物理化学学报 >> 2022, Vol. 38 >> Issue (5): 2008018.doi: 10.3866/PKU.WHXB202008018
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,*()
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基金资助:
Hongyu Liu1,2, Gang Meng1,*(), Zanhong Deng1, Meng Li1,2, Junqing Chang1,2, Tiantian Dai1,2, Xiaodong Fang1,*()
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.)Supported by:
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。
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. doi: 10.3866/PKU.WHXB202008018
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. doi: 10.3866/PKU.WHXB202008018
表1
MOS材料气敏性能提升的主要方法"
Method | Material | Target gas | Concentration (ppm) | Response | Year | Ref. |
Metal particle modification | Zn/In2O3 | Nitrogen dioxide | 5 | 130 | 2020 | |
Carbon nanocomposites/Quantum dots | Graphene-SnO2/ZnO | Hydrogen sulfide | 0.1 | 15.9 | 2020 | |
Metal particle modification | Pt/WO3 | Hydrogen | 150 | 100000 | 2020 | |
Metal ion doping/Quantum dots | Al3+/ZnO | 2-Chloroethyl ethyl sulfide | 20 | 5393 | 2019 | |
Quantum dots | ZnO | Ethanol | 100 | 139.41 | 2019 | |
Carbon nanocomposites/Quantum dots | Graphene/ZnO | Acetone | 0.5 | 2 | 2019 | |
Metal particle modification | Au/In2O3 | Ethanol | 100 | 11.12 | 2018 | |
Quantum dots | TiO2 | Ammonia | 0.2 | 2.13 | 2018 | |
Carbon nanocomposites | rGO/SnO2 | Nitrogen dioxide | 1 | 3.8 | 2018 | |
Metal particle modification | Au/ZnO | Trimethylamine | 30 | 65.8 | 2017 | |
Metal particle modification | Pd/TiO2 | Ammonia | 100 | 6.97 | 2017 | |
Metal particle modification | Au/MoO3 | BTX | 100 | 17.5–22.1 | 2017 | |
Quantum dots | ZnO | Hydrogen sulfide | 50 | 113.5 | 2017 | |
Carbon nanocomposites | Graphene/SnO2 | Nitrogen dioxide | 1–5 | 24.66–72.6 | 2017 | |
Quantum dots | WO3 | Formaldehyde | 100 | 1.6 | 2016 | |
Carbon nanocomposites | Graphene/SnO2 | Nitrogen dioxide | 20 | 9.5 | 2016 |
表2
主成分分析在气体识别中的主要应用"
Application field | Objective | Technology | Algorithm | Year | Ref. |
Medical care | Diagnosis of lung cancer via exhaled breath | E-nose | PCA | 2020 | |
Food industry | Identification of food volatile organic compounds | E-nose | PCA | 2019 | |
Environment | Nitric oxide, nitrogen dioxide, methane, propane | TM | PCA | 2018 | |
Food industry | Peach | E-nose | PCA | 2018 | |
Agriculture | The assessment of the ripening process of watermelon | E-nose | PCA | 2018 | |
Agriculture | Soil pollution detection | E-nose | PCA | 2018 | |
Medical care | Real breath analysis for the diagnosis of halitosis | E-nose | PCA | 2017 | |
Agriculture | Evaluate the aroma fingerprints of olive oil | E-nose | PCA | 2017 | |
Public security | Detection of 8 kinds of explosives | E-nose | PCA | 2016 | |
Agriculture | Improving recognition of odors | E-nose | PCA | 2016 | |
Agriculture | Firm the aromatic characteristics | E-nose | PCA | 2014 | |
Food industry | Quality identification of milk adult-eration | E-nose | PCA | 2013 | |
Agriculture | Sensory testing to assess flavor quality of white pepper | E-nose | PCA | 2013 | |
Environment | Methanol, ethanol, 1-butanol | TM | PCA | 2013 | |
Public security | DMMP detection by PEDOT nanotubes | E-nose | PCA | 2012 | |
Environment | Methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, etc. | TM | PCA | 2012 | |
Environment | Carbon monoxide, nitric oxide, nitrobenzene | TM | PCA | 2010 |
表3
线性判别分析在气体识别中的主要应用"
Application field | Objective | Technology | Algorithm | Year | Ref. |
Food industry | Quality detection of pomegranate fruit | E-nose | LDA, BPNN, etc. | 2020 | |
Food industry | Freshness classification of Horse Mackerels | E-nose | LDA, etc. | 2020 | |
Food industry | Detection of fungal infection on storage Jasmine brown rice | E-nose | LDA, PCA, etc. | 2020 | |
Environment | Ethanol, formaldehyde, toluene, benzene, chlorobenzene | TM | LDA, PCA | 2019 | |
Medical care | Detection of lung cancer | E-nose | LDA, PCA, ANN, etc. | 2018 | |
Food industry | Identification of trace amounts of detergent powder | E-nose | LDA, PCA, etc. | 2018 | |
Food industry | Quality identification of milk adult-eration | E-nose | LDA, PCA, ANN | 2018 | |
Environment | Carbon monoxide, ammonia | E-nose | LDA, PCA, etc. | 2018 | |
Public security | Distinguish TNT from other chemicals with a similar structure | E-nose | LDA, PCA | 2018 | |
Agriculture | Characterization of different varieties of Chinese jujubes | E-nose | LDA, PCA | 2018 | |
Agriculture | Classification of cumin species | E-nose | LDA, PCA, etc. | 2018 | |
Food industry | Identification of tequila | E-nose | LDA, BPNN | 2017 | |
Environment | Methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, acetone, etc. | TM | LDA, PCA, etc. | 2016 | |
Agriculture | Quality control of essential oils | E-nose | LDA, etc. | 2016 | |
Public security | Trinitrotoluene (TNT) and ammonium nitrate | E-nose | LDA | 2015 | |
Environment | Methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, 2-butanol, etc. | TM | LDA, ANN | 2015 | |
Agriculture | Estimation of the age and amount | E-nose | LDA, PCA, BPNN, etc. | 2014 | |
Agriculture | Quality control of Lonicera Japonica stored for different period of time | E-nose | LDA, PCA, ANN | 2014 | |
Food industry | Six different herbs and spices | TM | LDA, PCA | 2014 |
表4
人工神经网络在气体识别中的主要应用"
Application field | Objective | Technology | Algorithm | Year | Ref. |
Food industry | Quality detection of pomegranate fruit | E-nose | BPNN,LDA, etc. | 2020 | |
Food industry | Freshness of dried lycium fruit | E-nose | BPNN, PCA, etc. | 2020 | |
Food industry | An application for beef quality monitoring | E-nose | ANN, etc. | 2019 | |
Medical care | The detection of gastric | E-nose | ANN | 2018 | |
Medical care | Detection of lung cancer | E-nose | ANN, PCA, LDA, etc. | 2018 | |
Food industry | Predict human assessment of odors from common dairy operations | E-nose | ANN | 2018 | |
Food industry | Quality identification of milk adult-eration | E-nose | ANN, PCA, LDA | 2018 | |
Food industry | Quantification of wine mixtures | E-nose | ANN, etc. | 2018 | |
Environment | Carbon monoxide, methane | E-nose | ANN, etc. | 2018 | |
Food industry | Characterize and classify 7 Chinese robusta coffee cultivars | E-nose | BPNN, PCA, etc. | 2017 | |
Food industry | Evaluation of lipid oxidation of Chinese-style sausage | E-nose | ANN, etc. | 2017 | |
Environment | Toluene, xylene, ethanol | E-nose | ANN | 2017 | |
Food industry | Identification of tequila | E-nose | BPNN, LDA | 2017 | |
Environment | Acetone, toluene, hydrogen peroxide, benzene dichloromethane, etc. | E-nose | ANN, PCA | 2016 | |
Agriculture | Discrimination of soil under different nutrient addition | E-nose | BPNN, PCA | 2016 | |
Environment | Methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, 2-butanol, etc. | TM | ANN, LDA | 2015 | |
Food industry | Measurement of total volatile basic nitrogen in pork meat | E-nose | BPNN, PCA | 2014 | |
Agriculture | Estimation of the age and amount | E-nose | BPNN, PCA, LDA, etc. | 2014 |
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