Directly electrospun copper ferrite CuFe2O4 nanofiber-based for gas classification

IF 1.7 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Advances in Natural Sciences: Nanoscience and Nanotechnology Pub Date : 2024-05-27 DOI:10.1088/2043-6262/ad4850
Hong Phuoc Phan, Van Hoang Nguyen, Ngoc-Viet Nguyen and Van Hieu Nguyen
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Abstract

The cross-response is a considerable primary challenge of gas sensors based on semiconducting metal oxide (SMO), especially in detecting and classifying gases with comparable properties. In this work, the copper ferrite (CuFe2O4, CFO) nanofibers (NFs)-based sensors were straightforwardly synthesised by electrospinning technique. The morphology of the CFO NFs was observed using scanning electron microscopy (SEM), which revealed a rough surface with a diameter of approximately 80 nm. The composition of the fiber was confirmed by energy dispersive spectroscopy (EDS), which showed the fiber’s chemical elements to include Cu, Fe, and O. The microstructural characteristics of the CFO NFs were analysed using x-ray diffraction (XRD) and Raman spectroscopy, confirming the characteristic peaks of the CFO phase. The gas sensing characteristics of CFO-based sensors have been examined to 25−200 ppm of various gases of (CH3)2CO, CH3CH2OH, NH3, and H2 at a function of working temperature of 350−450 °C. The gas-sensing mechanism of the sensor based on CFO NFs is explained by the surface depletion layer and the grain boundary model. The successful categorisation of these gases into distinct groups was realised, indicating that the issue of cross-response caused by interfering gases was effectively addressed with the aid of an artificial intelligence algorithm.
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用于气体分级的直接电纺铜铁氧体 CuFe2O4 纳米纤维
交叉反应是基于半导体金属氧化物(SMO)的气体传感器面临的主要挑战,尤其是在检测和分类具有相似性质的气体时。在这项研究中,利用电纺丝技术直接合成了基于铜铁氧体(CuFe2O4,CFO)纳米纤维(NFs)的传感器。使用扫描电子显微镜(SEM)观察了 CFO 纳米纤维的形态,发现其表面粗糙,直径约为 80 纳米。使用 X 射线衍射 (XRD) 和拉曼光谱分析了 CFO 无纺布的微观结构特征,确认了 CFO 相的特征峰。在 350-450 °C 的工作温度下,研究了基于 CFO 的传感器对 25-200 ppm 的 (CH3)2CO、CH3CH2OH、NH3 和 H2 等各种气体的传感特性。基于 CFO NFs 的传感器的气体传感机制是通过表面耗尽层和晶界模型来解释的。成功地将这些气体划分为不同的组别,表明借助人工智能算法有效地解决了干扰气体引起的交叉反应问题。
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Advances in Natural Sciences: Nanoscience and Nanotechnology
Advances in Natural Sciences: Nanoscience and Nanotechnology NANOSCIENCE & NANOTECHNOLOGYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
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