GAS SENSOR ARRAY BASED ON GRAPHENE-DOPED TIO2 FOR ACETONE VAPOR WITH DEEP NEURAL NETWORK
DOI:
https://doi.org/10.11113/jurnalteknologi.v88.24681Abstract
Acetone is categorized as a volatile organic compound (VOC) gas. Exposure to it for a long period harms human health and the environment. Commonly, a single sensor is used to sense the target gas. However, it always suffered from low selectivity, making it difficult to analyze and differentiate various gases. To overcome this problem, a gas sensor array is proposed in this work to investigate the response value for each array to the acetone vapor. Six sensor arrays based on various ratios of TiO2 doped with graphene were deposited using the screen-printing technique on an FR3-printed circuit board (PCB). The gas sensor arrays were exposed to two different concentrations of acetone vapor at room temperature. Next, the response values were classified using a deep neural network (DNN). The results revealed that the T95_G5 gas sensor responds more to 25 mL and 35 mL of acetone gas, with 1.0966 and 1.2074, respectively. The overall DNN accuracy for acetone vapor was 82.70%.
References
J. Rodrigues, S. Jain, and N. G. Shimpi, “Performance of 1D tin (Sn) decorated spherical shape ZnO nanostructures as an acetone gas sensor for room and high temperature,” Mater. Sci. Eng. B, vol. 288, no. December 2022, p. 116199, 2023, doi: 10.1016/j.mseb.2022.116199.
M. Shooshtari and A. Salehi, “An electronic nose based on carbon nanotube -titanium dioxide hybrid nanostructures for detection and discrimination of volatile organic compounds,” Sensors Actuators B Chem., vol. 357, no. January, p. 131418, 2022, doi: 10.1016/j.snb.2022.131418.
H. Yan and Y. G. Zhou, “Electrical sensing of volatile organic compounds in exhaled breath for disease diagnosis,” Curr. Opin. Electrochem., vol. 33, p. 100922, 2022, doi: 10.1016/j.coelec.2021.100922.
G. Wan, R. Xue, T. Qin, F. Zhang, and Y. Li, “Acetone detection at low temperature using a gas nano-sensor based on ZnO flakes loaded with metal organic-skeleton ZIF-8,” Mater. Today Commun., vol. 37, no. October, p. 107561, 2023, doi: 10.1016/j.mtcomm.2023.107561.
N. Van Duy et al., “Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification,” Sensors Actuators B Chem., vol. 351, no. October 2021, p. 130979, 2022, doi: 10.1016/j.snb.2021.130979.
K. Yong, L. Su, H. Liu, C. Xu, and H. Zhang, “Co3O4/SnO2 heterojunctions formed as porous nanocubes with remarkable sensitivity and selectivity to acetone gas,” Mater. Lett., vol. 384, no. January, p. 138118, 2025, doi: 10.1016/j.matlet.2025.138118.
Y. Chen et al., “Highly sensitive and selective acetone gas sensors based on modified ZnO nanomaterials,” Mater. Sci. Semicond. Process., vol. 148, no. March, p. 106807, 2022, doi: 10.1016/j.mssp.2022.106807.
V. S. Chandak, M. B. Kumbhar, and P. M. Kulal, “Highly sensitive and selective acetone gas sensor-based La-doped ZnO nanostructured thin film,” Mater. Lett., vol. 357, no. October 2023, p. 135747, 2024, doi: 10.1016/j.matlet.2023.135747.
A. A. Haidry, Y. Wang, Y. Weng, A. Raza, and K. Yusuf, “Excellent acetone sensing enabled by tunable metal organic framework derived TiO2 nanodisks,” Talanta, vol. 287, no. January, p. 127547, 2025, doi: 10.1016/j.talanta.2025.127547.
X. Liu, E. Cao, Y. Zhang, Y. Tian, L. Sun, and B. Sun, “Ni-TiO2 nanotube arrays for highly sensitive and selective detection of acetone vapor,” Mater. Lett., vol. 355, no. October 2023, p. 135526, 2024, doi: 10.1016/j.matlet.2023.135526.
J. Oh et al., “Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature,” Sensors Actuators B Chem., vol. 364, no. February, p. 131894, 2022, doi: 10.1016/j.snb.2022.131894.
L. Lyu et al., “A novel CeO2 Hollow-Shell sensor constructed for high sensitivity of acetone gas detection,” Appl. Surf. Sci., vol. 571, no. August 2021, p. 151337, 2022, doi: 10.1016/j.apsusc.2021.151337.
C. Li, P. G. Choi, K. Kim, and Y. Masuda, “High performance acetone gas sensor based on ultrathin porous NiO nanosheet,” Sensors Actuators B Chem., vol. 367, no. June, p. 132143, 2022, doi: 10.1016/j.snb.2022.132143.
C. Shi et al., “Vertically aligned mesoporous Ce doped NiO nanowalls with multilevel gas channels for high-performance acetone gas sensors,” Sensors Actuators B Chem., vol. 401, no. October 2023, p. 134888, 2024, doi: 10.1016/j.snb.2023.134888.
Y. Y. Guo, X. H. Zheng, L. B. Bo, Z. Q. Gu, C. Zhang, and Y. F. Liu, “UV-activated gas sensor based on ordered mesoporous ZnO–TiO2 heterogeneous composites for trace NO2 detection at room temperature,” Talanta, vol. 285, no. October 2024, 2025, doi: 10.1016/j.talanta.2024.127415.
A. Vijaykumar, A. Mondal, S. Deb, B. Ajitha, and Y. A. K. Reddy, “Development of room-temperature operable TiO2-x-based hydrogen gas sensor with light irradiation,” Appl. Surf. Sci., vol. 670, no. May, p. 160664, 2024, doi: 10.1016/j.apsusc.2024.160664.
Y. S. Chang, M. C. Cheng, D. C. Tsai, and F. S. Shieu, “Silver nanowires@TiO2 core-shell for room-temperature 1000 ppm NH3 gas sensors,” J. Mater. Res. Technol., vol. 31, no. June, pp. 3079–3089, 2024, doi: 10.1016/j.jmrt.2024.07.022.
Q. Liu, S. Fan, L. Fu, C. Liu, J. Xu, and W. Tang, “Carbon quantum dot modified electrospun TiO2 nanofibers for flexible formaldehyde gas sensor under UV illumination at room temperature,” Diam. Relat. Mater., vol. 140, no. PB, p. 110542, 2023, doi: 10.1016/j.diamond.2023.110542.
J. Y. Kim, S. P. Bharath, A. Mirzaei, S. S. Kim, and H. W. Kim, “Identification of gas mixtures using gold-decorated metal oxide based sensor arrays and neural networks,” Sensors Actuators B Chem., vol. 386, no. January, p. 133767, 2023, doi: 10.1016/j.snb.2023.133767.
Á. Peña et al., “Optimization of multilayer graphene-based gas sensors by ultraviolet photoactivation,” Appl. Surf. Sci., vol. 610, no. August 2022, 2023, doi: 10.1016/j.apsusc.2022.155393.
M. Sangeetha and D. Madhan, “Ultra sensitive molybdenum disulfide (MoS2)/graphene based hybrid sensor for the detection of NO2 and formaldehyde gases by fiber optic clad modified method,” Opt. Laser Technol., vol. 127, no. December 2019, p. 106193, 2020, doi: 10.1016/j.optlastec.2020.106193.
Y. Li, Y. Xu, and X. Li, “The sensing mechanism of HCHO gas sensor based on transition metal doped graphene: Insights from DFT study,” Sensors Actuators A Phys., vol. 338, no. February, p. 113460, 2022, doi: 10.1016/j.sna.2022.113460.
Z. Chen, J. Wang, and Y. Wang, “Strategies for the performance enhancement of graphene-based gas sensors: A review,” Talanta, vol. 235, no. July, p. 122745, 2021, doi: 10.1016/j.talanta.2021.122745.
C. Wang, Y. Wang, Z. Yang, and N. Hu, “Review of recent progress on graphene-based composite gas sensors,” Ceram. Int., vol. 47, no. 12, pp. 16367–16384, 2021, doi: 10.1016/j.ceramint.2021.02.144.
J. Zhang et al., “A miniaturized electronic nose with artificial neural network for anti-interference detection of mixed indoor hazardous gases,” Sensors Actuators, B Chem., vol. 326, no. May 2020, p. 128822, 2021, doi: 10.1016/j.snb.2020.128822.
J. Park et al., “Optical bioelectronic nose of outstanding sensitivity and selectivity toward volatile organic compounds implemented with genetically engineered bacteriophage: Integrated study of multi-scale computational prediction and experimental validation,” Biosens. Bioelectron., vol. 177, no. October 2020, p. 112979, 2021, doi: 10.1016/j.bios.2021.112979.
S. Acharyya, P. K. Bhowmick, and P. K. Guha, “Selective identification and quantification of VOCs using metal nanoparticles decorated SnO2 hollow-spheres based sensor array and machine learning,” J. Alloys Compd., vol. 968, no. January, p. 171891, 2023, doi: 10.1016/j.jallcom.2023.171891.
R. Faleh and A. Kachouri, “A hybrid deep convolutional neural network-based electronic nose for pollution detection purposes,” Chemom. Intell. Lab. Syst., vol. 237, no. March, p. 104825, 2023, doi: 10.1016/j.chemolab.2023.104825.
Y. Wang, Y. Jiang, H. Hao, B. Yu, J. Li, and S. Zhou, “Pressure calibration- and profile fitting-free spectroscopy technology based on deep neural network for gas sensing,” Meas. J. Int. Meas. Confed., vol. 204, no. September, p. 112077, 2022, doi: 10.1016/j.measurement.2022.112077.
S. Godfrey Nnabuife, B. Kuang, J. F. Whidborne, and Z. Rana, “Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline riser using a Doppler ultrasonic sensor and deep neural networks,” Chem. Eng. J., vol. 403, no. December 2019, p. 126401, 2021, doi: 10.1016/j.cej.2020.126401.
S. A. Mohd Chachuli et al., “Effects of silver diffusement on TiO2-B2O3 nanocomposite sensor towards hydrogen sensing,” Mater. Res. Innov., vol. 00, no. 00, pp. 1–14, 2024, doi: 10.1080/14328917.2024.2304480.
S. A. M. Chachuli, N. A. Muhammad ‘Azmi, N. H. Shamsudin, O. Coban, and S. A. C. Aziz, “Impact of Chitosan as a Binder in Graphene Gas Sensor for Volatile Organic Compound Detection,” ChemistrySelect, vol. 10, no. 4, pp. 1–14, 2025, doi: 10.1002/slct.202405111.
J. Chu et al., “Identification of gas mixtures via sensor array combining with neural networks,” Sensors Actuators, B Chem., vol. 329, no. December 2019, p. 129090, 2021, doi: 10.1016/j.snb.2020.129090.
X. Chen, Q. Xing, X. Tang, Y. Cai, and M. Zhang, “Machine learning algorithm assisted cerium oxide based high selectivity acetone sensor,” Ceram. Int., vol. 50, no. 21PA, pp. 41770–41779, 2024, doi: 10.1016/j.ceramint.2024.08.030.
B. Wang et al., “Artificial olfaction based on tafel curve for quantitative detection of acetone ethanol gas mixture,” Sensors Actuators B Chem., vol. 377, no. November 2022, pp. 2–9, 2023, doi: 10.1016/j.snb.2022.133049.
I. Sohn et al., “Selective denoising autoencoder for classification of noisy gas mixtures using 2D transition metal dichalcogenides,” Talanta, vol. 283, no. May 2024, p. 127129, 2025, doi: 10.1016/j.talanta.2024.127129.
Z. Wang et al., “Improved deep bidirectional recurrent neural network for learning the cross-sensitivity rules of gas sensor array,” Sensors Actuators B Chem., vol. 401, no. July 2023, p. 134996, 2024, doi: 10.1016/j.snb.2023.134996.
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