Machine-intelligent inkjet-printed α-Fe2O3/rGO towards NO2 quantification in ambient humidity

SENSORS AND ACTUATORS B-CHEMICAL

卷:321

Article

Wu, Tien-Chun¹  Dai, Jie²  Hu, Guohua^1,3  Yu, Wen-Bei^1,4  Ogbeide, Osarenkhoe¹  De Luca, Andrea⁵  Huang, Xiao²  Su, Bao-Lian⁴  Li, Yu⁴  Udrea, Florin⁵  Hasan, Tawfique¹ 

[1] Univ Cambridge, Cambridge Graphene Ctr, Cambridge CB3 0FA, England

[2] Nanjing Tech Univ, Inst Adv Mat, Nanjing 210009, Peoples R China

[3] Chinese Univ Hong Kong, Dept Elect Engn, Shatin, Hong Kong, Peoples R China

[4] Wuhan Univ Technol, State Key Lab Adv Technol Mat Synth & Proc, Wuhan 430070, Hubei, Peoples R China

[5] Univ Cambridge, Dept Engn, Cambridge CB3 0FA, England

关键词: Inkjet on CMOS;    Temperature modulation;    Machine learning;    Cluster analysis;    Principal component analysis;    Factor analysis;    Electronic nose;   

DOI  :  10.1016/j.snb.2020.128446

来源: Elsevier

【 摘 要 】

Metal oxides (MOx) represent one of the most investigated chemiresistive gas sensing platforms in spite of the challenges in selectivity to analytes and interference from humidity (RH). While selectivity is traditionally improved by cross-referencing sensor arrays, interferences from humidity (RH) in ambient environment, to which the majority of the MOx materials are susceptible, cannot be inherently quantified. For standalone MOx sensors, it is therefore difficult to discriminate responses from analytes and humidity. We develop a framework which employs temperature modulation (TM) algorithms and machine learning (ML) approaches using principal component analysis (PCA) and cluster analysis of transient features, to quantify NO2 concentrations under specific RH conditions. With a single inkjet-printed MOx/reduced graphene oxide (rGO) complementary metal oxide-semiconductor (CMOS)-integrated sensor, we achieve an overall discrimination accuracy of 97.3%. Our approach may enable the development of predictive systems for humidity sensitive sensors under ambient moisture conditions, towards the realisation of low-power, miniaturised adaptive air quality monitoring.

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