4.7 Article

Chemiresistive sensor based on hollow Fe2O3 octahedrons incorporated into porous In2O3 nanofibers for enhanced sensing performance and recognition toward triethylamine

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SENSORS AND ACTUATORS B-CHEMICAL
卷 393, 期 -, 页码 -

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ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2023.134129

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Electrospinning; Heterojunction; Working temperature modulation

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In this study, hollow Fe2O3 octahedrons incorporated into porous In2O3 nanofibers (Fe2O3@In2O3) have been prepared using the electrospinning method, followed by thermal oxidation. The gas sensing properties of the prepared nanofibers, such as high response and selectivity towards triethylamine (TEA) with good stability, have been investigated. The dynamic sensing behaviors of the nanofibers under the modulation of working temperature by triangular wave heating voltage have also been studied.
The performance of chemiresistive gas sensors greatly depends on the sensing materials. Till now, it is still a hot topic to develop high-performance sensing materials. In this study, hollow Fe2O3 octahedrons incorporated into porous In2O3 nanofibers (Fe2O3@In2O3) have been prepared using the electrospinning method, followed by thermal oxidation. Through the electrospinning technique, MIL-101(Fe) octahedrons are incorporated as sacri-ficial templates into the precursor nanofibers containing In3+. The precursor nanofibers are transformed into porous In2O3 nanofibers by calcination in air, and MIL-101(Fe) are simultaneously changed into hollow Fe2O3 octahedrons, forming Fe2O3@In2O3 nanofibers. Considering their unique morphology, the gas sensing properties of the prepared nanofibers are investigated. The results demonstrate that they exhibit high response and selectivity toward triethylamine (TEA) with a good stability. Furthermore, their dynamic sensing behaviors are investigated under the modulation of working temperature by triangular wave heating voltage. According to their characteristic response curves, TEA can be effectively discriminated among all the detected gases in combination with a simple principal component analysis (PCA)-based recognition algorithm. Additionally, TEA can be quantitatively analyzed through the relationship between its concentration and the response at the characteristic point of the response curves.

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