Issue

Vol. 17 (2025)

Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition

https://doi.org/10.1007/s40820-025-01741-0
Lin Shi
School of Microelectronics, Shanghai University, Shanghai 201800, People’s Republic of China
Jian Song
School of Microelectronics, Shanghai University, Shanghai 201800, People’s Republic of China
Yu Wang
School of Microelectronics, Shanghai University, Shanghai 201800, People’s Republic of China
Heng Fu
School of Microelectronics, Shanghai University, Shanghai 201800, People’s Republic of China
Kingsley Patrick‑Iwuanyanwu
Department of Biochemistry (Toxicological Unit), University of Port Harcourt, Port Harcourt, Nigeria
Lei Zhang
School of Microelectronics, Shanghai University, Shanghai 201800, People’s Republic of China
Charles H. Lawrie
Sino‑Swiss Institute of Advanced Technology (SSIAT), Shanghai University, Shanghai 201899, People’s Republic of China
Jianhua Zhang
School of Microelectronics, Shanghai University, Shanghai 201800, People’s Republic of China

Corresponding Author: Jianhua Zhang

jhzhang@oa.shu.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 246

Abstract

Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.


Highlights:
1 This paper reviews the fundamentals and research progress of carbon-based multivariable chemical sensors, with a particular focus on the classification and identification of multiple analytes.
2 Carbon-based multivariable chemical sensors consisting of carbon nanotubes/graphene as the sensing material and field effect transistors as the transducers are discussed in detail.
3 A comprehensive analysis of multivariable sensing mechanisms is presented and design criteria for carbon-based multivariable sensors are summarized.

Keywords

Carbon nanotubes Graphene Field-effect transistors Gas sensors Biosensors
Shi, Lin, Jian Song, Yu Wang, Heng Fu, Kingsley Patrick‑Iwuanyanwu, Lei Zhang, Charles H. Lawrie, and Jianhua Zhang. 2025. “Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition”. Nano-Micro Letters 17 (May):246. https://doi.org/10.1007/s40820-025-01741-0.
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References
  1. F.J. Zhang, G. Qu, E. Mohammadi, J.G. Mei, Y. Diao, Solution-processed nanoporous organic semiconductor thin films: toward health and environmental monitoring of volatile markers. Adv. Funct. Mater. 27(23), 1701117 (2017). https://doi.org/10.1002/adfm.201701117
  2. H. Yuan, S.A.A.A. Aljneibi, J. Yuan, Y. Wang, H. Liu et al., ZnO nanosheets abundant in oxygen vacancies derived from metal-organic frameworks for ppb-level gas sensing. Adv. Mater. 31(11), e1807161 (2019). https://doi.org/10.1002/adma.201807161
  3. K. Lee, I. Cho, M. Kang, J. Jeong, M. Choi et al., Ultra-low-power E-nose system based on multi-micro-LED-integrated, nanostructured gas sensors and deep learning. ACS Nano 17(1), 539–551 (2023). https://doi.org/10.1021/acsnano.2c09314
  4. N.S.S. Capman, X.V. Zhen, J.T. Nelson, V.R.S. Kumar Chaganti, R.C. Finc et al., Machine learning-based rapid detection of volatile organic compounds in a graphene electronic nose. ACS Nano 16(11), 19567–19583 (2022). https://doi.org/10.1021/acsnano.2c10240
  5. Z. Wu, Q. Ding, H. Wang, J. Ye, Y. Luo et al., A humidity-resistant, sensitive, and stretchable hydrogel-based oxygen sensor for wireless health and environmental monitoring. Adv. Funct. Mater. 34(6), 2308280 (2024). https://doi.org/10.1002/adfm.202308280
  6. W. Huang, Q. Ding, H. Wang, Z. Wu, Y. Luo et al., Design of stretchable and self-powered sensing device for portable and remote trace biomarkers detection. Nat. Commun. 14(1), 5221 (2023). https://doi.org/10.1038/s41467-023-40953-z
  7. Z.-Y. Gu, X.-P. Yan, Metal-organic framework MIL-101 for high-resolution gas-chromatographic separation of xylene isomers and ethylbenzene. Angew. Chem. Int. Ed. 49(8), 1477–1480 (2010). https://doi.org/10.1002/anie.200906560
  8. R.A. Potyrailo, Multivariable sensors for ubiquitous monitoring of gases in the era of Internet of Things and industrial Internet. Chem. Rev. 116(19), 11877–11923 (2016). https://doi.org/10.1021/acs.chemrev.6b00187
  9. A. Hakeem Anwer, M. Saadaoui, A.T. Mohamed, N. Ahmad, A. Benamor, State-of-the-Art advances and challenges in wearable gas sensors for emerging applications: innovations and future prospects. Chem. Eng. J. 502, 157899 (2024). https://doi.org/10.1016/j.cej.2024.157899
  10. A.K. Yetisen, J.L. Martinez-Hurtado, B. Ünal, A. Khademhosseini, H. Butt, Wearables in medicine. Adv. Mater. 30(33), 1706910 (2018). https://doi.org/10.1002/adma.201706910
  11. B. Zong, S. Wu, Y. Yang, Q. Li, T. Tao et al., Smart gas sensors: recent developments and future prospective. Nano-Micro Lett. 17(1), 54 (2024). https://doi.org/10.1007/s40820-024-01543-w
  12. S. Wang, M. Sun, Y. Zhang, H. Ji, J. Gao et al., Ultrasensitive antibiotic perceiving based on aptamer-functionalized ultraclean graphene field-effect transistor biosensor. Anal. Chem. 94(42), 14785–14793 (2022). https://doi.org/10.1021/acs.analchem.2c03732
  13. Y. Sun, X. Fan, Optical ring resonators for biochemical and chemical sensing. Anal. Bioanal. Chem. 399(1), 205–211 (2011). https://doi.org/10.1007/s00216-010-4237-z
  14. J. Shi, Y. Jiang, Z. Duan, J. Li, Z. Yuan et al., Designing an optical gas chamber with stepped structure for non-dispersive infrared methane gas sensor. Sens. Actuat. A Phys. 367, 115052 (2024). https://doi.org/10.1016/j.sna.2024.115052
  15. A. Mashhadian, R. Jian, S. Tian, S. Wu, G. Xiong, An overview of electrochemical sensors based on transition metal carbides and oxides: synthesis and applications. Micromachines 15(1), 42 (2023). https://doi.org/10.3390/mi15010042
  16. J. Lee, N.-J. Choi, H.-K. Lee, J. Kim, S.Y. Lim et al., Low power consumption solid electrochemical-type micro CO2 gas sensor. Sens. Actuat. B Chem. 248, 957–960 (2017). https://doi.org/10.1016/j.snb.2017.02.040
  17. W. Zhang, J. Zou, J. Sun, W. Wang, L. Shan et al., Fabrication and sensing performance of carrier-free catalytic combustion hydrogen sensors based on electrodeposition method. Int. J. Hydrogen Energy 61, 1356 (2024). https://doi.org/10.1016/j.ijhydene.2024.03.005
  18. B. Shen, T. Zhou, X. Liu, X. Qin, W. Li, Numerical simulation of catalytic methane combustion in Al2O3 directional nanotubes modified by Pt and Pd catalyst. Appl. Sci. 13(11), 6547 (2023). https://doi.org/10.3390/app13116547
  19. X. Chen, J. Hu, P. Chen, M. Yin, F. Meng et al., UV-light-assisted NO2 gas sensor based on WS2/PbS heterostructures with full recoverability and reliable anti-humidity ability. Sens. Actuat. B Chem. 339, 129902 (2021). https://doi.org/10.1016/j.snb.2021.129902
  20. Y. Zhang, Y. Li, Y. Jiang, Z. Duan, Z. Yuan et al., Synergistic effect of charge transfer and interlayer swelling in V2CTx/SnS2 driving ultrafast and highly sensitive NO2 detection at room temperature. Sens. Actuat. B Chem. 411, 135788 (2024). https://doi.org/10.1016/j.snb.2024.135788
  21. M. Kang, I. Cho, J. Park, J. Jeong, K. Lee et al., High accuracy real-time multi-gas identification by a batch-uniform gas sensor array and deep learning algorithm. ACS Sens. 7(2), 430–440 (2022). https://doi.org/10.1021/acssensors.1c01204
  22. J. Xu, X. Fan, K. Xu, K. Wu, H. Liao et al., Ultrasensitive chemiresistive gas sensors based on dual-mesoporous zinc stannate composites for room temperature rice quality monitoring. Nano-Micro Lett. 17(1), 115 (2025). https://doi.org/10.1007/s40820-024-01645-5
  23. X. Yao, Y. Zhang, W. Jin, Y. Hu, Y. Cui, Carbon nanotube field-effect transistor-based chemical and biological sensors. Sensors 21(3), 995 (2021). https://doi.org/10.3390/s21030995
  24. S. Lim, K.V. Nguyen, W.H. Lee, Enhancing sensitivity in gas detection: porous structures in organic field-effect transistor-based sensors. Sensors 24(9), 2862 (2024). https://doi.org/10.3390/s24092862
  25. X. Sun, Y. Shan, M. Jian, Z. Wang, A multichannel fluorescence isothermal amplification device with integrated Internet of medical things for rapid sensing of pathogens through deep learning. Anal. Chem. 95(41), 15146–15152 (2023). https://doi.org/10.1021/acs.analchem.3c02973
  26. H.Y. Chae, J. Cho, R. Purbia, C.S. Park, H. Kim et al., Environment-adaptable edge-computing gas-sensor device with analog-assisted continual learning scheme. IEEE Trans. Ind. Electron. 70(10), 10720–10729 (2023). https://doi.org/10.1109/TIE.2022.3220871
  27. Y. Zhuang, X. Wang, P. Lai, J. Li, L. Chen et al., Wireless flexible system for highly sensitive ammonia detection based on polyaniline/carbon nanotubes. Biosensors 14(4), 191 (2024). https://doi.org/10.3390/bios14040191
  28. G. Kaur, M. Tintelott, M. Suranglikar, A. Masurier, X.-T. Vu et al., Time-encoded electrical detection of trace RNA biomarker by integrating programmable molecular amplifier on chip. Biosens. Bioelectron. 257, 116311 (2024). https://doi.org/10.1016/j.bios.2024.116311
  29. W. Liu, X. Wang, B. Dong, Y. Liu, D. Wei, Enzymatic cascade reactors on carbon nanotube transistor detecting trace prostate cancer biomarker. Biosens. Bioelectron. 263, 116603 (2024). https://doi.org/10.1016/j.bios.2024.116603
  30. H.-Y. Liu, Z. Zhu, J. He, Y. Yang, Y. Liang et al., Mass production of carbon nanotube transistor biosensors for point-of-care tests. Nano Lett. 24(34), 10510–10518 (2024). https://doi.org/10.1021/acs.nanolett.4c02518
  31. H. Peng, J. Guo, G. Chen, W. Qin, Self-powered perovskite CH3NH3PbBr3 field effect transistor with fast response and high sensitivity in sensing. Mater. Today Adv. 12, 100185 (2021). https://doi.org/10.1016/j.mtadv.2021.100185
  32. R. Omar, M. Yuan, J. Wang, M. Sublaban, W. Saliba et al., Self-powered freestanding multifunctional microneedle-based extended gate device for personalized health monitoring. Sens. Actuators B Chem. 398, 134788 (2024). https://doi.org/10.1016/j.snb.2023.134788
  33. J. Li, Y. Jiang, A. Xu, F. Luo, C. Lin et al., ZnO/Au/GaN heterojunction-based self-powered photoelectrochemical Sensor for alpha-fetoprotein detection. Talanta 268(Pt 2), 125381 (2024). https://doi.org/10.1016/j.talanta.2023.125381
  34. Y. Geng, Y. Ren, X. Wang, J. Li, L. Portilla et al., Highly sensitive and selective H2S sensors with ultra-low power consumption based on flexible printed carbon-nanotube-thin-film-transistors. Sens. Actuat. B Chem. 360, 131633 (2022). https://doi.org/10.1016/j.snb.2022.131633
  35. W. Yue, Y. Guo, J.C. Lee, E. Ganbold, J.K. Wu et al., Advancements in passive wireless sensing systems in monitoring harsh environment and healthcare applications. Nano-Micro Lett. 17(1), 106 (2025). https://doi.org/10.1007/s40820-024-01599-8
  36. J. Xu, X. Luo, H. Chen, B. Guo, L. Jia et al., A self-powered biosensor with cascade amplification capability facilitates ultra-sensitive detection of microRNA biomarkers. Biosens. Bioelectron. 275, 117233 (2025). https://doi.org/10.1016/j.bios.2025.117233
  37. M. Sun, S. Wang, Y. Zhang, Z. Zhang, S. Wang et al., An ultrasensitive flexible biosensor enabled by high-performance graphene field-effect transistors with defect-free van der Waals contacts for breast cancer miRNA fast detection. Talanta 287, 127637 (2025). https://doi.org/10.1016/j.talanta.2025.127637
  38. J. Gao, C. Wang, C. Wang, Y. Chu, S. Wang et al., Poly-l-lysine-modified graphene field-effect transistor biosensors for ultrasensitive breast cancer miRNAs and SARS-CoV-2 RNA detection. Anal. Chem. 94(3), 1626–1636 (2022). https://doi.org/10.1021/acs.analchem.1c03786
  39. G. Maduraiveeran, M. Sasidharan, V. Ganesan, Electrochemical sensor and biosensor platforms based on advanced nanomaterials for biological and biomedical applications. Biosens. Bioelectron. 103, 113–129 (2018). https://doi.org/10.1016/j.bios.2017.12.031
  40. M. Huang, X. Ma, Z. Wu, J. Li, Y. Shi et al., Ammonium sensing patch with ultrawide linear range and eliminated interference for universal body fluids analysis. Nano-Micro Lett. 17(1), 92 (2024). https://doi.org/10.1007/s40820-024-01602-2
  41. R. Paolesse, S. Nardis, D. Monti, M. Stefanelli, C. Di Natale, Porphyrinoids for chemical sensor applications. Chem. Rev. 117(4), 2517–2583 (2017). https://doi.org/10.1021/acs.chemrev.6b00361
  42. J.W. Gardner, P.N. Bartlett, Electronic Noses: Principles and Applications (Oxford University Press, Oxford, 1999). https://doi.org/10.1093/oso/9780198559559.001.0001
  43. A. Rivadeneyra, J. Fernández-Salmerón, M. Agudo-Acemel, J.A. López-Villanueva, A.J. Palma et al., A printed capacitive–resistive double sensor for toluene and moisture sensing. Sens. Actuat. B Chem. 210, 542–549 (2015). https://doi.org/10.1016/j.snb.2015.01.036
  44. R.A. Potyrailo, W.G. Morris, Multianalyte chemical identification and quantitation using a single radio frequency identification sensor. Anal. Chem. 79, 45 (2007). https://doi.org/10.1021/ac061748o
  45. R. Paul Wali, P.R. Wilkinson, S.P. Eaimkhong, J. Hernando-Garcia, J.L. Sánchez-Rojas et al., Fourier transform mechanical spectroscopy of micro-fabricated electromechanical resonators: a novel, information-rich pulse method for sensor applications. Sens. Actuat. B Chem. 147(2), 508–516 (2010). https://doi.org/10.1016/j.snb.2010.03.086
  46. N.C. Speller, N. Siraj, B.P. Regmi, H. Marzoughi, C. Neal et al., Rational design of QCM-D virtual sensor arrays based on film thickness, viscoelasticity, and harmonics for vapor discrimination. Anal. Chem. 87(10), 5156–5166 (2015). https://doi.org/10.1021/ac5046824
  47. N.A. Joy, M.I. Nandasiri, P.H. Rogers, W. Jiang, T. Varga et al., Selective plasmonic gas sensing: H2, NO2, and CO spectral discrimination by a single Au-CeO2 nanocomposite film. Anal. Chem. 84(11), 5025–5034 (2012). https://doi.org/10.1021/ac3006846
  48. R.A. Potyrailo, M. Larsen, O. Riccobono, Detection of individual vapors and their mixtures using a selectivity-tunable three-dimensional network of plasmonic nanops. Angew. Chem. Int. Ed. 52(39), 10360–10364 (2013). https://doi.org/10.1002/anie.201305303
  49. H.J. Jang, H.A. Joung, A. Goncharov, A.G. Kanegusuku, C.W. Chan et al., Deep learning-based kinetic analysis in paper-based analytical cartridges integrated with field-effect transistors. ACS Nano 18(36), 24792–24802 (2024). https://doi.org/10.1021/acsnano.4c02897
  50. B. Wang, T.P. Huynh, W. Wu, N. Hayek, T.T. Do et al., A highly sensitive diketopyrrolopyrrole-based ambipolar transistor for selective detection and discrimination of xylene isomers. Adv. Mater. 28(21), 4012–4018 (2016). https://doi.org/10.1002/adma.201505641
  51. U.N. Thakur, R. Bhardwaj, A. Hazra, A multivariate computational approach with hybrid graphene oxide sensor array for partial fulfillment of breath acetone sensing. IEEE Sens. J. 22(21), 20207–20215 (2022). https://doi.org/10.1109/JSEN.2022.3208712
  52. K. Drozdowska, S. Rumyantsev, J. Smulko, A. Kwiatkowski, P. Sai et al., The effects of gas exposure on the graphene/AlGaN/GaN heterostructure under UV irradiation. Sens. Actuat. B Chem. 381, 133430 (2023). https://doi.org/10.1016/j.snb.2023.133430
  53. Y. Tong, Y. Liu, Y. Zhao, J. Thong, D.S.H. Chan et al., Selectivity of MoS2 gas sensors based on a time constant spectrum method. Sens. Actuat. A Phys. 255, 28–33 (2017). https://doi.org/10.1016/j.sna.2016.12.024
  54. J. Park, R. Rautela, N. Alzate-Carvajal, S. Scarfe, L. Scarfe et al., UV illumination as a method to improve the performance of gas sensors based on graphene field-effect transistors. ACS Sens. 6(12), 4417–4424 (2021). https://doi.org/10.1021/acssensors.1c01783
  55. M. Sun, Y. Zhang, S. Wang, S. Wang, L. Gao et al., Laser-induced graphene van der Waals contact-enabled high-performance 2D-materials-based field-effect transistor. Carbon 225, 119151 (2024). https://doi.org/10.1016/j.carbon.2024.119151
  56. S. Zhou, M. Xiao, F. Liu, J. He, Y. Lin et al., Sub-10 parts per billion detection of hydrogen with floating gate transistors built on semiconducting carbon nanotube film. Carbon 180, 41 (2021). https://doi.org/10.1016/j.carbon.2021.04.076
  57. R. Kour, S. Arya, S.-J. Young, V. Gupta, P. Bandhoria et al., Review: recent advances in carbon nanomaterials as electrochemical biosensors. J. Electrochem. Soc. 167(3), 037555 (2020). https://doi.org/10.1149/1945-7111/ab6bc4
  58. R. Eivazzadeh-Keihan, E. Bahojb Noruzi, E. Chidar, M. Jafari, F. Davoodi et al., Applications of carbon-based conductive nanomaterials in biosensors. Chem. Eng. J. 442, 136183 (2022). https://doi.org/10.1016/j.cej.2022.136183
  59. C. Wang, K. Xia, H. Wang, X. Liang, Z. Yin et al., Advanced carbon for flexible and wearable electronics. Adv. Mater. 31, 1801072 (2019). https://doi.org/10.1002/adma.201801072
  60. V. Pavlenko, S.H. Khosravi, S. Żółtowska, A.B. Haruna, M. Zahid et al., A comprehensive review of template-assisted porous carbons: Modern preparation methods and advanced applications. Mater. Sci. Eng. R Rep. 149, 100682 (2022). https://doi.org/10.1016/j.mser.2022.100682
  61. C. Gutiérrez-Sánchez, E. Lorenzo, Review: advances on oxidase enzymes modified nanoporous carbon and gold surfaces for bioelectrochemical applications. J. Electrochem. Soc. 169(2), 027513 (2022). https://doi.org/10.1149/1945-7111/ac527b
  62. A. Salvi, S. Kharbanda, P. Thakur, M. Shandilya, A. Thakur, Biomedical application of carbon quantum dots: a review. Carbon Trends 17, 100407 (2024). https://doi.org/10.1016/j.cartre.2024.100407
  63. C. Ding, A. Zhu, Y. Tian, Functional surface engineering of C-dots for fluorescent biosensing and in vivo bioimaging. Acc. Chem. Res. 47, 20 (2014). https://doi.org/10.1021/ar400023s
  64. J. Liu, J. Liu, Z. Li, L. Zhao, T. Wang et al., Carbon dots-modified hollow mesoporous photonic crystal materials for sensitivity- and selectivity-enhanced sensing of chloroform vapor. Nano-Micro Lett. 17(1), 96 (2024). https://doi.org/10.1007/s40820-024-01598-9
  65. S. Afreen, K. Muthoosamy, S. Manickam, U. Hashim, Functionalized fullerene (C60) as a potential nanomediator in the fabrication of highly sensitive biosensors. Biosens. Bioelectron. 63, 354–364 (2015). https://doi.org/10.1016/j.bios.2014.07.044
  66. X. Shen, J. Song, K. Kawakami, K. Ariga, Molecule-to-material-to-bio nanoarchitectonics with biomedical fullerene nanops. Materials 15(15), 5404 (2022). https://doi.org/10.3390/ma15155404
  67. M. Sun, S. Wang, Y. Liang, C. Wang, Y. Zhang et al., Flexible graphene field-effect transistors and their application in flexible biomedical sensing. Nano-Micro Lett. 17(1), 34 (2024). https://doi.org/10.1007/s40820-024-01534-x
  68. J. Shen, Y. Zhu, X. Yang, C. Li, Graphene quantum dots: emergent nanolights for bioimaging, sensors, catalysis and photovoltaic devices. Chem. Commun. 48(31), 3686–3699 (2012). https://doi.org/10.1039/c2cc00110a
  69. Y. Shao, J. Wang, H. Wu, J. Liu, I.A. Aksay et al., Graphene based electrochemical sensors and biosensors: a review. Electroanalysis 22(10), 1027–1036 (2010). https://doi.org/10.1002/elan.200900571
  70. K. Luo, H. Peng, B. Zhang, L. Chen, P. Zhang et al., Advances in carbon nanotube-based gas sensors: Exploring the path to the future. Coord. Chem. Rev. 518, 216049 (2024). https://doi.org/10.1016/j.ccr.2024.216049
  71. Z. Cohen, R.M. Williams, Single-walled carbon nanotubes as optical transducers for nanobiosensors in vivo. ACS Nano 18(52), 35164–35181 (2024). https://doi.org/10.1021/acsnano.4c13076
  72. V. Schroeder, S. Savagatrup, M. He, S. Lin, T.M. Swager, Carbon nanotube chemical sensors. Chem. Rev. 119(1), 599–663 (2019). https://doi.org/10.1021/acs.chemrev.8b00340
  73. A.K. Katiyar, J. Choi, J.H. Ahn, Recent advances in CMOS-compatible synthesis and integration of 2D materials. Nano Converg. 12(1), 11 (2025). https://doi.org/10.1186/s40580-025-00478-1
  74. X. Zang, Q. Zhou, J. Chang, Y. Liu, L. Lin, Graphene and carbon nanotube (CNT) in MEMS/NEMS applications. Microelectron. Eng. 132, 192–206 (2015). https://doi.org/10.1016/j.mee.2014.10.023
  75. H.S.P. Wong, Beyond the conventional transistor. IBM J. Res. Dev. 46(2.3), 133–168 (2002). https://doi.org/10.1147/rd.462.0133
  76. L. Lin, B. Deng, J. Sun, H. Peng, Z. Liu, Bridging the gap between reality and ideal in chemical vapor deposition growth of graphene. Chem. Rev. 118(18), 9281–9343 (2018). https://doi.org/10.1021/acs.chemrev.8b00325
  77. L. Zhang, J. Dong, F. Ding, Strategies, status, and challenges in wafer scale single crystalline two-dimensional materials synthesis. Chem. Rev. 121(11), 6321–6372 (2021). https://doi.org/10.1021/acs.chemrev.0c01191
  78. B. Sun, J. Pang, Q. Cheng, S. Zhang, Y. Li et al., Synthesis of wafer-scale graphene with chemical vapor deposition for electronic device applications. Adv. Mater. Technol. 6(7), 2000744 (2021). https://doi.org/10.1002/admt.202000744
  79. W. Gao, J. Kono, Science and applications of wafer-scale crystalline carbon nanotube films prepared through controlled vacuum filtration. R. Soc. Open Sci. 6(3), 181605 (2019). https://doi.org/10.1098/rsos.181605
  80. S. Kim, G. Singh, Y. Kim, J. Park, I. Jeon et al., Gate-tunable graphene/ZnO-based dual- channel field-effect transistor gas sensor for simultaneous detection of CO2 and NO2 gases. IEEE Sens. J. 23(20), 24214–24223 (2023). https://doi.org/10.1109/JSEN.2023.3309413
  81. S. Tripathy, A. Sett, S. Majumder, T.K. Bhattacharyya, Study of gate induced sensitivity amplification in carbon nanotube thin film transistor based ammonia sensor. Sens. Actuat. B Chem. 382, 133511 (2023). https://doi.org/10.1016/j.snb.2023.133511
  82. M. Zhao, Y. Tian, L. Yan, R. Liu, P. Chen et al., Unique modulation effects on the performance of graphene-based ammonia sensors via ultrathin bimetallic Au/Pt layers and gate voltages. Phys. Chem. Chem. Phys. 25(29), 19764–19772 (2023). https://doi.org/10.1039/D3CP01813J
  83. U. Weimar, W. Göpel, Chemical imaging: II. Trends in practical multiparameter sensor systems. Sens. Actuat. B Chem. 52(1–2), 143–161 (1998). https://doi.org/10.1016/S0925-4005(98)00268-8
  84. W. Göpel, Chemical imaging: I. Concepts and visions for electronic and bioelectronic noses1. Sens. Actuator B Chem. 52, 125 (1998). https://doi.org/10.1016/S0925-4005(98)00267-6
  85. A. Hierlemann, R. Gutierrez-Osuna, Higher-order chemical sensing. Chem. Rev. 108(2), 563–613 (2008). https://doi.org/10.1021/cr068116m
  86. R.A. Potyrailo, C. Surman, N. Nagraj, A. Burns, Materials and transducers toward selective wireless gas sensing. Chem. Rev. 111(11), 7315–7354 (2011). https://doi.org/10.1021/cr2000477
  87. R. Potyrailo, R.R. Naik, Bionanomaterials and bioinspired nanostructures for selective vapor sensing. Annu. Rev. Mater. Res. 43, 307–334 (2013). https://doi.org/10.1146/annurev-matsci-071312-121710
  88. K.S. Novoselov, A.K. Geim, S.V. Morozov, D. Jiang, Y. Zhang et al., Electric field effect in atomically thin carbon films. Science 306(5696), 666–669 (2004). https://doi.org/10.1126/science.1102896
  89. S. Park, Y. Suh, H. Floresca, M. Kim, HRTEM observation of mechanically exfoliated graphene from natural graphite and HOPG. ECS Trans. 19(5), 81–85 (2009). https://doi.org/10.1149/1.3119530
  90. S.H. Dave, C. Gong, A.W. Robertson, J.H. Warner, J.C. Grossman, Chemistry and structure of graphene oxide via direct imaging. ACS Nano 10(8), 7515–7522 (2016). https://doi.org/10.1021/acsnano.6b02391
  91. C. Gómez-Navarro, J.C. Meyer, R.S. Sundaram, A. Chuvilin, S. Kurasch et al., Atomic structure of reduced graphene oxide. Nano Lett. 10(4), 1144–1148 (2010). https://doi.org/10.1021/nl9031617
  92. S.Z.N. Demon, A.I. Kamisan, N. Abdullah, S.A.M. Noor, O.K. Khim et al., Graphene-based materials in gas sensor applications: a review. Sens. Mater. 32(2), 759 (2020). https://doi.org/10.18494/sam.2020.2492
  93. I.H. Kim, T. Yun, J.E. Kim, H. Yu, S.P. Sasikala et al., Mussel-inspired defect engineering of graphene liquid crystalline fibers for synergistic enhancement of mechanical strength and electrical conductivity. Adv. Mater. (2018). https://doi.org/10.1002/adma.201803267
  94. A. Xavier Mendes, S. Moraes Silva, C.D. O’Connell, S. Duchi, A.F. Quigley et al., Enhanced electroactivity, mechanical properties, and printability through the addition of graphene oxide to photo-cross-linkable gelatin methacryloyl hydrogel. ACS Biomater. Sci. Eng. 7(6), 2279–2295 (2021). https://doi.org/10.1021/acsbiomaterials.0c01734
  95. J. Siddiqui, M. Jamal Deen, Biodegradable asparagine-graphene oxide free chlorine sensors fabricated using solution-based processing. Analyst 147(16), 3643–3651 (2022). https://doi.org/10.1039/d2an00533f
  96. Y. Ravi Kumar, K. Deshmukh, T. Kovářík, S.K. Khadheer Pasha, A systematic review on 2D materials for volatile organic compound sensing. Coord. Chem. Rev. 461, 214502 (2022). https://doi.org/10.1016/j.ccr.2022.214502
  97. M. Nami, M. Taheri, I.A. Deen, M. Packirisamy, M.J. Deen, Nanomaterials in chemiresistive and potentiometric gas sensors for intelligent food packaging. TrAC Trends. Anal. Chem. 174, 117664 (2024). https://doi.org/10.1016/j.trac.2024.117664
  98. N.R. Tanguy, M. Arjmand, N. Yan, Nanocomposite of nitrogen-doped graphene/polyaniline for enhanced ammonia gas detection. Adv. Mater. Interfaces 6(16), 1900552 (2019). https://doi.org/10.1002/admi.201900552
  99. X. Tang, M. Debliquy, D. Lahem, Y. Yan, J.-P. Raskin, A review on functionalized graphene sensors for detection of ammonia. Sensors 21(4), 1443 (2021). https://doi.org/10.3390/s21041443
  100. M. Sun, C. Zhang, S. Lu, S. Mahmood, J. Wang et al., Recent advances in graphene field-effect transistor toward biological detection. Adv. Funct. Mater. 34(44), 2405471 (2024). https://doi.org/10.1002/adfm.202405471
  101. F. Schedin, A.K. Geim, S.V. Morozov, E.W. Hill, P. Blake et al., Detection of individual gas molecules adsorbed on graphene. Nat. Mater. 6(9), 652–655 (2007). https://doi.org/10.1038/nmat1967
  102. N. Srikanth, A.C. Kumar, History of carbon nanotubes, in Handbook of Carbon Nanotubes. ed. by J. Abraham, S. Thomas, N. Kalarikkal (Springer International Publishing, Cham, 2020), pp.1–22. https://doi.org/10.1007/978-3-319-70614-6_51-1
  103. J. Prasek, J. Drbohlavova, J. Chomoucka, J. Hubalek, O. Jasek et al., Methods for carbon nanotubes synthesis: review. J. Mater. Chem. 21(40), 15872 (2011). https://doi.org/10.1039/c1jm12254a
  104. M. Asaftei, M. Lucidi, C. Cirtoaje, A.-M. Holban, C.A. Charitidis et al., Fighting bacterial pathogens with carbon nanotubes: focused review of recent progress. RSC Adv. 13(29), 19682–19694 (2023). https://doi.org/10.1039/d3ra01745a
  105. V. Georgakilas, J.A. Perman, J. Tucek, R. Zboril, Broad family of carbon nanoallotropes: classification, chemistry, and applications of fullerenes, carbon dots, nanotubes, graphene, nanodiamonds, and combined superstructures. Chem. Rev. 115(11), 4744–4822 (2015). https://doi.org/10.1021/cr500304f
  106. M.F. Yu, O. Lourie, M.J. Dyer, K. Moloni, T.F. Kelly et al., Strength and breaking mechanism of multiwalled carbon nanotubes under tensile load. Science 287(5453), 637–640 (2000). https://doi.org/10.1126/science.287.5453.637
  107. E. Muchuweni, E.T. Mombeshora, Enhanced thermoelectric performance by single-walled carbon nanotube composites for thermoelectric generators: a review. Appl. Surf. Sci. Adv. 13, 100379 (2023). https://doi.org/10.1016/j.apsadv.2023.100379
  108. T. Dürkop, S.A. Getty, E. Cobas, M.S. Fuhrer, Extraordinary mobility in semiconducting carbon nanotubes. Nano Lett. 4(1), 35–39 (2004). https://doi.org/10.1021/nl034841q
  109. J.M. Herrera-Ramirez, R. Perez-Bustamante, A. Aguilar-Elguezabal, Chapter two - an overview of the synthesis, characterization, and applications of carbon nanotubes. in Carbon-Based Nanofillers and Their Rubber Nanocomposites, edited by S. Yaragalla, R. Mishra, S. Thomas, N. Kalarikkal, and H. J. Maria. Elsevier, (2019), pp. 47–75. https://doi.org/10.1016/B978-0-12-813248-7.00002-X
  110. J.W.G. Wilder, L.C. Venema, A.G. Rinzler, R.E. Smalley, C. Dekker, Electronic structure of atomically resolved carbon nanotubes. Nature 391(6662), 59–62 (1998). https://doi.org/10.1038/34139
  111. J. Kong, N.R. Franklin, C. Zhou, M.G. Chapline, S. Peng et al., Nanotube molecular wires as chemical sensors. Science 287(5453), 622–625 (2000). https://doi.org/10.1126/science.287.5453.622
  112. Z.F. Ren, Z.P. Huang, J.W. Xu, J.H. Wang, P. Bush et al., Synthesis of large arrays of well-aligned carbon nanotubes on glass. Science 282(5391), 1105–1107 (1998). https://doi.org/10.1126/science.282.5391.1105
  113. W. Zhou, Z. Han, J. Wang, Y. Zhang, Z. Jin et al., Copper catalyzing growth of single-walled carbon nanotubes on substrates. Nano Lett. 6(12), 2987–2990 (2006). https://doi.org/10.1021/nl061871v
  114. L. Shi, L. Gong, Y. Wang, Y. Li, Y. Zhang, Extended-gate structure for carbon-based field effect transistor type formaldehyde gas sensor. Sens. Actuat. B Chem. 400, 134944 (2024). https://doi.org/10.1016/j.snb.2023.134944
  115. L.S. Liyanage, H. Lee, N. Patil, S. Park, S. Mitra et al., Wafer-scale fabrication and characterization of thin-film transistors with polythiophene-sorted semiconducting carbon nanotube networks. ACS Nano 6(1), 451–458 (2012). https://doi.org/10.1021/nn203771u
  116. D. Kiriya, K. Chen, H. Ota, Y. Lin, P. Zhao et al., Design of surfactant-substrate interactions for roll-to-roll assembly of carbon nanotubes for thin-film transistors. J. Am. Chem. Soc. 136(31), 11188–11194 (2014). https://doi.org/10.1021/ja506315j
  117. G. Yuan, W. Liu, X. Huang, Z. Wan, C. Wang et al., Stacking transfer of wafer-scale graphene-based van der Waals superlattices. Nat. Commun. 14(1), 5457 (2023). https://doi.org/10.1038/s41467-023-41296-5
  118. B. Jiang, S.W. Wang, J.Y. Sun, Z.F. Liu, Controllable synthesis of wafer-scale graphene films: challenges, status, and perspectives. Small 17(37), 2008017 (2021). https://doi.org/10.1002/smll.202008017
  119. M.D. Bishop, G. Hills, T. Srimani, C. Lau, D. Murphy et al., Fabrication of carbon nanotube field-effect transistors in commercial silicon manufacturing facilities. Nat. Electron. 3(8), 492–501 (2020). https://doi.org/10.1038/s41928-020-0419-7
  120. F. Liu, M. Xiao, Y. Ning, S. Zhou, J. He et al., Toward practical gas sensing with rapid recovery semiconducting carbon nanotube film sensors. Sci. China Inf. Sci. 65(6), 162402 (2022). https://doi.org/10.1007/s11432-021-3286-3
  121. M. Xiao, S. Liang, J. Han, D. Zhong, J. Liu et al., Batch fabrication of ultrasensitive carbon nanotube hydrogen sensors with sub-ppm detection limit. ACS Sens. 3(4), 749–756 (2018). https://doi.org/10.1021/acssensors.8b00006
  122. Y. Liang, M. Xiao, D. Wu, Y. Lin, L. Liu et al., Wafer-scale uniform carbon nanotube transistors for ultrasensitive and label-free detection of disease biomarkers. ACS Nano 14(7), 8866–8874 (2020). https://doi.org/10.1021/acsnano.0c03523
  123. S. Zhan, H. Zuo, B. Liu, W. Xu, J. Cao et al., Wafer-scale field-effect transistor-type sensor using a carbon nanotube film as a channel for ppb-level hydrogen sulfide detection. ACS Sens. 8(8), 3060–3067 (2023). https://doi.org/10.1021/acssensors.3c00653
  124. Y. Sun, Y. Zhang, Wafer-scale floating-gate field effect transistor sensor built on carbon nanotubes film for Ppb-level NO2 detection. Chem. Eng. J. 473, 145480 (2023). https://doi.org/10.1016/j.cej.2023.145480
  125. M. Soikkeli, A. Murros, A. Rantala, O. Txoperena, O.P. Kilpi et al., Wafer-scale graphene field-effect transistor biosensor arrays with monolithic CMOS readout. ACS Appl. Electron. Mater. 5(9), 4925–4932 (2023). https://doi.org/10.1021/acsaelm.3c00706
  126. C. Wang, J. Zhang, C. Zhou, Macroelectronic integrated circuits using high-performance separated carbon nanotube thin-film transistors. ACS Nano 4(12), 7123–7132 (2010). https://doi.org/10.1021/nn1021378
  127. L. Liu, J. Han, L. Xu, J. Zhou, C. Zhao et al., Aligned, high-density semiconducting carbon nanotube arrays for high-performance electronics. Science 368(6493), 850–856 (2020). https://doi.org/10.1126/science.aba5980
  128. U. Weimar, K.D. Schierbaum, W. Göpel, R. Kowalkowski, Pattern recognition methods for gas mixture analysis: application to sensor arrays based upon SnO2. Sens. Actuat. B Chem. 1(1–6), 93–96 (1990). https://doi.org/10.1016/0925-4005(90)80179-4
  129. W. Huang, R.C. Hayward, Orthogonal ambipolar semiconductors with inherently multi-dimensional responses for the discriminative sensing of chemical vapors. ACS Appl. Mater. Interfaces 10, 33353 (2018). https://doi.org/10.1021/acsami.8b10789
  130. L. Shi, P. Tang, J. Hu, Y. Zhang, A strategy for multigas identification using multielectrical parameters extracted from a single carbon-based field-effect transistor sensor. ACS Sens. 9(6), 3126–3136 (2024). https://doi.org/10.1021/acssensors.4c00357
  131. T. Hayasaka, A. Lin, V.C. Copa, L.P. Lopez Jr., R.A. Loberternos et al., An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol. Microsyst. Nanoeng. 6, 50 (2020). https://doi.org/10.1038/s41378-020-0161-3
  132. O.G. Agbonlahor, M. Muruganathan, A. Banerjee, H. Mizuta, Machine learning identification of atmospheric gases by mapping the graphene-molecule van der waals complex bonding evolution. Sens. Actuat. B Chem. 380, 133383 (2023). https://doi.org/10.1016/j.snb.2023.133383
  133. B. Liu, Y. Huang, K.W. Kam, W.-F. Cheung, N. Zhao et al., Functionalized graphene-based chemiresistive electronic nose for discrimination of disease-related volatile organic compounds. Biosens. Bioelectron. X 1, 100016 (2019). https://doi.org/10.1016/j.biosx.2019.100016
  134. J. Sun, M. Muruganathan, H. Mizuta, Room temperature detection of individual molecular physisorption using suspended bilayer graphene. Sci. Adv. 2(4), e1501518 (2016). https://doi.org/10.1126/sciadv.1501518
  135. W. Fu, L. Feng, G. Panaitov, D. Kireev, D. Mayer et al., Biosensing near the neutrality point of graphene. Sci. Adv. 3(10), e1701247 (2017). https://doi.org/10.1126/sciadv.1701247
  136. O.G. Agbonlahor, M. Muruganathan, T. Imamura, H. Mizuta, Adsorbed molecules as interchangeable dopants and scatterers with a van der waals bonding memory in graphene sensors. ACS Sens. 5(7), 2003–2009 (2020). https://doi.org/10.1021/acssensors.0c00403
  137. I. Lundström, S. Shivaraman, C. Svensson, L. Lundkvist, A hydrogen−sensitive MOS field−effect transistor. Appl. Phys. Lett. 26(2), 55–57 (1975). https://doi.org/10.1063/1.88053
  138. F. Liao, C. Chen, V. Subramanian, Organic TFTs as gas sensors for electronic nose applications. Sens. Actuat. B Chem. 107(2), 849–855 (2005). https://doi.org/10.1016/j.snb.2004.12.026
  139. L. Torsi, A. Dodabalapur, L. Sabbatini, P.G. Zambonin, Multi-parameter gas sensors based on organic thin-film-transistors. Sens. Actuator B Chem. 67, 312 (2000). https://doi.org/10.1016/S0925-4005(00)00541-4
  140. M. Josowicz, J. Janata, Suspended gate field effect transistors modified with polypyrrole as alcohol sensor. Anal. Chem. 58(3), 514–517 (1986). https://doi.org/10.1021/ac00294a003
  141. C.-H. Kim, I.-T. Cho, J.-M. Shin, K.-B. Choi, J.-K. Lee et al., A new gas sensor based on MOSFET having a horizontal floating-gate. IEEE Electron Device Lett. 35(2), 265–267 (2014). https://doi.org/10.1109/LED.2013.2294722
  142. I. Lundström, H. Sundgren, F. Winquist, M. Eriksson, C. Krantz-Rülcker et al., Twenty-five years of field effect gas sensor research in Linköping. Sens. Actuat. B Chem. 121(1), 247–262 (2007). https://doi.org/10.1016/j.snb.2006.09.046
  143. Z. Shen, W. Huang, L. Li, H. Li, J. Huang et al., Research progress of organic field-effect transistor based chemical sensors. Small 19, 2302406 (2023). https://doi.org/10.1002/smll.202302406
  144. S.A. Dr, M.F. Dr, B. Souharce, H.T. Dr, U.S.P. Dr, Organic semiconductors for solution-processable field-effect transistors (OFETs). Angew. Chem. Int. Ed. 47(22), 4070–4098 (2008). https://doi.org/10.1002/anie.200701920
  145. S.J. Tans, A.R.M. Verschueren, C. Dekker, Room-temperature transistor based on a single carbon nanotube. Nature 393(6680), 49–52 (1998). https://doi.org/10.1038/29954
  146. A. Javey, J. Guo, Q. Wang, M. Lundstrom, H. Dai, Ballistic carbon nanotube field-effect transistors. Nature 424(6949), 654–657 (2003). https://doi.org/10.1038/nature01797
  147. Z. Zhang, X. Liang, S. Wang, K. Yao, Y. Hu et al., Doping-free fabrication of carbon nanotube based ballistic CMOS devices and circuits. Nano Lett. 7(12), 3603–3607 (2007). https://doi.org/10.1021/nl0717107
  148. Y. Zhang, N.W. Franklin, R.J. Chen, H. Dai, Metal coating on suspended carbon nanotubes and its implication to metal–tube interaction. Chem. Phys. Lett. 331(1), 35–41 (2000). https://doi.org/10.1016/S0009-2614(00)01162-3
  149. D.R. Kauffman, A. Star, Chemically induced potential barriers at the carbon nanotube-metal nanop interface. Nano Lett. 7(7), 1863–1868 (2007). https://doi.org/10.1021/nl070330i
  150. O.K. Varghese, D. Gong, M. Paulose, K.G. Ong, C.A. Grimes, Hydrogen sensing using titania nanotubes. Sens. Actuat. B Chem. 93(1–3), 338–344 (2003). https://doi.org/10.1016/S0925-4005(03)00222-3
  151. S.-Y. Guo, P.-X. Hou, F. Zhang, C. Liu, H.-M. Cheng, Gas sensors based on single-wall carbon nanotubes. Molecules 27(17), 5381 (2022). https://doi.org/10.3390/molecules27175381
  152. A.C. Romain, J. Nicolas, Long term stability of metal oxide-based gas sensors for e-nose environmental applications: an overview. Sens. Actuat. B Chem. 146(2), 502–506 (2010). https://doi.org/10.1016/j.snb.2009.12.027
  153. J.F. Fennell Jr., S.F. Liu, J.M. Azzarelli, J.G. Weis, S. Rochat et al., Nanowire chemical/biological sensors: status and a roadmap for the future. Angew. Chem. Int. Ed. 55(4), 1266–1281 (2016). https://doi.org/10.1002/anie.201505308
  154. F.M. Albarghouthi, D. Semeniak, I. Khanani, J.L. Doherty, B.N. Smith et al., Addressing signal drift and screening for detection of biomarkers with carbon nanotube transistors. ACS Nano 18, 5698 (2024). https://doi.org/10.1021/acsnano.3c11679
  155. IUPAC, Nomenclature, symbols, units and their usage in spectrochemical analysis - II. Data interpretation. Pure Appl. Chem. 45, 99 (1976). https://doi.org/10.1351/pac197645020099
  156. J.A.C. Broekaert, Daniel C. Harris: quantitative chemical analysis, 9th ed. Anal. Bioanal. Chem. 407(30), 8943–8944 (2015). https://doi.org/10.1007/s00216-015-9059-6
  157. C. Mackin, A. Fasoli, M. Xue, Y. Lin, A. Adebiyi, L. Bozano, T. Palacios, Chemical sensor systems based on 2D and thin film materials. 2D Mater. 7(2), 022002 (2020). https://doi.org/10.1088/2053-1583/ab6e88
  158. H.-P. Loock, P.D. Wentzell, Detection limits of chemical sensors: applications and misapplications. Sens. Actuat. B Chem. 173, 157–163 (2012). https://doi.org/10.1016/j.snb.2012.06.071
  159. L. Torsi, M. Magliulo, K. Manoli, G. Palazzo, Organic field-effect transistor sensors: a tutorial review. Chem. Soc. Rev. 42, 8612 (2013). https://doi.org/10.1039/C3CS60127G
  160. M. Püntener, T. Vigassy, E. Baier, A. Ceresa, E. Pretsch, Improving the lower detection limit of potentiometric sensors by covalently binding the ionophore to a polymer backbone. Anal. Chim. Acta 503(2), 187–194 (2004). https://doi.org/10.1016/j.aca.2003.10.030
  161. W. Shin, R.-H. Koo, S. Hong, G. Jung, Y. Jeong et al., Effects of sensor platform scaling on signal-to-noise ratio in the resistor- and horizontal floating-gate FET-type gas sensors. IEEE Trans. Electron Devices 70(11), 5845–5850 (2023). https://doi.org/10.1109/TED.2023.3312060
  162. J.R. Vig, F.L. Walls, A review of sensor sensitivity and stability. Proceedings of the 2000 IEEE/EIA International Frequency Control Symposium and Exhibition. June 9–9, 2000, Kansas City, MO, USA. IEEE, (2000)., pp. 30–33.
  163. X. Zhao, L. Lu, Y. Zou, C. Xiang, F. Xu et al., Morphology tuning of NiMo layered double hydroxide-modified MXene for highly sensitive detection of NH3 with long-term stability. ACS Appl. Nano Mater. 7(18), 21860–21870 (2024). https://doi.org/10.1021/acsanm.4c03794
  164. S.M. Scott, D. James, Z. Ali, Data analysis for electronic nose systems. Microchim. Acta 156(3), 183–207 (2006). https://doi.org/10.1007/s00604-006-0623-9
  165. P.C. Jurs, G.A. Bakken, H.E. McClelland, Computational methods for the analysis of chemical sensor array data from volatile analytes. Chem. Rev. 100(7), 2649–2678 (2000). https://doi.org/10.1021/cr9800964
  166. M. Ranbir, G. Kumar, J. Singh, N.K. Singh et al., Machine learning-based analytical systems: food forensics. ACS Omega 7(51), 47518–47535 (2022). https://doi.org/10.1021/acsomega.2c05632
  167. F. Cui, Y. Yue, Y. Zhang, Z. Zhang, H.S. Zhou, Advancing biosensors with machine learning. ACS Sens. 5(11), 3346–3364 (2020). https://doi.org/10.1021/acssensors.0c01424
  168. D. Karakaya, O. Ulucan, M. Turkan, Electronic nose and its applications: a survey. Int. J. Autom. Comput. 17(2), 179–209 (2020). https://doi.org/10.1007/s11633-019-1212-9
  169. J. Tan, J. Xu, Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: a review. Artif. Intell. Agric. 4, 104–115 (2020). https://doi.org/10.1016/j.aiia.2020.06.003
  170. S. Feng, F. Farha, Q. Li, Y. Wan, Y. Xu et al., Review on smart gas sensing technology. Sens. 19, 3760 (2019). https://doi.org/10.3390/s19173760
  171. H. Mei, J. Peng, T. Wang, T. Zhou, H. Zhao et al., Overcoming the limits of cross-sensitivity: pattern recognition methods for chemiresistive gas sensor array. Nano-Micro Lett. 16(1), 269 (2024). https://doi.org/10.1007/s40820-024-01489-z
  172. S. Satter, F. Bender, N. Post, A.J. Ricco, F. Josse, Analysis of multivariable sensor responses to multi-analyte gas samples in the presence of interferents and humidity. ACS Sens. 9(11), 6247–6256 (2024). https://doi.org/10.1021/acssensors.4c02200
  173. Q. Teng, K. Zhou, K. Yu, X. Zhang, Z. Li et al., Principal component analysis-assisted zirconium-based metal-organic frameworks/DNA biosensor for the analysis of various phosphates. Talanta 271, 125733 (2024). https://doi.org/10.1016/j.talanta.2024.125733
  174. A. Boujnah, A. Boubaker, S. Pecqueur, K. Lmimouni, A. Kalboussi, An electronic nose using conductometric gas sensors based on P3HT doped with triflates for gas detection using computational techniques (PCA, LDA, and kNN). J. Mater. Sci. Mater. Electron. 33(36), 27132–27146 (2022). https://doi.org/10.1007/s10854-022-09376-2
  175. T. Itoh, Y. Koyama, Y. Sakumura, T. Akamatsu, A. Tsuruta et al., Discrimination of volatile organic compounds using a sensor array via a rapid method based on linear discriminant analysis. Sens. Actuat. B Chem. 387, 133803 (2023). https://doi.org/10.1016/j.snb.2023.133803
  176. A. Zhang, Y. Zhang, W. Cheng, X. Li, K. Chen et al., Dual-gas sensing via SnO2-TiO2 heterojunction on MXene: machine learning-enhanced selectivity and sensitivity for hydrogen and ammonia detection. Sens. Actuat. B Chem. 429, 137340 (2025). https://doi.org/10.1016/j.snb.2025.137340
  177. J. Shan, K. Liu, J. Jiang, T. Liu, H. Yin, Application of support vector machine in quantitative analysis of mixed gas. Acta Opt. Sin. 43, 1206001 (2023). https://doi.org/10.3788/AOS221681
  178. Q. Zhang, T. Ren, K. Cao, Z. Xu, Advances of machine learning-assisted small extracellular vesicles detection strategy. Biosens. Bioelectron. 251, 116076 (2024). https://doi.org/10.1016/j.bios.2024.116076
  179. Y. Yang, D. Xie, D. Chen, L. Geng, Y. Zhang et al., A quadrilateral gas sensor cell by complete MEMS process for gas identification. IEEE Sens. J. 25(2), 2267–2275 (2025). https://doi.org/10.1109/JSEN.2024.3502637
  180. V.K. Ojha, A. Abraham, V. Snášel, Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng. Appl. Artif. Intell. 60, 97–116 (2017). https://doi.org/10.1016/j.engappai.2017.01.013
  181. L. Höfler, Good results from sensor data: performance of machine learning algorithms for regression problems in chemical sensors. Sens. Actuat. B Chem. 421, 136528 (2024). https://doi.org/10.1016/j.snb.2024.136528
  182. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors. Nature 323, 533 (1986). https://doi.org/10.1038/323533a0
  183. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
  184. D. Kwon, G. Jung, W. Shin, Y. Jeong, S. Hong et al., Low-power and reliable gas sensing system based on recurrent neural networks. Sens. Actuat. B Chem. 340, 129258 (2021). https://doi.org/10.1016/j.snb.2020.129258
  185. N. Lim, S. Hong, J. Jung, G.Y. Jung, D.H. Woo et al., Enhancing mixed gas discrimination in e-nose system: Sparse recurrent neural networks using transient current fluctuation of SMO array sensor. J. Ind. Inf. Integr. 42, 100715 (2024). https://doi.org/10.1016/j.jii.2024.100715
  186. G. Wei, G. Li, J. Zhao, A. He, Development of a LeNet-5 gas identification CNN structure for electronic noses. Sensors 19(1), 217 (2019). https://doi.org/10.3390/s19010217
  187. L. Guo, T. Wang, Z. Wu, J. Wang, M. Wang et al., Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks. Adv. Mater. 32(45), e2004805 (2020). https://doi.org/10.1002/adma.202004805
  188. Y. Jiang, Z. Wang, Y. Hu, W. Lin, L. Yao et al., Intelligent sniffer: a chip-based portable e-nose for accurate and fast essence evaluation. Sens. Actuat. B Chem. 426, 136989 (2025). https://doi.org/10.1016/j.snb.2024.136989
  189. R. Cao, Z. Lu, J. Hu, Y. Zhang, Carbon-based FET-type gas sensor for the detection of ppb-level benzene at room temperature. Chemosensors 12(9), 179 (2024). https://doi.org/10.3390/chemosensors12090179
  190. S. Abegg, L. Magro, J. van den Broek, S.E. Pratsinis, A.T. Güntner, A pocket-sized device enables detection of methanol adulteration in alcoholic beverages. Nat. Food 1(6), 351–354 (2020). https://doi.org/10.1038/s43016-020-0095-9
  191. J. van den Broek, S. Abegg, S.E. Pratsinis, A.T. Güntner, Highly selective detection of methanol over ethanol by a handheld gas sensor. Nat. Commun. 10(1), 4220 (2019). https://doi.org/10.1038/s41467-019-12223-4
  192. Y.G. Kim, B.M. Oh, H. Kim, E.H. Lee, D.H. Lee et al., Trifluoromethyl ketone P3HT-CNT composites for chemiresistive amine sensors with improved sensitivity. Sens. Actuat. B Chem. 367, 132076 (2022). https://doi.org/10.1016/j.snb.2022.132076
  193. M. Doshi, J. Zhang, E.P. Fahrenthold, Spin polarized chemiresistive CNT sensors for selective detection of explosive molecules. J. Phys. Chem. C 128(35), 14824–14833 (2024). https://doi.org/10.1021/acs.jpcc.4c03732
  194. S. Homayoonnia, A. Phani, S. Kim, MOF/MWCNT-nanocomposite manipulates high selectivity to gas via different adsorption sites with varying electron affinity: a study in methane detection in parts-per-billion. ACS Sens. 7(12), 3846–3856 (2022). https://doi.org/10.1021/acssensors.2c01796
  195. M. Khatib, H. Haick, Sensors for volatile organic compounds. ACS Nano 16(5), 7080–7115 (2022). https://doi.org/10.1021/acsnano.1c10827
  196. M. Kaloumenou, E. Skotadis, N. Lagopati, E. Efstathopoulos, D. Tsoukalas, Breath analysis: a promising tool for disease diagnosis-the role of sensors. Sensors 22(3), 1238 (2022). https://doi.org/10.3390/s22031238
  197. J. Yang, R. Sun, X. Bao, J. Liu, J.W. Ng et al., Enhancing selectivity of two-dimensional materials-based gas sensors. Adv. Funct. Mater. (2025). https://doi.org/10.1002/adfm.202420393
  198. R.A. Potyrailo, R.K. Bonam, J.G. Hartley, T.A. Starkey, P. Vukusic et al., Towards outperforming conventional sensor arrays with fabricated individual photonic vapour sensors inspired by Morpho butterflies. Nat. Commun. 6, 7959 (2015). https://doi.org/10.1038/ncomms8959
  199. T.-C. Wu, J. Dai, G. Hu, W.-B. Yu, O. Ogbeide et al., Machine-intelligent inkjet-printed α-Fe2O3/rGO towards NO2 quantification in ambient humidity. Sens. Actuat. B Chem. 321, 128446 (2020). https://doi.org/10.1016/j.snb.2020.128446
  200. M. Trawka, J. Smulko, L. Hasse, C.-G. Granqvist, F.E. Annanouch et al., Fluctuation enhanced gas sensing with WO3-based nanop gas sensors modulated by UV light at selected wavelengths. Sens. Actuator B Chem. 234, 453 (2016). https://doi.org/10.1016/j.snb.2016.05.032
  201. K. Drozdowska, A. Rehman, P. Sai, B. Stonio, A. Krajewska et al., Organic vapor sensing mechanisms by large-area graphene back-gated field-effect transistors under UV irradiation. ACS Sens. 7(10), 3094–3101 (2022). https://doi.org/10.1021/acssensors.2c01511
  202. I. Cho, K. Lee, Y.C. Sim, J.S. Jeong, M. Cho et al., Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor. Light Sci. Appl. 12(1), 95 (2023). https://doi.org/10.1038/s41377-023-01120-7
  203. B. Gil, D. Wales, H. Tan, E. Yeatman, Detection of medically relevant volatile organic compounds with graphene field-effect transistors and separated by low-frequency spectral and time signatures. Nanoscale 16(1), 61–71 (2023). https://doi.org/10.1039/d3nr04961b
  204. O. Ogbeide, G. Bae, W. Yu, E. Morrin, Y. Song et al., Inkjet-printed rGO/binary metal oxide sensor for predictive gas sensing in a mixed environment. Adv. Funct. Mater. 32(25), 2113348 (2022). https://doi.org/10.1002/adfm.202113348
  205. L. Bian, Z. Wang, D.L. White, A. Star, Machine learning-assisted calibration of Hg2+ sensors based on carbon nanotube field-effect transistors. Biosens. Bioelectron. 180, 113085 (2021). https://doi.org/10.1016/j.bios.2021.113085
  206. A. Mirzaei, S.G. Leonardi, G. Neri, Detection of hazardous volatile organic compounds (VOCs) by metal oxide nanostructures-based gas sensors: a review. Ceram. Int. 42(14), 15119–15141 (2016). https://doi.org/10.1016/j.ceramint.2016.06.145
  207. Q. Deng, S. Gao, T. Lei, Y. Ling, S. Zhang et al., Temperature & light modulation to enhance the selectivity of Pt-modified zinc oxide gas sensor. Sens. Actuat. B Chem. 247, 903–915 (2017). https://doi.org/10.1016/j.snb.2017.03.107
  208. A. Zulkefli, B. Mukherjee, R. Sahara, R. Hayakawa, T. Iwasaki et al., Enhanced selectivity in volatile organic compound gas sensors based on ReS2-FETs under light-assisted and gate-bias tunable operation. ACS Appl. Mater. Interfaces 13(36), 43030–43038 (2021). https://doi.org/10.1021/acsami.1c10054
  209. A. Zulkefli, B. Mukherjee, R. Hayakawa, T. Iwasaki, S. Nakaharai et al., Light-assisted and gate-tunable oxygen gas sensor based on rhenium disulfide field-effect transistors. Phys. Status Solidi RRL 14(11), 2000330 (2020). https://doi.org/10.1002/pssr.202000330
  210. H. Kwon, H. Yoo, M. Nakano, K. Takimiya, J.J. Kim et al., Gate-tunable gas sensing behaviors in air-stable ambipolar organic thin-film transistors. RSC Adv. 10(4), 1910–1916 (2020). https://doi.org/10.1039/c9ra09195e
  211. A. Maity, S. Rapoport, H. Haick, Gate-controlled chiral recognition and spin assessment with all-electric hybrid quantum wire-based transistors. Small 19(51), 2205038 (2023). https://doi.org/10.1002/smll.202205038
  212. N. Peng, Q. Zhang, Y.C. Lee, O.K. Tan, N. Marzari, Gate modulation in carbon nanotube field effect transistors-based NH3 gas sensors. Sens. Actuat. B Chem. 132(1), 191–195 (2008). https://doi.org/10.1016/j.snb.2008.01.025
  213. A. Bilić, J.R. Reimers, N.S. Hush, J. Hafner, Adsorption of ammonia on the gold (111) surface. J. Chem. Phys. 116(20), 8981–8987 (2002). https://doi.org/10.1063/1.1471245
  214. J.P. Novak, E.S. Snow, E.J. Houser, D. Park, J.L. Stepnowski et al., Nerve agent detection using networks of single-walled carbon nanotubes. Appl. Phys. Lett. 83(19), 4026–4028 (2003). https://doi.org/10.1063/1.1626265
  215. S. Rumyantsev, G. Liu, M.S. Shur, R.A. Potyrailo, A.A. Balandin, Selective gas sensing with a single pristine graphene transistor. Nano Lett. 12(5), 2294–2298 (2012). https://doi.org/10.1021/nl3001293
  216. R. Samnakay, C. Jiang, S.L. Rumyantsev, M.S. Shur, A.A. Balandin, Selective chemical vapor sensing with few-layer MoS2 thin-film transistors: Comparison with graphene devices. Appl. Phys. Lett. 106(2), 023115 (2015). https://doi.org/10.1063/1.4905694
  217. S. Huang, A. Croy, L.A. Panes-Ruiz, V. Khavrus, V. Bezugly et al., Machine learning-enabled smart gas sensing platform for identification of industrial gases. Adv. Intell. Syst. 4(4), 2200016 (2022). https://doi.org/10.1002/aisy.202200016
  218. I. Heller, A.M. Janssens, J. Männik, E.D. Minot, S.G. Lemay et al., Identifying the mechanism of biosensing with carbon nanotube transistors. Nano Lett. 8(2), 591–595 (2008). https://doi.org/10.1021/nl072996i
  219. A.B. Artyukhin, M. Stadermann, R.W. Friddle, P. Stroeve, O. Bakajin et al., Controlled electrostatic gating of carbon nanotube FET devices. Nano Lett. 6(9), 2080–2085 (2006). https://doi.org/10.1021/nl061343j
  220. X. Xu, B.J. Bowen, R.E.A. Gwyther, M. Freeley, B. Grigorenko et al., Tuning electrostatic gating of semiconducting carbon nanotubes by controlling protein orientation in biosensing devices. Angew. Chem. Int. Ed. 60(37), 20184–20189 (2021). https://doi.org/10.1002/anie.202104044
  221. R.J. Chen, H.C. Choi, S. Bangsaruntip, E. Yenilmez, X. Tang et al., An investigation of the mechanisms of electronic sensing of protein adsorption on carbon nanotube devices. J. Am. Chem. Soc. 126(5), 1563–1568 (2004). https://doi.org/10.1021/ja038702m
  222. R.J. Chen, S. Bangsaruntip, K.A. Drouvalakis, N.W. Shi Kam, M. Shim et al., Noncovalent functionalization of carbon nanotubes for highly specific electronic biosensors. Proc. Natl. Acad. Sci. USA 100(9), 4984–4989 (2003). https://doi.org/10.1073/pnas.0837064100
  223. A.M. Dr, K.B. Dr, M.B. Dr, K.K. Prof, Functionalized metallic carbon nanotube devices for pH sensing. Chem. Phys. Chem. 8(2), 220–223 (2007). https://doi.org/10.1002/cphc.200600498
  224. T. Hao, R. Zhang, S. Ren, Y. Jia, Undecorated GFET for determinations of heavy metal ions aided by machine learning algorithms. Talanta Open 7, 100176 (2023). https://doi.org/10.1016/j.talo.2022.100176
  225. I. Park, J. Lim, S. You, M.T. Hwang, J. Kwon et al., Detection of SARS-CoV-2 virus amplification using a crumpled graphene field-effect transistor biosensor. ACS Sens. 6(12), 4461–4470 (2021). https://doi.org/10.1021/acssensors.1c01937
  226. C. Qu, I. Maini, Q. Guo, A. Stacey, D.A.J. Moran, Extreme enhancement-mode operation accumulation channel hydrogen-terminated diamond FETs with Vth < − 6 V and high on-current. Adv. Electron. Mater. 2400770 (2024). https://doi.org/10.1002/aelm.202400770
  227. R. Abimaheshwari, T. Murugadass, R. Abinaya, M. Navaneethan, S. Harish, Fabrication of SnS2/Si heterostructure for ultra-high selective and rapid room temperature NO2 gas detection with enhanced carrier mobility. Sens. Actuat. B Chem. 428, 137165 (2025). https://doi.org/10.1016/j.snb.2024.137165
  228. H. Tabata, H. Matsuyama, T. Goto, O. Kubo, M. Katayama, Visible-light-activated response originating from carrier-mobility modulation of NO2 gas sensors based on MoS2 monolayers. ACS Nano 15(2), 2542–2553 (2021). https://doi.org/10.1021/acsnano.0c06996
  229. G.O. Silva, Z.P. Michael, L. Bian, G.V. Shurin, M. Mulato et al., Nanoelectronic discrimination of nonmalignant and malignant cells using nanotube field-effect transistors. ACS Sens. 2(8), 1128–1132 (2017). https://doi.org/10.1021/acssensors.7b00383
  230. L. Bian, D.C. Sorescu, L. Chen, D.L. White, S.C. Burkert et al., Machine-learning identification of the sensing descriptors relevant in molecular interactions with metal nanop-decorated nanotube field-effect transistors. ACS Appl. Mater. Interfaces 11(1), 1219–1227 (2019). https://doi.org/10.1021/acsami.8b15785
  231. W. Shao, D.C. Sorescu, Z. Liu, A. Star, Machine learning discrimination and ultrasensitive detection of fentanyl using gold nanop-decorated carbon nanotube-based field-effect transistor sensors. Small 20(35), e2311835 (2024). https://doi.org/10.1002/smll.202311835
  232. E. Fu, K. Khederlou, N. Lefevre, S.A. Ramsey, M.L. Johnston et al., Progress on electrochemical sensing of pharmaceutical drugs in complex biofluids. Chemosensors 11(8), 467 (2023). https://doi.org/10.3390/chemosensors11080467
  233. Z. Liu, G.V. Shurin, L. Bian, D.L. White, M.R. Shurin et al., A carbon nanotube sensor array for the label-free discrimination of live and dead cells with machine learning. Anal. Chem. 94(8), 3565–3573 (2022). https://doi.org/10.1021/acs.analchem.1c04661
  234. Z. Liu, L. Bian, C.J. Yeoman, G. Dennis Clifton, J.E. Ellington et al., Bacterial vaginosis monitoring with carbon nanotube field-effect transistors. Anal. Chem. 94(9), 3849–3857 (2022). https://doi.org/10.1021/acs.analchem.1c04755
  235. R. Mitobe, Y. Sasaki, W. Tang, Q. Zhou, X. Lyu et al., Multi-oxyanion detection by an organic field-effect transistor with pattern recognition techniques and its application to quantitative phosphate sensing in human blood serum. ACS Appl. Mater. Interfaces 14(20), 22903–22911 (2022). https://doi.org/10.1021/acsami.1c21092
  236. M. Sensi, R.F. de Oliveira, M. Berto, M. Palmieri, E. Ruini et al., Reduced graphene oxide electrolyte-gated transistor immunosensor with highly selective multiparametric detection of anti-drug antibodies. Adv. Mater. 35(36), e2211352 (2023). https://doi.org/10.1002/adma.202211352
  237. D. Tsui, F. Downey, S. Navaneethan, A. Paul, T. Bodily et al., A machine learning approach to COVID-19 detection via graphene field-effect-transistor (GFET). 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). July 24-27, 2023, Sydney, Australia. IEEE, (2023)., pp. 1–4
  238. S.-Z. Liang, G. Chen, A.R. Harutyunyan, J.O. Sofo, Screening of charged impurities as a possible mechanism for conductance change in graphene gas sensing. Phys. Rev. B 90(11), 115410 (2014). https://doi.org/10.1103/physrevb.90.115410
  239. S. Adam, E.H. Hwang, V.M. Galitski, S. Das Sarma, A self-consistent theory for graphene transport. Proc. Natl. Acad. Sci. USA 104(47), 18392–18397 (2007). https://doi.org/10.1073/pnas.0704772104
  240. J.-H. Chen, C. Jang, S. Adam, M.S. Fuhrer, E.D. Williams et al., Charged-impurity scattering in graphene. Nat. Phys. 4(5), 377–381 (2008). https://doi.org/10.1038/nphys935
  241. S. Mehdi Aghaei, M.M. Monshi, I. Torres, S.M.J. Zeidi, I. Calizo, DFT study of adsorption behavior of NO, CO, NO2, and NH3 molecules on graphene-like BC3: a search for highly sensitive molecular sensor. Appl. Surf. Sci. 427, 326–333 (2018). https://doi.org/10.1016/j.apsusc.2017.08.048
  242. X.-Y. Liang, N. Ding, S.-P. Ng, C.L. Wu, Adsorption of gas molecules on Ga-doped graphene and effect of applied electric field: a DFT study. Appl. Surf. Sci. 411, 11–17 (2017). https://doi.org/10.1016/j.apsusc.2017.03.178
  243. Z. Zhou, J. Song, Y. Xie, Y. Ma, H. Hu et al., DFT calculation for organic semiconductor-based gas sensors: Sensing mechanism, dynamic response and sensing materials. Chin. Chem. Lett. (2025). https://doi.org/10.1016/j.cclet.2025.110906
  244. M. Tripathi, G. Deokar, J. Casanova-Chafer, J. Jin, A. Sierra-Castillo et al., Vertical heterostructure of graphite-MoS2 for gas sensing. Nanoscale Horiz. 9, 1330 (2024). https://doi.org/10.1039/d4nh00049h
  245. S. Yuan, S. Zeng, Y. Hu, W. Kong, H. Yang et al., Epitaxial metal-organic framework-mediated electron relay for H2 detection on demand. ACS Nano 18(30), 19723–19731 (2024). https://doi.org/10.1021/acsnano.4c05206
  246. Y. Huang, X. Yan, Q. He, J. Qiu, Y. Zhang et al., Ceria and gold co-decorated porous MoS2@graphene nanocomposite electrochemical electrode integrated with smartphone-controlled microstation for simultaneous dual metal ions detection. Electrochim. Acta 437, 141509 (2023). https://doi.org/10.1016/j.electacta.2022.141509
  247. H.-L. Hou, C. Anichini, P. Samori, A. Criado, M. Prato, 2D van der waals heterostructures for chemical sensing. Adv. Funct. Mater. 32, 2207065 (2022). https://doi.org/10.1002/adfm.202207065
  248. O. Sisman, O. Smirnova, Y. Xia, N. Greiner-Mai, A. Reupert et al., Overcoming the selectivity-sensitivity trade-off in electroactive gas sensing using hybrid glass composites. Adv. Funct. Mater. 35(10), 2416535 (2025). https://doi.org/10.1002/adfm.202416535
  249. S.M. Lee, Y.J. Kim, S.J. Park, W.S. Cheon, J. Kim et al., In-situ growth of 2D MOFs as a molecular sieving layer on SnS2 nanoflakes for realizing ultraselective H2S detection. Adv. Funct. Mater. 35(12), 2417019 (2025). https://doi.org/10.1002/adfm.202417019
  250. X. Wu, S. Shi, J. Jiang, D. Lin, J. Song et al., Bionic olfactory neuron with in-sensor reservoir computing for intelligent gas recognition. Adv. Mater. (2025). https://doi.org/10.1002/adma.202419159
  251. X. Zhang, C. Liu, Z. Liu, Y. Liu, H. Chen et al., Bioengineered two-dimensional nanomaterials (Bio-2DNMs) for cancer diagnosis and therapy: a comprehensive review. Chem. Eng. J. 507, 160430 (2025). https://doi.org/10.1016/j.cej.2025.160430
  252. V.V. Tran, V.K.H. Bui, D.L.T. Nguyen, H.H. Do, Challenges and development strategies of conjugated polymers composites-based biosensors for detection of virus biomarkers. Chem. Eng. J. 503, 158532 (2025). https://doi.org/10.1016/j.cej.2024.158532
  253. L.R. Jaishi, W. Ding, R.A. Kittelson, F. Tsow, X. Xian, Metal-organic frameworks (MOFs)-based piezoelectric-colorimetric hybrid sensor for monitoring green leaf volatiles. ACS Sens. 9(12), 6553–6562 (2024). https://doi.org/10.1021/acssensors.4c02016
  254. K.K. Zamansky, F.S. Fedorov, S.D. Shandakov, M. Chetyrkina, A.G. Nasibulin, A gas sensor based on free-standing SWCNT film for selective recognition of toxic and flammable gases under thermal cycling protocols. Sens. Actuat. B Chem. 417, 136116 (2024). https://doi.org/10.1016/j.snb.2024.136116
  255. Z.W. Wong, S.Y. New, Recent advances in biosensors based on hybridization chain reaction and silver nanoclusters. Small Meth. 9, 2401436 (2025). https://doi.org/10.1002/smtd.202401436
  256. A.-M. Andringa, J.R. Meijboom, E.C.P. Smits, S.G.J. Mathijssen, P.W.M. Blom et al., Gate-bias controlled charge trapping as a mechanism for NO2 detection with field-effect transistors. Adv. Funct. Mater. 21(1), 100–107 (2011). https://doi.org/10.1002/adfm.201001560
  257. M. Mattmann, C. Roman, T. Helbling, D. Bechstein, L. Durrer et al., Pulsed gate sweep strategies for hysteresis reduction in carbon nanotube transistors for low concentration NO2 gas detection. Nanotechnology 21(18), 185501 (2010). https://doi.org/10.1088/0957-4484/21/18/185501
  258. D. Kwon, J.H. Lee, S. Kim, R. Lee, H. Mo et al., Drift-free pH detection with silicon nanowire field-effect transistors. IEEE Electron Device Lett. 37(5), 652–655 (2016). https://doi.org/10.1109/LED.2016.2542846
  259. J. Han, X. Xie, S. Sun, An improved analytic dictionary learning method for sparse representation in gas recognition and classification. IEEE Sens. J. 24(9), 15665–15675 (2024). https://doi.org/10.1109/JSEN.2024.3381149
  260. D. Guo, D. Zhang, L. Zhang, Sparse representation-based classification for breath sample identification. Sens. Actuat. B Chem. 158(1), 43–53 (2011). https://doi.org/10.1016/j.snb.2011.05.010
  261. D.R. Wijaya, F. Afianti, Stability assessment of feature selection algorithms on homogeneous datasets: A study for sensor array optimization problem. IEEE Access 8, 33944 (2020). https://doi.org/10.1109/ACCESS.2020.2974982
  262. J. Fonollosa, L. Fernández, R. Huerta, A. Gutiérrez-Gálvez, S. Marco, Temperature optimization of metal oxide sensor arrays using mutual information. Sens. Actuat. B Chem. 187, 331–339 (2013). https://doi.org/10.1016/j.snb.2012.12.026
  263. J. Zhou, C.M. Welling, S. Kawadiya, M.A. Deshusses, S. Grego et al., Sensor-array optimization based on mutual information for sanitation-related malodor alerts. in 2019 IEEE Biomedical Circuits and Systems Conference (Biocas 2019) (IEEE, New York, 2019). https://doi.org/10.1109/biocas.2019.8919132
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