CUED Publications database

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

Wu, T and Dai, J and Hu, G and Yu, W-B and Ogbeide, O and De Luca, A and Huang, X and Su, B-L and Li, Y and Udrea, F and Hasan, T Machine-intelligent inkjet-printed α-Fe2O3/rGO towards NO2 quantification in ambient humidity. Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers. ISSN 0925-4005 (Unpublished)

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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 se- lectivity 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, can- not be inherently quantified. For standalone MOx sensors, it is therefore difficult to discriminate responses from analytes and humidity. We develop a strategy which employs temperature modu- lation (TM) algorithms and machine learning (ML) approaches using principal component analy- sis (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 ac- curacy 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, minia- turised adaptive air quality monitoring.

Item Type: Article
Depositing User: Cron Job
Date Deposited: 19 Jun 2020 21:14
Last Modified: 25 Feb 2021 06:17