Hong Phuoc Phan, Van Hoang Nguyen, Ngoc-Viet Nguyen and Van Hieu Nguyen
{"title":"用于气体分级的直接电纺铜铁氧体 CuFe2O4 纳米纤维","authors":"Hong Phuoc Phan, Van Hoang Nguyen, Ngoc-Viet Nguyen and Van Hieu Nguyen","doi":"10.1088/2043-6262/ad4850","DOIUrl":null,"url":null,"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.","PeriodicalId":7359,"journal":{"name":"Advances in Natural Sciences: Nanoscience and Nanotechnology","volume":"129 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Directly electrospun copper ferrite CuFe2O4 nanofiber-based for gas classification\",\"authors\":\"Hong Phuoc Phan, Van Hoang Nguyen, Ngoc-Viet Nguyen and Van Hieu Nguyen\",\"doi\":\"10.1088/2043-6262/ad4850\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":7359,\"journal\":{\"name\":\"Advances in Natural Sciences: Nanoscience and Nanotechnology\",\"volume\":\"129 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Natural Sciences: Nanoscience and Nanotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2043-6262/ad4850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Natural Sciences: Nanoscience and Nanotechnology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2043-6262/ad4850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Directly electrospun copper ferrite CuFe2O4 nanofiber-based for gas classification
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.