VOCs分子的半导体型传感器识别检测研究进展

doi:10.3866/PKU.WHXB202008018

物理化学学报 >> 2022, Vol. 38 >> Issue (5): 2008018.doi: 10.3866/PKU.WHXB202008018

VOCs分子的半导体型传感器识别检测研究进展

刘弘禹¹^,², 孟钢¹^,*(), 邓赞红¹, 李蒙¹^,², 常鋆青¹^,², 代甜甜¹^,², 方晓东¹^,*()

¹ 中国科学院合肥物质科学研究院，安徽光学精密机械研究所，光子器件与材料安徽省重点实验室，合肥 230031
² 中国科学技术大学，研究生院科学岛分院，合肥 230026

收稿日期:2020-08-06 录用日期:2020-08-28 发布日期:2020-09-03
通讯作者: 孟钢,方晓东 E-mail:menggang@aiofm.ac.cn;xdfang@aiofm.ac.cn
作者简介:孟钢，中国科学院安徽光机所研究员，生于1982年。2010年在中国科学院安徽光学精密机械研究所获博士学位。中国科学院“百人计划”入选者，主要从事半导体纳米材料光电与传感器件研究
方晓东，中国科学院安徽光机所研究员，生于1963年。2000年在日本大阪大学基础工学部获得博士学位。中国科学院“百人计划”入选者，主要从事紫外激光、光电材料与器件研究
基金资助:
国家自然科学基金(11604339);国家自然科学基金(11674324);中国科学院“百人计划”;中国科学院-日本学术振兴会协议项目(GJHZ1891);中国科学院与泰国科技发展(GJHZ202101);量子光学与光量子器件国家重点实验室开放课题(KF201901)

Progress in Research on VOC Molecule Recognition by Semiconductor Sensors

Hongyu Liu¹^,², Gang Meng¹^,*(), Zanhong Deng¹, Meng Li¹^,², Junqing Chang¹^,², Tiantian Dai¹^,², Xiaodong Fang¹^,*()

¹ Anhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
² Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China

Received:2020-08-06 Accepted:2020-08-28 Published:2020-09-03
Contact: Gang Meng,Xiaodong Fang E-mail:menggang@aiofm.ac.cn;xdfang@aiofm.ac.cn
About author:Email: xdfang@aiofm.ac.cn, Tel.: +86-551-65593661, (X.F.)
Email: menggang@aiofm.ac.cn, Tel.: +86-551-65593508, (G.M.)
Supported by:
the National Natural Science Foundation of China(11604339);the National Natural Science Foundation of China(11674324);CAS Pioneer Hundred Talents Program from Chinese Academy of Sciences;CAS-JSPS Joint Research Projects(GJHZ1891);CAS-NSTDA Joint Research Projects(GJHZ202101);National Key Laboratory of Quantum Optics and Photonic Devices, China(KF201901)

摘要/Abstract

摘要：

具有体积小、功耗低、灵敏度高、硅工艺兼容性好等优点的金属氧化物半导体(MOS)气体传感器现已广泛地应用于军事、科研和国民经济的各个领域。然而MOS传感器的低选择性阻碍了其在物联网(IoT)时代的应用前景。为此，本文综述了解决MOS传感器选择性的研究进展，主要介绍了敏感材料性能提升、电子鼻和热调制三种改善MOS传感器选择性的技术方法，阐述了三种方法目前所存在的问题及其未来的发展趋势。同时，本文还对比介绍了机器嗅觉领域主流的主成分分析(PCA)、线性判别分析(LDA)和神经网络(NN)模式识别/机器学习算法。最后，本综述展望了具有数据降维、特征提取和鲁棒性识别分类性能的卷积神经网络(CNN)深度学习算法在气体识别领域的应用前景。基于敏感材料性能的提升、多种调制手段与阵列技术的结合以及人工智能(AI)领域深度学习算法的最新进展，将会极大地增强非选择性MOS传感器的挥发性有机化合物(VOCs)分子识别能力。

关键词: 金属氧化物半导体, 气体传感器, 电子鼻, 热调制, 模式识别, 机器学习, 卷积神经网络

Abstract:

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.

Key words: Metal oxide semiconductor, Gas sensor, E-nose, Thermal modulation, Pattern recognition, Machine learning, Convolutional neural network

MSC2000:

O649

刘弘禹, 孟钢, 邓赞红, 李蒙, 常鋆青, 代甜甜, 方晓东. VOCs分子的半导体型传感器识别检测研究进展[J]. 物理化学学报, 2022, 38(5): 2008018.

Hongyu Liu, Gang Meng, Zanhong Deng, Meng Li, Junqing Chang, Tiantian Dai, Xiaodong Fang. Progress in Research on VOC Molecule Recognition by Semiconductor Sensors[J]. Acta Phys. -Chim. Sin., 2022, 38(5): 2008018.

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Method	Material	Target gas	Concentration (ppm)	Response	Year	Ref.
Metal particle modification	Zn/In₂O₃	Nitrogen dioxide	5	130	2020	51
Carbon nanocomposites/Quantum dots	Graphene-SnO₂/ZnO	Hydrogen sulfide	0.1	15.9	2020	46
Metal particle modification	Pt/WO₃	Hydrogen	150	100000	2020	52
Metal ion doping/Quantum dots	Al³⁺/ZnO	2-Chloroethyl ethyl sulfide	20	5393	2019	56
Quantum dots	ZnO	Ethanol	100	139.41	2019	42
Carbon nanocomposites/Quantum dots	Graphene/ZnO	Acetone	0.5	2	2019	47
Metal particle modification	Au/In₂O₃	Ethanol	100	11.12	2018	53
Quantum dots	TiO₂	Ammonia	0.2	2.13	2018	44
Carbon nanocomposites	rGO/SnO₂	Nitrogen dioxide	1	3.8	2018	48
Metal particle modification	Au/ZnO	Trimethylamine	30	65.8	2017	4
Metal particle modification	Pd/TiO₂	Ammonia	100	6.97	2017	54
Metal particle modification	Au/MoO₃	BTX	100	17.5–22.1	2017	55
Quantum dots	ZnO	Hydrogen sulfide	50	113.5	2017	44
Carbon nanocomposites	Graphene/SnO₂	Nitrogen dioxide	1–5	24.66–72.6	2017	49
Quantum dots	WO₃	Formaldehyde	100	1.6	2016	45
Carbon nanocomposites	Graphene/SnO₂	Nitrogen dioxide	20	9.5	2016	50

Application field	Objective	Technology	Algorithm	Year	Ref.
Food industry	Quality detection of pomegranate fruit	E-nose	LDA, BPNN, etc.	2020	102
Food industry	Freshness classification of Horse Mackerels	E-nose	LDA, etc.	2020	103
Food industry	Detection of fungal infection on storage Jasmine brown rice	E-nose	LDA, PCA, etc.	2020	104
Environment	Ethanol, formaldehyde, toluene, benzene, chlorobenzene	TM	LDA, PCA	2019	41
Medical care	Detection of lung cancer	E-nose	LDA, PCA, ANN, etc.	2018	101
Food industry	Identification of trace amounts of detergent powder	E-nose	LDA, PCA, etc.	2018	105
Food industry	Quality identification of milk adult-eration	E-nose	LDA, PCA, ANN	2018	106
Environment	Carbon monoxide, ammonia	E-nose	LDA, PCA, etc.	2018	107
Public security	Distinguish TNT from other chemicals with a similar structure	E-nose	LDA, PCA	2018	109
Agriculture	Characterization of different varieties of Chinese jujubes	E-nose	LDA, PCA	2018	111
Agriculture	Classification of cumin species	E-nose	LDA, PCA, etc.	2018	40
Food industry	Identification of tequila	E-nose	LDA, BPNN	2017	65
Environment	Methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, acetone, etc.	TM	LDA, PCA, etc.	2016	108
Agriculture	Quality control of essential oils	E-nose	LDA, etc.	2016	112
Public security	Trinitrotoluene (TNT) and ammonium nitrate	E-nose	LDA	2015	110
Environment	Methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, 2-butanol, etc.	TM	LDA, ANN	2015	85
Agriculture	Estimation of the age and amount	E-nose	LDA, PCA, BPNN, etc.	2014	113
Agriculture	Quality control of Lonicera Japonica stored for different period of time	E-nose	LDA, PCA, ANN	2014	114
Food industry	Six different herbs and spices	TM	LDA, PCA	2014	84

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VOCs分子的半导体型传感器识别检测研究进展

Progress in Research on VOC Molecule Recognition by Semiconductor Sensors

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