Nanoscale Horizons This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. View Article Online COMMUNICATION View Journal | View Issue Revealing the improved stability of amorphous boron-nitride upon carbon doping† Open Access Article. Published on 20 December 2022. Downloaded on 4/14/2023 2:28:02 PM. Cite this: Nanoscale Horiz., 2023, 8, 361 Onurcan Kaya, ab Luigi Colombo,c Aleandro Antidormi,a Mario Lanza d and Received 4th November 2022, Stephan Roche*ae Accepted 14th December 2022 DOI: 10.1039/d2nh00520d rsc.li/nanoscale-horizons We report on a large improvement of the thermal stability and mechanical properties of amorphous boron-nitride upon carbon New concepts doping. By generating versatile force fields using first-principles The concept lies in the fact that novel amorphous structures combining and machine learning simulations, we investigate the structural boron, nitrogen and carbon stand as disruptive alternatives to silicon oxide in interconnect technologies of modern microelectronics. These properties of amorphous boron-nitride with varying contents of structures display ultralow dielectric coefficients (below 2) and superior carbon (from a few percent to 40 at%). We found that for 20 at% of technology performances and stability. In the present work we find that a carbon, the sp3/sp2 ratio reaches a maximum with a negligible suitable combination of C, B, and N allows determination of the upper graphitisation effect, resulting in an improvement of the thermal limit of such performances. Existing research in simulation in the study stability by up to 20% while the bulk Young’s modulus increases by of materials for interconnect technology do not rely on accurate artificial intelligence algorithms that we have used in this study. As a result, our about 30%. These results provide a guide to experimentalists and approach provides predictive modelling with results of strategic impor- engineers to further tailor the growth conditions of BN-based tance for the development of new materials technologies. Materials compounds as non-conductive diffusion barriers and ultralow science suffers from the complexity of understanding the properties of dielectric coefficient materials for a number of applications includ- highly disordered structures such as amorphous forms of boron-nitride. ing interconnect technology. Experimental characterization is very difficult, and in the absence of accurate simulation techniques, it is almost impossible to progress in an efficient way. Our methodology and results demonstrate that machine learning techniques enable in-depth characterisation of amorphous materials properties, and provide predictions that will further guide 1. Introduction engineers and experimentalists in the search of optimised growth conditions and integration. For the past 20 years, the semiconductor industry has been searching for dielectric materials with low diffusivity to reduce are being extensively studied as potential barrier materials for the size of back-end-of-logic (BEOL) interconnects and to BEOL interconnects.4 However, 2D materials synthesized via further promote the miniaturization of logic and memory scalable methods like chemical vapour deposition (CVD) and devices in electronic circuits.1,2 However, most dielectric mate- liquid-phase exfoliation (LPE) face significant integration chal- rials suffer from poor mechanical properties, insufficient lenges related to layer thickness control and to the presence of chemical stability, and not-enough low metal diffusivity, which grain boundaries.5–7 In hexagonal boron nitride (h-BN), the leads to reliability failures.3 Two-dimensional (2D) materials preferred insulating 2D material due to its high band gap (B6 eV), these native defects provoke prohibitive fluctuations a Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, of the electric field breakdown, which has led to the investiga- Campus UAB, Bellaterra, 08193, Barcelona, Spain. E-mail: stephan.roche@icn2.cat tion of amorphous boron nitride (a-BN) films. In fact, dielectric b School of Engineering, RMIT University, Melbourne, Victoria, 3001, Australia materials used by the semiconductor industry are amorphous c Department of Materials Science and Engineering, The University of Texas at with silicon dioxide being the most used and important of Dallas, Richardson, TX, 75080, USA them. The improved performance of amorphous materials is d Department of Material Science and Engineering, King Abdullah University of principally due to the lack of available dangling bonds and Science and Technology, Thuwal, 23955, Saudi Arabia e grain boundaries. Sputtered a-BN films with thickness ranging ICREA Institucio Catalana de Recerca i Estudis Avancats, 08010 Barcelona, Spain † Electronic supplementary information (ESI) available: Method section, support- from 12 nm to 800 nm have shown a dielectric constant from ing information and figures. Structural properties section, supporting figure. See 2.61 to 5.88.8,9 Atomically thin a-BN has been recently demon- DOI: https://doi.org/10.1039/d2nh00520d strated to possess ultralow dielectric constant (k o 2), low This journal is © The Royal Society of Chemistry 2023 Nanoscale Horiz., 2023, 8, 361–367 | 361 View Article Online Communication Nanoscale Horizons metal diffusivity and high mechanical robustness.10,11 This 2. Machine learning-based approach recent breakthrough follows years of efforts in the pursuit of for designing realistic disordered a- wafer-scale fabrication of atomically thin h-BN on various types of substrates, for potential use in the integration with graphene BN:C This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. and/or transition metal dichalcogenides for nanoelectronic To explore in-depth the atomistic/molecular features of devices and circuits.12–16 C-doped a-BN (a-BN:C), we employ atomistic calculations in Importantly, a-BN can be prepared at much lower tem- the framework of classical molecular dynamics, carried out peratures than h-BN, making its integration in a silicon within the LAMMPS package.25 To properly describe the intrin- device flow much easier while decreasing the energy and sically complex nature of such disordered material, a new force- thus cost of production. The lower temperature and the lack Open Access Article. Published on 20 December 2022. Downloaded on 4/14/2023 2:28:02 PM. field has been generated based on machine-learning methods. of grain boundaries also make a-BN a desirable material Such methods have already shown a superior capability in the for diffusion barriers and as a dielectric material adjacent to atomistic description of highly disordered materials, their TMDs and graphene,10 with a variety of potential applica- main advantage relying on the possibility to fit a sufficiently tions in spintronics,17 advanced data encryption technol- complex potential without a predefined functional form. The ogies18 and neuromorphic computing devices. 19–22 The resulting potential is capable of predicting the interatomic excellent properties of atomically thin a-BN as a dielectric forces with accuracy comparable to first-principle calculations material highlight the potential for many applications, but in a large number of configurations. In this work, we use the before we can take advantage of this material, a deeper GAP framework, which is based on the Gaussian process understanding of its microscopic structure and its relation- regression methodology, typically using the Smooth Overlap ship with the material performance (dielectric constant, of Atomic Positions (SOAP) kernel to describe the similarity thermal and chemical stability, electrical breakdown) is between local environments in materials. One advantage of crucially needed. To that end, in-depth studies on the effect kernel regression is that the flexibility of the functional form of the amorphous structure and alloying with carbon and naturally grows with the amount of training data. The general hydrogen are of great interest. Indeed, carbon and hydrogen approach is to fit a potential to an initial training set of total are typical contaminants in dielectrics grown at low tem- energies, forces and stresses calculated using a given DFT perature, e.g. SiO2 and Si3N4 synthesized with plasma electronic structure method (see for instance ref. 26 and 27). enhanced CVD. In that respect, theoretical structural studies Here, we use a GAP model that we have specifically devel- are advantageous over carrying out much more expensive oped for a-BN:C. Most structures in the reference database are and time consuming experimental approaches. snapshots from DFT-MD or GAP-MD simulations of the dis- From the theoretical and computational perspective, ato- ordered phases. The database used to train the potential is as mistic calculations represent a suitable tool to describe large as 10 000 different structures, each consisting of a system complex structures of concern, giving access to details at the of 2 to 200 atoms. The resulting potential has been validated by atomic and molecular level. However, the extremely disordered comparing the radial distribution functions of a-BN:C at 300 K nature of amorphous materials requires a computational and 5000 K using the DFT and GAP potential as shown in Fig. 1. approach able to capture the interatomic potentials in arbitrary GAP-MD simulations perfectly match with the DFT data at complex local environments, a challenge that can only be tackled with machine learning based methods.23,24 Specifically, classical molecular dynamics with the employment of force- fields derived using machine-learning and ab initio techniques constitute a powerful methodology to describe sufficiently large disordered material samples while keeping first-principles accuracy. Here, using classical molecular dynamics, we report that C-doped a-BN undergoes a strong modification of its thermal stability and mechanical properties as a function of carbon concentration. Remarkably, for a C concentration of 20 at%, a large enhancement of thermal stability is observed together with a corresponding increase of Young’s modulus. In order to properly describe the atomic interactions, we built a Gaussian Approximated Potential (GAP) trained on a suitably large data- set of atomistic structures obtained via ab initio calculations, which ensures the reliability and accuracy of the presented Fig. 1 Radial distribution functions of atomistic samples generated via the data. These results offer a practical strategy to tune the atomis- quenching from the melt protocol using ab initio (solid lines) and GAP tic properties of a-BN through the addition of carbon atoms molecular dynamics (dashed lines). The calculations are performed on with a subsequent strong improvement of the observed perfor- samples with 200 atoms and 5 at% C-content. Curves referring to different mance of a-BN for electronic applications. values of temperature (300 K and 5000 K) and density are shown. 362 | Nanoscale Horiz., 2023, 8, 361–367 This journal is © The Royal Society of Chemistry 2023 View Article Online Nanoscale Horizons Communication different temperatures and densities. The details of the gen- The short-range order in a-BN:C is in turn dominated by the first eration and validation steps of the potential are given in the nearest-neighbor distances contributing to the first peak located at an ESI.† average distance of B1.42 Å (the average B–N chemical bond length). Finally, we employed the generated potential to explore a wide As can be seen from Fig. 2c, which shows a closer image of the first This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. range of a-BN:C structures using classical molecular dynamics. peak, a clear broadening is observed increasing with doping concen- Specifically, generation of amorphous samples of a-BN:C with tration, suggesting that it is induced by the presence of carbon atoms. varying C concentrations was simulated by the quenching ‘‘from- Such modification is due to the formation of chemical bonds the-melt method’’ applied to randomly generated atomistic config- different from B–N (B–C, C–N, C–C), as can be verified from urations (containing 1000 atoms) with a cubic shape (L = Lx = Ly = Fig. S3a and b in the ESI,† where the chemical decomposition of Lz E 21 Å). The size of the systems was chosen to obtain a mass the first peak of the RDF in the two samples of a-BN:C with a C Open Access Article. Published on 20 December 2022. Downloaded on 4/14/2023 2:28:02 PM. density close to the experimental value (2.1 g cm 3) as observed by content of 0 and 40 at% is shown. The presence of carbon atoms in Hong et al.10 and to be large enough to contain a reliable sampling the compounded system determines the formation of new chemical of all the possible bonds and atomic structures as present in real bonds whose average length is slightly different from the B–N one. fabricated samples. Furthermore, the dimension of the systems Specifically, the B–C, C–N and C–C bond average lengths are found to allows for a satisfactory analysis without resulting in prohibitive be B1.52 Å, B1.3 Å and B1.40 Å, respectively. The overall peak is computational times. The details of the preparation of the samples consequently the convolution of peaks at different distances. as well as the size-dependence of the results are given in the The local character of a-BN:C can also be understood by Methods section in the ESI.† considering the average coordination number in the samples created as a function of doping concentration. The average coordination number is close to 3 as shown in Fig. S3c in the ESI,† denoting that most of the atoms are sp2-hybridized with 3. Structural and morphological some interesting feature induced by C incorporation. As can be analysis observed, the average coordination number increases with C- content and reaches a maximum value for a C-content between As a first result, we present the analysis of the structural 10 and 20 at%, whereas it decreases for larger concentrations. This properties of a-BN:C with different C concentrations. In this finding highlights the role played by small concentration of carbon work we considered a carbon concentration ranging from 0 to atoms in favoring a larger coordination number for all of the 40 at%. A subset of the samples generated with VMD28 is shown atoms in the sample. A more detailed insight can be obtained by schematically in Fig. 2a. In order to assess the degree of looking at the actual number of atoms with a given coordination crystallinity vs. disorder of the samples we computed the radial number. Fig. S3d in the ESI† shows the ratio of the number of sp2- distribution function g(r) (RDF) as a function of C concen- hybridized atoms (having coordination number 3) to the number tration (Fig. 2b). All the systems are characterized by clear and of sp3-hybridized atoms (having coordination number 4). Specifi- recognizable peaks for interatomic distances smaller than 5 Å; cally, the sp2 vs. sp3 ratios correspond to the data points with no peak can be identified for larger distances, denoting the coordination number 3. As can be seen, the number of 3- absence of long-range order in the samples independent of C coordinated atoms undergo a decrease by a factor of 2 with respect concentration. In this respect, the amorphous character of to the undoped sample when C is introduced. Differently stated, a-BN is not affected by C concentration. the presence of doping in the samples is responsible for a reduction of the sp2 vs. sp3 ratio with a minimum observed around 20 at% C-content. The density of samples is also increased slightly from 2.18 g cm 3 to 2.21 g cm 3 with the increasing sp3 content. A deeper understanding of the chemical composition of the samples can be obtained by looking at the number and the nature of chemical species involved in the bonds of the sample as a function of C-content. As shown in Fig. S3b in the ESI,† while the total number of B–N bonds monotonically decreases with increasing C content, the number of bonds involving C gradually increases. The most consistent chemical bonds invol- ving C is B–C, whose quantity equals that of B–N bonds for a C-content of 30 at%. A generally smaller concentration is found for N–C and C–C bonds, increasing with doping. 4. Structural stability analysis Fig. 2 (a) Ball-and-stick picture of a-BN:C; (b) radial distribution function (RDF) versus spatial distance between atoms; (c) zoom-in of the main In order to assess the stability of a-BN:C, we employed the peak. validated GAP potential to perform several MD calculations and This journal is © The Royal Society of Chemistry 2023 Nanoscale Horiz., 2023, 8, 361–367 | 363 View Article Online Communication Nanoscale Horizons extracted the atoms mean squared displacement (MSD) and diffusivity at different temperatures. The particle MSD and diffusivity, the latter being related to the behavior of MSD, namely D = limt-NMSD(t)/6t, are generally very useful quan- This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. tities and have been used to describe the ‘‘mobility’’ of the atoms in the solid and liquid phases of materials.29 In parti- cular, in the low-temperature regime, the MSD tends to a non- zero constant whereas the diffusivity is zero; for higher tem- peratures the material undergoes structural rearrangements of the atomic positions and a non-zero slope of MSD is hence Open Access Article. Published on 20 December 2022. Downloaded on 4/14/2023 2:28:02 PM. observed. From the comparison of diffusivity vs. temperature in our a-BN:C samples, an insight is obtained on the impact of C-content on the structural stability of a-BN:C. To this aim, we simulated a heating process of our samples consisting of a multi-step procedure. After equilibrating the samples at 300 K for 5 ps, the temperature was gradually modified by repeatedly performing the following steps: (1) the temperature is linearly increased with DT = 50 K in 10 ps; (2) the temperature is kept Fig. 3 Potential energy curves of a-BN:C samples observed during a fixed in the following 50 ps. The above iterative scheme is heating MD run. The dashed lines show the linear fit extracted from the repeated until a maximum temperature larger than 3000 K has low-temperature range. been reached. The length of the different time intervals is chosen to be sufficiently large to allow for a statistically mean- ingful calculation of the MSD and diffusivity. The simulations curves have been averaged over 5 runs for each value of were performed in a constant-temperature (NPT) ensemble C-content and the vertical bars denote the corresponding errors. with a Nosé-Hover thermostat for temperature control and a As can be observed, for low temperatures (up to B 1500 K) the time step of 0.25 fs. diffusivity is zero (within statistical accuracy) for all the samples, The structural stability of a-BN:C can be obtained by looking meaning that the atomic positions do not change over time and at the time evolution of the potential energy of the samples the material is structurally stable for all the C-doping values during a heating phase of the system. To this aim, the samples considered. For temperatures higher than 1500 K, thermal are slowly heated at a constant rate from ambient temperature energy induces structural rearrangements in the system leading (300 K) up to 3500 K in 50 ps and the potential energy is to atomic diffusion. The diffusivity consequently increases consequently monitored. No qualitative change was observed rapidly with temperature in a strongly non-linear fashion. In when adopting different heating rates. Fig. 3 shows the curves particular, we observe how the diffusivity in samples with a of the potential energy for our systems, with the values rigidly C-content of 10–20 at% is lower than that of samples with other shifted for the sake of visualization. In the plot the dashed doping levels up to T B 2500 K. This points to stronger curves represent the extrapolated lines at low temperature. structural stability of the corresponding samples upon an While the potential energy of the systems vs. temperature increase in temperature. The samples with a C-content of 10– linearly increases in the low-temperature regime, a strong devia- 20 at% are indeed characterized by a considerably smaller tion of the former is observed for sufficiently high thermal energy. diffusivity at generally high temperatures: a ratio of B3 is found Eventually the potential energy takes on the values corresponding when comparing the least and most stable systems at 2000 K. to the material liquid phase. The temperature at which the Our MD simulations confirm previous results30,31 concern- potential energy undergoes deviation from the linear trend ing the thermal stability of a-BN:C samples with increasing sp3 observed in the low-temperature regime can be taken as a para- fraction. As shown in Fig. 3b, the increase of C-content up to meter of stability of the sample: the larger its value, the more 20 at% enhances the sp3 fraction in a-BN:C samples which structurally stable the system. As can be observed from Fig. 3, such leads to improved thermal stability. However, larger C-content a temperature strongly increases for a small amount of C-doping, in a-BN:C samples leads to graphitization of samples and a reaching a maximum for a C-content between 10 and 20 at% in lower sp3 fraction. Since a-BN:C samples with 20 at% C content agreement with the sp2/sp3 increase discussed. A further increase contain the largest sp3 content among all samples with negli- of C-content leads to a reduction of the stability, so that a non- gible graphitisation content, a correlation between these struc- monotonic trend is found and identified to be driven by the ratio tural features and the thermal stability becomes evident. of sp2/sp3 defining the structural morphology of a-BN:C samples. The chemical composition of the sample also changes the thermal stability of the structure. 5. Mechanical properties As an alternative way to calculate thermal stability, we show the computed diffusivity for the different a-BN:C samples as a The mechanical properties of high-quality BN thin films have function of temperature as shown in Fig. S4 in the ESI.† The been studied experimentally recently and surprisingly have 364 | Nanoscale Horiz., 2023, 8, 361–367 This journal is © The Royal Society of Chemistry 2023 View Article Online Nanoscale Horizons Communication Table 1 Comparison of the obtained Young’s modulus with available literature Sample E [GPa] 865  7332 This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Nanoindentation measurement of monolayer h-BN Monolayer hBN using Tersoff potentials 627 64235–37 Nanoindentation measurement of cubic BN 600  10038 Amorphous BN w/C%20 – 2.21 g cm 3 (this work) 362 Amorphous BN – 2.18 g cm 3 (this work) 275 Amorphous BN – 1.45 g cm 3 106.234 Open Access Article. Published on 20 December 2022. Downloaded on 4/14/2023 2:28:02 PM. values to the literature values. Interestingly, the maximum Young’s modulus value obtained for the 20 at% C-doping level is only reduced by a factor of 3 compared to the nanoindenta- tion measurement of pure crystalline hBN samples32 and by a factor of 2 compared to sheets with high concentration of vacancies,35 which shows a very strong resilience of mechanical strength for such highly disordered forms of structurally and Fig. 4 Bulk Young’s modulus and shear modulus as a function of chemically modified a-BN compounds. Meanwhile, Liu et al.34 C-doping in simulated samples. obtained half of the value of Young’s modulus. However, the density of the sample used is very low suggesting the origin of such difference. Poisson’s ratio is also important to understand been found to be weakly dependent on the stack thickness. the mechanical behavior of materials. While Poisson’s ratio for Monolayer BN was found to have a Young’s modulus of 0.865  a-BN:C structures without any C content is calculated as 0.295, 0.073 TPa and a fracture strength of 70.5  5.5 GPa, with C-doped structures exhibit Poisson’s ratio between 0.266 and negligible variation upon increasing the number of layers.32 0.318 with no obvious trends with the level of C-content. The change of mechanical properties of amorphous BN is an Similar to what was observed for thermal stability, it has important question to answer given its relevance in the context been reported that39,40 an increased sp3 fraction can improve of integration in electronic devices. Young’s modulus. C-doping up to 20 at%, as shown earlier, Here, we study Young’s, bulk and shear moduli of a-BN:C, increases the sp3 fraction in a-BN:C samples leading to better where we perform additional short MD quenches from 300 K to mechanical properties and higher material density. However, a very low temperature, and finally a conjugate-gradient relaxa- larger C-content causes more sp2 content and C–C bonds which tion to minimize the forces on atoms; the cell vectors remained reduces the density and mechanical properties. Since they have fixed to keep the density unchanged. For each optimized the highest sp3 content, a-BN:C samples with 20 at% C-doping structure, we compute the full 6  6 matrix of elastic constants achieve the best mechanical properties and thermal C without imposing symmetry operations, and invert this robustness. matrix to obtain the compliance matrix S. Using these elastic constants, we investigate the mechanical stability and proper- 6. Conclusions ties of the a-BN:C structures at different C doping levels. In order to validate the mechanical stability of a-BN:C, we The recent growth of atomically thin amorphous boron nitride check if the elastic constants satisfy these conditions: C11– has provided a new opportunity for solving long standing C12 4 0, C11 4 0, C44 4 0 and C11 + 2C12 4 0. a-BN:C at all challenges of stable low dielectric constant materials. However, levels of C doping satisfies these conditions, hence it confirms despite the promising first results, it is crucial to fully under- the plausibility to form a-BN:C structures.33 stand the role of doping elements such as carbon and hydrogen After calculating the thermal stability of a-BN:C, Young’s, on the stability of the material as well as their effects on sp2 bulk and shear moduli are calculated using Voigt-Reuss-Hill versus sp3 bonding in these highly disordered structures. In this approximation, as shown in Fig. 4. The mechanical properties paper, thanks to machine learning approaches, we show that it of aBN:C samples are related to their sp2/sp3 ratios and is possible to extend the exploration range of complexity of density.34 All the moduli consistently show a non-monotonic such amorphous materials, and that carbon doping enables trend, analogous to thermal stability and density, with a max- further improvement of the sought after properties of a-BN. imum in correspondence of doping between 10 and 20 at%. Future studies should consider the impact of other atomic While the Young’s modulus of undoped a-BN:C is 274.85 GPa, entities such as hydrogen and combinations with carbon for it increased to 361.54 GPa with 20 at% C. Similarly, bulk and instance. The results of this study opens a new pathway for shear moduli are increased from 223.13 GPa and 106.02 GPa to improving the properties and stability of these amorphous 296.70 GPa and 139.38 GPa. At 40 at% C doping, Young’s and compounds for their use in applications such as nanoelectro- shear moduli become lower than that of undoped a-BN. Table 1 nics, spintronics, photovoltaics and/or neuromorphic comput- shows the comparison of our calculated Young’s modulus ing technologies. This journal is © The Royal Society of Chemistry 2023 Nanoscale Horiz., 2023, 8, 361–367 | 365 View Article Online Communication Nanoscale Horizons Author contributions 8 A. Prakash, V. Todi, K. B. Sundaram, L. Ross, G. Xu, M. French, P. Henry and S. W. King, ECS J. Solid State Sci. S. R. and L. C. conceived the research project. O. K. and A. A. Technol., 2014, 4, N3122. performed the simulations. O. K., S. R., L. C. and M. L. 9 Q. Abbas, H. Liang, J. Shi, Y. Chen, X. Xia, A. Ul Ahmad, This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. discussed and analyzed the simulation data. O. K., L. C. and J. Liu and G. Du, Mater. Lett., 2018, 227, 284–288. S. R. wrote the manuscript. All authors read and approved the 10 S. Hong, C.-S. Lee, M.-H. Lee, Y. Lee, K. Y. Ma, G. Kim, manuscript. S. I. Yoon, K. Ihm, K.-J. Kim, T. J. Shin, S. W. Kim, E.-C. Jeon, H. Jeon, J.-Y. Kim, H.-I. Lee, Z. Lee, A. Antidormi, S. Roche, Data availability statement M. Chhowalla, H.-J. Shin and H. S. Shin, Nature, 2020, 582, 511–514. Open Access Article. Published on 20 December 2022. Downloaded on 4/14/2023 2:28:02 PM. The generated GAP potential can be found at Zenodo (https:// 11 C.-M. Lin, C.-H. Hsu, W.-Y. Huang, V. Astié, P.-H. Cheng, zenodo.org) with DOI: 10.5281/zenodo.7500738. Y.-M. Lin, W.-S. Hu, S.-H. Chen, H.-Y. Lin, M.-Y. Li, B. Magyari-Kope, C.-M. Yang, J.-M. Decams, T.-L. Lee, Conflicts of interest D. Gui, H. Wang, W.-Y. Woon, P. Lin, J. Wu, J.-J. Lee, S. S. Liao and M. Cao, Adv. Mater. Technol., 2022, 2200022. There are no conflicts to declare. 12 S. Liu, R. He, L. Xue, J. Li, B. Liu and J. H. Edgar, Chem. Mater., 2018, 30, 6222–6225. 13 J. Lee, A. V. Ravichandran, J. Mohan, L. Cheng, A. T. Lucero, Acknowledgements H. Zhu, Z. Che, M. Catalano, M. J. Kim, R. M. Wallace, This project has been supported by Samsung Advanced Institute of A. Venugopal, W. Choi, L. Colombo and J. Kim, ACS Appl. Technology and is conducted under the REDI Program, a project Mater. Interfaces, 2020, 12, 36688–36694. that has received funding from the European Union’s Horizon 14 S. Sonde, A. Dolocan, N. Lu, C. Corbet, M. J. Kim, E. Tutuc, 2020 research and innovation programme under the Marie Skło- S. K. Banerjee and L. Colombo, 2D Mater., 2017, 4, 025052. dowska-Curie grant agreement no. 101034328. ICN2 acknowledges 15 A. F. Rigosi, H. M. Hill, N. R. Glavin, S. J. Pookpanratana, the Grant PCI2021-122092-2A funded by MCIN/AEI/10.13039/ Y. Yang, A. G. Boosalis, J. Hu, A. Rice, A. A. Allerman, 501100011033 and by the ‘‘European Union NextGenerationEU/ N. V. Nguyen, C. A. Hacker, R. E. Elmquist, A. R. H. Walker PRTR’’. 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