Progress in Research on VOC Molecule Recognition by Semiconductor Sensors

doi:10.3866/PKU.WHXB202008018

	Progress in Research on VOC Molecule Recognition by Semiconductor Sensors
	Liu, Hongyu 1,2; Meng, Gang 1; Deng, Zanhong 1; Li, Meng 1,2; Chang, Junqing 1,2; Dai, Tiantian 1,2; Fang, Xiaodong 1
刊名	ACTA PHYSICO-CHIMICA SINICA
	2022-05-15
卷号	38
关键词	Metal oxide semiconductor Gas sensor E-nose Thermal modulation Pattern recognition Machine learning Convolutional neural network
ISSN号	1000-6818
DOI	10.3866/PKU.WHXB202008018
通讯作者	Meng, Gang(menggang@aiofm.ac.cn) ; Fang, Xiaodong(xdfang@aiofm.ac.cn)
英文摘要	Metal oxide semiconductor (MOS) gas sensors have been widely used in military and scientific research, as well as various industries; this is because of the unique advantages of MOS gas sensors including their small size, low power consumption, high sensitivity, and good silicon chip compatibility. However, the poor selectivity of MOS sensors has restricted their potential application in the Internet of Things (IoT) era. In this paper, progress in the research addressing the selectivity issues of MOS sensors is reviewed, and three strategies for selective MOS sensors, and performance improvements of MOS, e-nose, and thermal modulation, are introduced. Research on the performance improvements of MOS-sensitive materials provides an important guarantee for fast and accurate identification of trace gas molecules. The e-nose system adopts an array of sensors with distinct surface chemical properties; more "features" of volatile organic compound (VOC) molecules can be extracted by enlarging the number of sensor arrays, providing a "many-to-one" or "many-to-many" approach to discriminate VOC gas molecules via pattern recognition/machine learning algorithms. For thermal modulation technology, the working temperature of the sensor is intentionally swept during one measurement cycle, and the dynamic response signals of the sensor to different VOC gases under a given temperature mode are tested. Combined with signal processing and pattern recognition/machine learning, the "one-to-many" recognition of VOC gas molecules is realized by a single MOS sensor. Principal component analysis (PCA), linear discriminant analysis (LDA), and neural network (NN) pattern recognition/machine learning algorithms are compared in this review. Among them, the LDA algorithm based on supervised learning can be used as a signal dimension reduction or pattern recognition method. It is mainly applicable to the gas identification and classification of small datasets of VOC gas molecules. LDA is superior to PCA (based on unsupervised learning) in identifying and classifying VOC gas molecules. Compared with the LDA algorithm, an artificial neural network (ANN) based on the back-propagation algorithm, as a highly robust machine learning classification model, has the potential to process large datasets and realize the classification and identification of multiple kinds of VOC gases. Finally, the deep learning algorithm of convolutional neural networks (CNNs), with the performance of data dimension reduction, feature extraction, and robust identification, is expected to be applied in the field of VOC gas identification. Based on the performance improvement of MOS, a combination of multiple modulation methods and array technology, as well as the latest developments of deep learning algorithms in the artificial intelligence (AI) field, will greatly enhance the VOC molecular recognition capability of nonselective MOS sensors.
资助项目	National Natural Science Foundation of China[11604339] ; National Natural Science Foundation of China[11674324] ; CAS Pioneer Hundred Talents Program from Chinese Academy of Sciences, CAS-JSPS Joint Research Projects[GJHZ1891] ; National Key Laboratory of Quantum Optics and Photonic Devices, China[KF201901] ; CAS-NSTDA Joint Research Projects[GJHZ202101]
WOS关键词	ELECTRONIC NOSE ; GAS-SENSOR ; QUANTUM DOTS ; TEMPERATURE MODULATION ; NONLINEAR RESPONSES ; OLFACTORY SYSTEM ; METAL ; DISCRIMINATION ; NANOWIRE ; CO
WOS研究方向	Chemistry
语种	英语
出版者	PEKING UNIV PRESS
WOS记录号	WOS:000722096000009
资助机构	National Natural Science Foundation of China ; CAS Pioneer Hundred Talents Program from Chinese Academy of Sciences, CAS-JSPS Joint Research Projects ; National Key Laboratory of Quantum Optics and Photonic Devices, China ; CAS-NSTDA Joint Research Projects
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/126730]
专题	中国科学院合肥物质科学研究院
通讯作者	Meng, Gang; Fang, Xiaodong
作者单位	1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Anhui Prov Key Lab Photon Devices & Mat, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Liu, Hongyu,Meng, Gang,Deng, Zanhong,et al. Progress in Research on VOC Molecule Recognition by Semiconductor Sensors[J]. ACTA PHYSICO-CHIMICA SINICA,2022,38.
APA	Liu, Hongyu.,Meng, Gang.,Deng, Zanhong.,Li, Meng.,Chang, Junqing.,...&Fang, Xiaodong.(2022).Progress in Research on VOC Molecule Recognition by Semiconductor Sensors.ACTA PHYSICO-CHIMICA SINICA,38.
MLA	Liu, Hongyu,et al."Progress in Research on VOC Molecule Recognition by Semiconductor Sensors".ACTA PHYSICO-CHIMICA SINICA 38(2022).