the enzyme The results of Tablewas immobilized, 2 compare Rs, RctEISandwas CPEused to for values compare andsignificant the most contrast the stepsbio- of sensor againstmodification. the electrode the already-evaluated platform. The most characteristic value in impedance results is the Rct; in The results this case, of to it is easy Table 2 compare see the Rs, Rct and little difference CPE values between for the most the electrodes, (206 Ω) and RGOsignificant steps the laccase-RGO (231 Ω) electrodes, which expose less resistance to the current flow than the of the electrode modification. The most characteristic value in impedance results is Rct; barein this one case,Ω). (1638 it is easy The to see effects theinlittle seen Rct difference between electrode of the laccase-RGO the electrodes, couldRGO (206 Ω) be explained and the laccase-RGO (231 Ω) electrodes, which expose less resistance to the current flow than the bare one (1638 Ω). The effects seen in Rct of the laccase-RGO electrode could be explained with the negative charge formed in the carboxylic groups of the amino acids, which are deprotonated at pH 7.4, generating a minimal increase in the electronic trans- ference between the electrode surface and the negative complex used as analyte. Nanomaterials 2021, 11, x FOR indicate changes that may be related to the double layer capacitance thickens the decrease. PEER REVIEW 9 of 19 Rs, as mentioned above, describes the bulk features of the solution and the diffusion char- acteristics; therefore, it may not be affected by the modifications on the electrode surface as canMoreover, be observed CPE,ina the table,value complex indicating the idealsignificance, with empirical situation. was adjusted. The results Nanomaterials 2021, 11, 2094 indicate changes that may be related to the double layer capacitance thickens the decrease. 9 of 20 TableRs, 2. Fitted values of circuit elements in the equivalent electric circuit for the different as mentioned above, describes the bulk features of the solution and the diffusion char- studied surfaces. acteristics; therefore, it may not be affected by the modifications on the electrode surface as can be observed in the table, indicating the ideal situation. with Electrode the negative charge formed Rs (Ω)in the carboxylicRct groups (Ω) of the aminoCPE acids, (F)which areTable deprotonated GEC at pH 7.4, generating 240.7 2. Fitted values of circuit a minimal increase ± 4 in the equivalent elements 1638 ± 62 in the electronic electric circuit7.9 for×the transference 10different −5 ± 0.7 ×studied 10−5 between the surfaces. electrode surface and the negative complex used as analyte. Moreover, CPE, RGO 252.0 ± 3 206 ± 142 4.2 × 10−5 ± 0.5 × 10−5 a complex value with empirical significance, was adjusted. The results indicate changes thatLaccase-RGO 252.0 Rs±(Ω)2capacitance231Rct± (Ω) 138 the decrease. 4.3 ×Rs, 10as −5 ± 0.4 × 10−5 may be Electrode related to the double layer thickens CPE (F) mentioned above, describesGECthe bulk features240.7of the± solution 4 1638 and the±diffusion 62 7.9 × 10 ± 0.7 characteristics; −5 × 10−5 therefore, Finally, the it may not beRGO resulting biosensor affected by the modifications was tested 252.0 ± 3 on the 206 versus electrode a phenolic ± 142 surface 4.2 compound, as can 0.5 × 10catechol, × 10be ±observed −5 −5in which is a Laccase-RGO the table,standard indicatingsubstrate of252.0 the laccase the ideal situation. ±2 enzyme. In Figure 7A 231 ± 138 4.3is× 10 shown −5 ± 0.4the × 10excellent −5 performance of the enzyme by the comparison of the responses of each sensor against Fitted values Table 2.concentrations different Finally, of circuit the resulting elements of catechol. biosensor in was Note thethat equivalent electric the versus tested anodic circuit apeak for the was phenolic useddifferent compound,as the studied analytical catechol, surfaces. which is a standard substrate of the laccase enzyme. In Figure 7A is shown the excellent signal in the following experiments since, contrary to what was expected, it is the part whereperformance the synergistic Electrode of theeffect enzyme of Rsall by the comparison (Ω)the system elements of the responses of each Rct (Ω) was best [54–56]. CPEIn sensor against (F)the calibration different curves, observed concentrations a linear trend of catechol. Note that the anodic peak was used as the analytical 240.7from ± 4 2 to 100 µmol·L 1638 ± 62for the biosensor and0.7 a×wider −5 range −1 GEC 7.9 × 10−5 ± signal in the following experiments since, contrary to what was expected, it is10the part −1 for the bare RGOand the RGO electrodes 252.0 ± 3 was observed, 206 ± 142 which goes 4.2 ×from 10−5 2 ± to 0.5 1500 × 10−µmol·L 5 where the synergistic effect of all the system elements was best [54–56]. − 5 In the calibration − 5 and fromLaccase-RGO 2 to 800 µmol·L−1 252.0 ±2 respectively. In the231 ± 138image is 4.3 same × 10 also noted ± 0.4 × 10 enhance- a clear curves, observed a linear trend from 2 to 100 µmol·L−1 for the biosensor and a wider range ment for in the the bare sensitivity and theof RGOthe electrodes system, increasing was observed, by more whichthan goesthreefold from 2 to the 1500initial µmol·L slope −1 value.andFinally, from 2the resulting to 800 µmol·L biosensor was tested −1 respectively. In theversus same imagea phenolic is alsocompound, noted a clear catechol, enhance- which mentisin Figure a7B standard thecompares substrate sensitivity GEC, of the of RGO,the laccase system, enzyme.by andincreasing Lac-RGO Inmore Figure responses 7A than is shown against threefold the thethesame excellent concentra- initial slope performance tion of of the enzyme by the comparison of the responses catechol. In the figure, as expected from the previous characterization, a current value. of each sensor against different enhancement concentrations Figurecan 7Bbe observed compares of catechol. GEC,when RGO,Note theand that GEC the anodic electrode Lac-RGO peak is was modified responses used against withas same the the analytical graphene.concentra-On the signal tion in of the following catechol. In theexperiments figure, as since, expected contrary from to the what previouswas expected, it characterization, is the a part current other side, some differences are appreciated when RGO and Lac-RGO responses are com- where the synergistic enhancement canusbeeffect observedof allwhen the system the GECelements electrode wasisbest [54–56]. modified withIn graphene. the calibration On the pared, which curves, observed leads a linear to assume trend are the fromappreciated laccase 2 to 100 µmol activity. − 1 ·L RGO for the Two of biosensor the and most characteristic a widerare range other changes side, some differences when and Lac-RGO responses com-are theare forpared,bare observed and the which RGO leads in electrodes us the oxidation to assume wasthe baselinewhich observed, laccase and the activity. reduction goesTwo fromof2 tothe peak. 1500mostµmolThese ·L−1 andfacts characteristic caused from by 2 tothe 800 catalysis·L−1ofrespectively. the anodicInreaction, sameand thebaseline image consequently noted there clearisenhancement a significant en- changes areµmol observed in the oxidation and is thealso reduction a peak. These facts are largement of the cathodic in the sensitivity pathway. of the system, increasing by more than threefold the initial slope value. caused by the catalysis of the anodic reaction, and consequently there is a significant en- largement of the cathodic pathway. FigureFigure 7.Figure (A)7.Calibration 7.(A) curves (A)Calibration Calibration for Lac-RGO curves curves electrode for Lac-RGO for Lac-RGO (∎),(∎), electrode ( RGO ), RGOelectrode electrode electrode(●)(•and (●) andGEC ) and GECelectrode GEC electrode (▲) electrode(▲) forthe (N)for the same same same con- con- centrations centrations concentrations of of catechol. of catechol. catechol. Currents Currents for the the oxidative oxidative foroxidative the peak peak (B) (B)(B) peak CV CV CV voltammogramsresponse voltammograms voltammograms responseofof response of the the the three three three electrodes electrodes electrodes in in in the thethe same samesame concentration concentration concentration of catechol. of catechol. of catechol. Measures Measures Measures in phosphate ininphosphate phosphate buffer buffer buffer + KCl KCl + KCl solution, solution, solution, pH pH pH 7.4. 7.4. 7.4. 3.3.2. 3.3.2. Analytical Figure Analytical Characterization 7B Characterization compares GEC, RGO, of of the and the Biosensorresponses against the same concentra- Lac-RGO Biosensor tion of Once catechol. In the figure, the biosensor as expected operation from the their was confirmed, previous characterization, analytical a current properties were studied Once the biosensor enhancement can be operation observed wasthe when confirmed, GEC their is electrode analytical modified properties with wereOn graphene. studied in detail. The properties studied were the linear range, limit of detection (LOD), limit of in detail. The properties thequantification other side, some studied differences (LOQ), were are the linearwhen appreciated repeatability, intermediate range, RGO limit precision, and ofLac-RGO detection selectivity, (LOD), limit of responses and lifetime.are quantification compared, which(LOQ), repeatability, leads us to assume intermediate precision, the laccase activity. Twoselectivity, of the mostand lifetime. characteristic changes are observed in the oxidation baseline and the reduction peak. These facts are caused by the catalysis of the anodic reaction, and consequently there is a significant enlargement of the cathodic pathway. Nanomaterials 2021, 11, 2094 10 of 20 3.3.2. Analytical Characterization of the Biosensor Once the biosensor operation was confirmed, their analytical properties were studied in detail. The properties studied were the linear range, limit of detection (LOD), limit of quantification (LOQ), repeatability, intermediate precision, selectivity, and lifetime. The linear range is generally defined as the area in the calibration curve where the signals are directly proportional to the concentration. This parameter is typically evaluated using the correlation coefficient. As can be observed in the inset of Figure 7A, the linear range found spanned from 2 to 100 µmol·L−1 . The correlation coefficient for this range was 0.992, indicating a satisfactory linearity. Regarding the LOD and LOQ, there are multiple ways to calculate them, such as the use of the calibration curve for the target analyte (see Equation (1), where m is the slope of the calibration curve and σ the standard deviation of the response at a 95% confidence level), the measure of signal/noise ratio or by the use of the blank standard deviation [57,58]. In this work, we used the first option, obtaining an LOD of 2.1 µmol·L−1 and a LOQ of 7.7 µmol·L−1 . 3.3 σ 10.0 σ LOD = LOQ = (1) m m The repeatability is defined for the European Medicines Agency (EMEA) as the “term that expresses the precision under the same operating conditions over a short interval of time” [59]. In this case, 10 consecutive measurements of a 50 µmol·L−1 catechol solution were performed, obtaining a relative standard deviation (RSD) value equal to 8%. Moreover, a second repeatability test was done to check the reliability of the biosensor during the day. For this purpose, 3 calibration experiments were repeated in 3 different time instants of the day (9 a.m., 1 p.m. and 4 p.m., see Figure 8A). The results showed an RSD for each point of the curve not larger than 8%. All repeatability tests were taken as positive, considering that a group of data is significantly equal when the RSD value is lower than 10%. In the EMEA guideline, is also defined the intermediate precision [59]. This term expresses “the within-laboratories variations, for example, when considering different days of analysis, different analyst or analysis in different equipment”. This test measured, across three different days, three different calibration curves, from three regenerated biosensors, checking at the same time two parameters, the difference between days and the difference of the electrode surface regeneration procedure (see Figure 8B). The findings indicated that there were no differences between the regenerated electrodes or the measurements on different days (RSD = 6%). The last studied parameter was the lifetime of the biosensor (or the intermediate precision of different days). To perform this test, an electrode was stored in the fridge at 4 ◦ C in a closed Eppendorf and a measurement was done every day (without regeneration of the surface) using a 50 µmol·L−1 catechol solution. The RSD results were 3% (n = 3). To visualize the selectivity of the biosensor, a Principal Component Analysis (PCA) (ex- ecuted in Matlab R2020a) was carried out to show the ability of the biosensor to distinguish between the different substances considered. In particular, six substances from different families were analyzed with a Lac-RGO-modified electrode. As can be seen in Figure 9, the score plot shows that substances without phenols in their molecular structure as ibuprofen and cadaverine have similar behaviors to the buffer response. This fact demonstrates then the correct performance against the specific substrates. indicated that there were no differences between the regenerated electrodes or the mea urements on different days (RSD = 6%). The last studied parameter was the lifetime of th biosensor (or the intermediate precision of different days). To perform this test, an ele trode was stored in the fridge at 4°C in a closed Eppendorf and a measurement was don Nanomaterials 2021, 11, 2094 11 of 20 every day (without regeneration of the surface) using a 50 µmol·L−1 catechol solution. Th RSD results were 3% (n = 3). Nanomaterials 2021, 11, x FOR PEER REVIEW 11 of 19 Figure 8. Study Figureof the Lac-RGO 8. Study analytical of the Lac-RGO biosensor analytical properties, biosensor properties, (A) calibration (A) calibration curves curves repeatability repeatability test; measurements test; measurements Figure 9, the score plot shows that substances without phenols in their molecular structure carried outcarried in three-time instantsinstants out in three-time and without regeneration and without regenerationofofthe theelectrode, (B) electrode, (B) intermediate intermediate precision precision as ibuprofen and cadaverine have similar behaviors to the buffer response. testThis test made made with factwith 3 3 calibration curves on 3 calibration curves on 3 different days different days with with renewedrenewed electrode electrode surface. surface. demonstrates then the correct performance against the specific substrates. To visualize the selectivity of the biosensor, a Principal Component Analysis (PCA (executed in Matlab R2020a) was carried out to show the ability of the biosensor to disti guish between the different substances considered. In particular, six substances from d ferent families were analyzed with a Lac-RGO-modified electrode. As can be seen Figure 9. PCA Score plot for the three principal components of 6 different molecules, acetaminophen, Figure 9. PCA Score plot for the three principal components of 6 different molecules, acetamino- cadaverine, catechin, 4-Ethylguaiacol, and ibuprofen. phen, cadaverine, catechin, 4-Ethylguaiacol, and ibuprofen. Table 3 was built to compare the results in the presented work with the others in the Table 3 was built to compare the results in the presented work with the others in the literature. From here, it can be concluded that our biosensor has a similar behavior to literature. From here, it can be concluded that our biosensor has a similar behavior to de- devices with similar properties, even if it is compared with electrodes that include more vices with similar properties, even if it is compared with electrodes that include more than than one catalytic material. one catalytic material. Table 3. Reported values for the analytical study of sensors and biosensors for the quantification of catechol. Method Electrode LOD (µM) Sensitivity (µA·µM−1) Linear Range (µM) Ref CV Lac/rGO/GEC 2.1 0.431 (15.39 µA µM−1 cm−2) 2–100 - CV Tyrosinase-polysaccharide 6 0.001 60–800 [60] CV Lac/PANI/GCE 1 2.07 0.23 3.2–19.6 [55] CV Lac/AP-rGOs/GCE 2 7 15.79 15–700 [61] CV Lac/AA/LuPc2 3 0.58 0.36 4–150 [62] CV AuNPs/ZnO/Al2O3/GO/chitosan/GCE 3.1 - 0.5–40 [63] CV FePP/MWCNT/Nafion/GCE 4 3.75 0.1867 65–1600 [64] Amp. 11 N-OMC/PVA/Lac/AuE 5 0.31 0.29 0.39–8.98 [65] Amp. Lac/CNTs–CS/GCE 6 0.66 - 1.2–30 [66] Amp Lac/PAP/MWCNTs/SPE 7 0.20 5.8 × 104 0.63–20.7 [47] Nanomaterials 2021, 11, 2094 12 of 20 Table 3. Reported values for the analytical study of sensors and biosensors for the quantification of catechol. Method Electrode LOD (µM) Sensitivity (µA·µM−1 ) Linear Range (µM) Ref CV Lac/rGO/GEC 2.1 0.431 (15.39 µA µM−1 cm−2 ) 2–100 - CV Tyrosinase-polysaccharide 6 0.001 60–800 [60] CV Lac/PANI/GCE 1 2.07 0.23 3.2–19.6 [55] CV Lac/AP-rGOs/GCE 2 7 15.79 15–700 [61] CV Lac/AA/LuPc2 3 0.58 0.36 4–150 [62] CV AuNPs/ZnO/Al2O3/GO/chitosan/GCE 3.1 - 0.5–40 [63] CV FePP/MWCNT/Nafion/GCE 4 3.75 0.1867 65–1600 [64] Amp. 11 N-OMC/PVA/Lac/AuE 5 0.31 0.29 0.39–8.98 [65] Amp. Lac/CNTs–CS/GCE 6 0.66 - 1.2–30 [66] Amp Lac/PAP/MWCNTs/SPE 7 0.20 5.8 × 104 0.63–20.7 [47] Amp Lac/Nafion/MWCNTs/SPE 0.45 1.6 × 104 1.4–65.4 [47] Amp Lac/MWCNTs/SPE 0.73 1.5 × 104 2.4–134.4 [47] DPV Lac/MWCNT-COOH/AuNPs/SDBS/PEDOT/GCE 8 12.26 0.012 12.0–94.1 [67] DPV RGO-MWCNT/GCE 1.8 0.07 5.5–540 [68] DPV MWCNT-PMG/GCE 9 5.8 1.3 µA µM−1 cm−2 30–1190 [69] DPV MWCNT/PMT/GCE 10 0.05 1.895 0.5–150 [70] 1 Laccase/Polyaniline/Glassy carbon electrode. 2 Lac/aminopyrene-reduced graphene oxide/GCE 3 Lac/arachidic acid/Lutetium bisphthalocyanine 4 Iron porphyrins/multi-walled carbon nan- otubes/Nafion/GCE 5 Nitrogen-doped ordered mesoporous carbon/Polyvinyl alcohol/Lac/Au electrode 6 Lac/Carbon nanotubes-chitosan/GCE 7 Lac/Polyazetidine prepolymer/MWCNTs/Screen printed electrode 8 Lac/MWCNT-COOH/AuNPs/sodium dodecyl benzene sulfonate/Poly(3,4-ethylenedioxythiophene)/GCE 9 MWCNT- poly(malachite green)/GCE 10 MWCNT/ poly (3-methylthiophene)/GCE 11 Amperometry. Nanomaterials 2021, 11, 2094 13 of 20 3.3.3. Kinetic and Inhibition Study The kinetics in enzymatic studies is one of the most interesting features as it is unique for each enzyme and substrate. For this reason, this issue is normally studied in detail; Leonor Michaelis and Maud Menten developed one of the most common models. This relates the enzyme substrate concentration ([S]) with the reaction rate (V). In this model, the enzyme rate depicts a saturation curve arriving to a steady state situation. At this point, the reaction rate achieves its maximum (Vmax ). This typical behavior is clearly observable in Figures 7A and 8B. From here, Michaelis and Menten deduced their equation (Equation (2)), where KM is the Michaelis–Menten’s constant, defined as the concentration of substrate needed to reach half of the maximum rate [71]. It is important to take into account that the KM and Vmax values will be unique for each pair of enzyme and substrate. To determine experimentally these parameters, the Lineweaver-Burk linearization is normally used (see its tailored version in Equation (3)) [72]. However, to assure that the Michaelis–Menten kinetics are valid for the studied system, first, the Hills coefficient (h, see Equation (4)) is commonly calculated. In this way, when h results are being close to 1, a Michaelis–Menten model can be assumed. In this work, the h estimated value from the catechol calibration curve was 0.99 ± 0.09, demonstrating that the Lac-RGO biosensor followed the already explained kinetics. The rate parameters KM and Vmax , obtained from Equation (4), were 2.62 ± 0.50 µmol·L−1 and 1.90 × 10−4 ± 0.13 × 10−4 A, respectively. [S] V0 = Vmax (2) [S] + KM 1 K 1 1 = M · + (3) V Vmax [S] Vmax Vmax ·([S]/KM )h V= (4) 1 + ([S]/KM )h As commented in the introduction section, there are common household products that have been used for a long period of time, that are now under scrutiny due to their possible effects on the environment and health. Examples are the emerging pollutants found in cosmetics, processed food, or pharmaceutical compounds. The inhibition study of the Lac-RGO biosensor was carried out using benzoic acid and EDTA, two examples of CECs. Benzoic acid is a colorless crystalline compound that is widely used in pharmaceutical products either as an active product ingredient (for its antifungal properties) or as an excipient (as a lubricant in tablets). Both in the food and cosmetics industry, it is used as a preservative [73,74]. On the other hand, EDTA is a colorless and soluble product used in various and distinct applications; for example, in the paper industry as a metal chelate agent or in the food and cleaning industries as a preservative [75,76]. Regarding the legislated limits for the herein target compounds, there is not an individual value. Nevertheless, the WHO made preliminary studies that recommend as maximum values 5 mg·kg−1 for the benzoic acid and 600 µg·L−1 for the EDTA (values for drinking-water) [77,78]. Any substance that in a particular context can decrease the kinetics of an enzyme will be considered as an enzymatic inhibitor. As mentioned before, the Michaelis–Menten constants (Equation (2)) are unique for a specific enzyme-substrate system, so the presence of inhibitors will lead to renaming the KM and Vmax to K’M and V’max . There are three modelled scenarios [23]. The first situation is competitive inhibition, where the inhibitor and the substrate compete for the same active site; thus, KM < K’M and Vmax = V’max . The second situation is named non-competitive inhibition; in this case, the inhibitor is not a direct competitor but is bound in an allosteric site that deactivates the enzyme. The changes in the constants are noted as KM = K’M and Vmax > V’max . Finally, the last scenario is the uncompetitive inhibition, where the inhibitor is bound, producing a Nanomaterials 2021, 11, 2094 14 of 20 bigger affinity between the substrate and the enzyme, leading to fewer active sites in the system, in that case KM > K’M and Vmax = V’max [72]. Experimentally, the changes can be elucidated comparing the Lineweaver-Burk lin- earization for different concentrations of inhibitor. In this sense, Figures 10 and 11 show the inhibition effects of different concentrations of benzoic acid and EDTA in a Lac-RGO elec- trode. Both figures show a typical behavior of the first scenario: a competitive inhibition. Figures 10A and 11A depict the calibration curves of catechol using a Lac-RGO electrode in the presence of different concentrations of benzoic acid and EDTA, respectively. Zone 1 highlighted the steady Vmax and zone 2 in the increase in KM with the inhibitor concentra- tion. In Table 4 there are complementary data used to corroborate the conclusions from Figures 10A and 11A; concretely, it includes the slopes from the K’M and V’max (calculated from the linear regressions on Figures 10A and 11A), when inhibitor concentrations are varied. In this table, positive slopes confirm the Michaelis constant increase (significant variation of K’M with slope values above uncertainties), and the constant trend for the maximum velocity (the uncertainties include the zero-slope value). This state is supported Nanomaterials 2021, 11, x FOR PEER REVIEW by Figure 10B,C and Figure 11B,C, where the calculated KM ’ and Vmax ’ values, and their 14 of 19 trends, are plotted. Figure 10.10. Figure Biosensors kinetic Biosensors study, kinetic study,(A) (A)Lineweaver–Burk linearizationfor Lineweaver–Burk linearization forfour fourbenzoic benzoic acid acid concentrations; concentrations; (B) (B) represen- repre- tation of the of sentation obtained K’M values the obtained for each K’M values inhibitor for each concentration; inhibitor andand concentration; (C)(C) V’max obtained V’max values obtained for values foreach eachinhibitor inhibitorconcen- tration. concentration. Figure 10. 2021, Nanomaterials Biosensors 11, 2094 kinetic study, (A) Lineweaver–Burk linearization for four benzoic acid concentrations; (B) represen- 15 of 20 tation of the obtained K’M values for each inhibitor concentration; and (C) V’max obtained values for each inhibitor concen- tration. Figure 11. 11. Figure Biosensors kinetic Biosensors kineticstudy, (A) Lineweaver–Burk study, (A) Lineweaver–Burk linearization linearization for four for four EDTAEDTA concentrations; concentrations; (B) representation (B) representation of of the theobtained obtained K’MM values for each values for eachinhibitor inhibitor concentration; concentration; andand (C) (C) V’ V’ max obtained obtained max values values for for each each inhibitor inhibitor concentration. concentration. Table 4. Variation of kinetic parameters vs. inhibitor concentration. Values of the linear trend K’M and V’max vs. Inhibitor concentration when kinetics of the laccase enzyme is evaluated. Inhibition values from calibration curves of catechol in different concentrations of acid benzoic and EDTA inhibitors. Slope of K’M Slope of V’max Substance KI (mmol·L−1 ) (µmol·L−1 ) vs. [I] (A·L·mmol−1 ) vs. [I] Benzoic acid 0.11 ± 0.04 3·10−6 ± 6·10−6 25 ± 19 EDTA 0.2 ± 0.1 0 ± 1·10−5 17 ± 9 Moreover, the inhibition constant (KI ) for each inhibitor. Table 4 shows the higher affinity of benzoic acid (25 mmol·L−1 ) to Laccase than EDTA (17 mmol·L−1 ). To calculate KI values, expression (5), corresponding to competitive inhibition, was used.   [I] K 0 M = KM · 1 + (5) KI On a further visualization of the inhibition effect, Figure 12B,D shows the percentage of the inhibition achieved for each pollutant in 800 µmol·L−1 of catechol. A bigger effect for benzoic acid, as expected from the previous comments, was fulfilled. In detail, a maximum ≈ 60% of inhibition was reached for the benzoic acid system in front of the ≈25% for EDTA. Voltammograms presented in Figure 12A,C shows the biosensor response in front of the catechol in the presence or absence of the inhibitor. In each case, the shape of the RGO voltammogram profile is recovered (Figure 7B) when benzoic acid and EDTA are in solution, indicating that laccase has lost its activity when they are present. To sum of the inhibition achieved for each pollutant in 800 µmol·L−1 of catechol. A bigger effect for benzoic acid, as expected from the previous comments, was fulfilled. In detail, a max- imum ≈ 60% of inhibition was reached for the benzoic acid system in front of the ≈25% for EDTA. Voltammograms presented in Figure 12A,C shows the biosensor response in front Nanomaterials 2021, 11, 2094 of the catechol in the presence or absence of the inhibitor. In each case, the shape 16ofofthe 20 RGO voltammogram profile is recovered (Figure 7B) when benzoic acid and EDTA are in solution, indicating that laccase has lost its activity when they are present. To sum up, the expected inhibition effect was verified, permitting the quantification of EDTA and benzoic up, the expected inhibition effect was verified, permitting the quantification of EDTA and acid in controlled samples. benzoic acid in controlled samples. Figure 12. Figure 12. Biosensor Biosensor inhibition inhibition study studyin inaaconstant constantconcentration concentrationofofcatechol catechol(800 (800µmol ·L−−11) when different concentrations µmol·L concentrations of (A,B) benzoic acid and (C,D) EDTA inhibitors are present in the sample. (A,C) depicts the CV obtained of (A,B) benzoic acid and (C,D) EDTA inhibitors are present in the sample. (A,C) depicts the CV obtained for the for the maximum maximum inhibition percentage and (B,D) the inhibition percentage profile when varying the inhibitor concentration. inhibition percentage and (B,D) the inhibition percentage profile when varying the inhibitor concentration. Percentage ofPercentage of inhibition calculated according to the relative maximum rate velocities, 100·(1 inhibition calculated according to the relative maximum rate velocities, 100·(1 − V’/V).− V’/V). 4. Conclusions 4. Conclusions The reported work has shown the capabilities of graphene as a nanomaterial to im- The reported work has shown the capabilities of graphene as a nanomaterial to prove improve andandfacilitate thethe facilitate development development of electrochemical of electrochemical biosensors, specifically biosensors, those specifically em- those ploying redox employing redoxenzymes. enzymes.TheTheadvantages advantages observed observedcan can be attributed to two be attributed factors: to two the factors: first one is the amelioration of the electrochemical transduction properties, the first one is the amelioration of the electrochemical transduction properties, thanks thanks to in- herent electrical characteristics of graphene. The second, the conjunction to inherent electrical characteristics of graphene. The second, the conjunction with the with the chemi- cal properties chemical of theof properties material, as it serves the material, as a linkage as it serves platform as a linkage for immobilization platform of bio- for immobilization of molecules such as enzymes. biomolecules such as enzymes. All this All this makes makes thisthis combination combination an an unbeatable unbeatable choice choice for for electrochemical electrochemical biosensing. biosensing. The case specified in here, the biosensing of catechol, has demonstrated The case specified in here, the biosensing of catechol, has demonstrated an improvement an improvement of sensitivity of sensitivity ofofca.ca.30%, 30%,which which in turn improves in turn improves the the direct determination direct of phenolic determination com- of phenolic pounds or opens the door to consider inhibition. In this inhibitory biosensing compounds or opens the door to consider inhibition. In this inhibitory biosensing applica- application, two different tion, chemicals, two different EDTA chemicals, EDTA andand benzoate, benzoate, have havebeen beenverified verifiedas as inhibitors inhibitors of the of the enzyme reaction, which may constitute the basis for a future analytical device or system to monitor CECs in water. Author Contributions: Conceptualization, M.d.V.; methodology, M.d.V.; Investigation, M.B.-S.-E. and N.F.M.; writing—original draft preparation, M.B.-S.-E.; writing—review and editing, M.d.V.; supervision, M.d.V.; funding acquisition, M.d.V. All authors have read and agreed to the published version of the manuscript. Nanomaterials 2021, 11, 2094 17 of 20 Funding: This research was funded by the Spanish Government (Ministerio de Economía y Compet- itividad) grant number PID2019-107102RB-C21. M.B.S. was funded by Secretaria d’Universitats i Recerca del Departament d’Empreses i Coneixement de la Generalitat de Catalunya and by European Social Fund, European Union through a FI fellowship. M.d.V. thanks the support from program ICREA Academia. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors thanks to Anna Herrera Chacón from the Universitat Autònoma de Barcelona for her collaboration with TEM images and to M.J Esplandiu from ICN2, for the AFM study. Conflicts of Interest: The authors declare no conflict of interest. References 1. Hulanicki, A.; Glab, S.; Ingman, F. Chemical sensors: Definitions and classification. Pure Appl. Chem. 1991, 63, 1247–1250. [CrossRef] 2. Novoselov, K.S.; Geim, A.K.; Morozov, S.V.; Jiang, D.; Zhang, Y.; Dubonos, S.V.; Grigorieva, I.V.; Firsov, A.A. Electric field effect in atomically thin carbon films. 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