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Comparative study of CuO/Co3O4 external and CuO-Co3O4 internal heterojunctions: Do these factors always enhance gas-sensing performance?

Phuoc, P.H. and Viet, N.N. and Chien, N.V. and Van Hoang, N. and Hung, C.M. and Hoa, N.D. and Van Duy, N. and Hong, H.S. and Trung, D.D. and Van Hieu, N. (2023) Comparative study of CuO/Co3O4 external and CuO-Co3O4 internal heterojunctions: Do these factors always enhance gas-sensing performance? Sensors and Actuators B: Chemical, 384. ISSN 09254005

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Abstract

The nanoscale heterojunctions in the nanofiber (NF) structure have been widely utilized in semiconductor metal oxide-based gas sensors to optimize the sensing characteristics. In this study, the CuO-Co3O4 composite and CuO/Co3O4 mixed-NFs were synthesized to compare the influence level of internal- and external- junctions within NF on the gas sensing characteristics. The gas-sensing properties of four kinds of sensors based on NFs (CuO-Co3O4 composite, CuO/Co3O4 mixed, pristine CuO, and pristine Co3O4 NFs) were investigated in the concentration ranges of 0.1–1 ppm H2S reducing gas and 1–10 ppm NO2 oxidizing gas at different working temperatures from 250 °C to 450 °C. In addition, the response/recovery times, and the detection limits of NF sensors toward the target gases were explored. The evidence confirmed that both the internal and external junctions influenced the gas sensing performances for the reducing and oxidizing gases in two opposite trends. The response of hetero-junction NF sensors was increased to H2S gas while reduced to NO2 gas. Thermal fingerprint analysis using the machine learning algorithm was applied to strengthen the selection of the gases. The results confirmed the internal junction existing within NFs was mainly governing the gas sensing performance. © 2023 Elsevier B.V.

Item Type: Article
Divisions: Faculties > Faculty of Mechanical Engineering
Identification Number: 10.1016/j.snb.2023.133620
Uncontrolled Keywords: Chemical detection; Chemical sensors; Copper oxides; Gas detectors; Gas sensing electrodes; Gases; Learning algorithms; Machine learning; Nanofibers; Nitrogen oxides, Comparatives studies; Composite nanofibers; CuO-co3O4; Gas sensing; Gas-sensors; Mixed-nanofiber; Nanofiber sensors; Oxidizing gas; Reducing gas; Sensing performance, Heterojunctions
URI: http://eprints.lqdtu.edu.vn/id/eprint/10788

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