Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 Nanoarchitectures
Abstract AI‐assisted electronic nose systems often emphasize sensitivity‐driven datasets, overlooking the comprehensive analysis of gaseous chemical attributes critical for precise gas identification. Conventional fabrication methods generate inconsistent datasets and focus primarily on improving cl...
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| Format: | Article |
| Language: | English |
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Wiley
2025-07-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202501293 |
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| author | Yun‐Haeng Cho Dong‐Su Kim Jung Hwan Seo Jae Han Chung Zion Park Ki Chang Kwon Jae‐Kwon Ko Tae Won Ha Jeong‐O Lee Gyu‐Li Kim Seong‐Jun Ro Hyojung Kim Chil‐Hyoung Lee Kwangjae Lee Young‐Seok Shim Donghwi Cho |
| author_facet | Yun‐Haeng Cho Dong‐Su Kim Jung Hwan Seo Jae Han Chung Zion Park Ki Chang Kwon Jae‐Kwon Ko Tae Won Ha Jeong‐O Lee Gyu‐Li Kim Seong‐Jun Ro Hyojung Kim Chil‐Hyoung Lee Kwangjae Lee Young‐Seok Shim Donghwi Cho |
| author_sort | Yun‐Haeng Cho |
| collection | DOAJ |
| description | Abstract AI‐assisted electronic nose systems often emphasize sensitivity‐driven datasets, overlooking the comprehensive analysis of gaseous chemical attributes critical for precise gas identification. Conventional fabrication methods generate inconsistent datasets and focus primarily on improving classification accuracy through deep learning, neglecting the fundamental role of sensor material design. This study addresses these challenges by developing a highly reliable sensor platform to standardize gas sensing for deep learning applications. Specifically, 1D SnO2 nanonetworks functionalized with Au and Pd nanocatalysts are fabricated via a systematic deposition process, enhancing gas diffusion and reaction kinetics. Stability improvements through controlled aging process reduce the coefficient of variation to below 5% across seven target gases: acetone, hydrogen, ethanol, carbon monoxide, propane, isoprene, and toluene. The platform exhibits exceptional deep learning performance, achieving over 99.5% classification accuracy using a residual network model, even in high‐humidity environments (up to 80% relative humidity) and at parts‐per‐trillion detection limits. This study highlights the synergy between nanostructure engineering and AI, establishing a robust framework for next‐generation bioinspired electronic nose systems with enhanced reliability and analytical capability. |
| format | Article |
| id | doaj-art-be5025ba14b34bbfae6e7886e769b0a9 |
| institution | DOAJ |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-be5025ba14b34bbfae6e7886e769b0a92025-08-20T03:15:35ZengWileyAdvanced Science2198-38442025-07-011225n/an/a10.1002/advs.202501293Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 NanoarchitecturesYun‐Haeng Cho0Dong‐Su Kim1Jung Hwan Seo2Jae Han Chung3Zion Park4Ki Chang Kwon5Jae‐Kwon Ko6Tae Won Ha7Jeong‐O Lee8Gyu‐Li Kim9Seong‐Jun Ro10Hyojung Kim11Chil‐Hyoung Lee12Kwangjae Lee13Young‐Seok Shim14Donghwi Cho15School of Energy Materials and Chemical Engineering Korea University of Technology and Education (KOREATECH) Cheonan 31253 Republic of KoreaNational Center for Nano Process & Equipments, Energy & Nano Technology Group Korea Institute of Industrial Technology (KITECH) Gwangju 61012 Republic of KoreaDepartment of Mechanical Engineering Hongik University Seoul 04066 Republic of KoreaSchool of Energy Materials and Chemical Engineering Korea University of Technology and Education (KOREATECH) Cheonan 31253 Republic of KoreaSchool of Energy Materials and Chemical Engineering Korea University of Technology and Education (KOREATECH) Cheonan 31253 Republic of KoreaDivision of Chemical and Material Metrology Korea Research Institute of Standards and Science (KRISS) Daejeon 34113 Republic of KoreaDivision of Chemical and Material Metrology Korea Research Institute of Standards and Science (KRISS) Daejeon 34113 Republic of KoreaNational Center for Nano Process & Equipments, Energy & Nano Technology Group Korea Institute of Industrial Technology (KITECH) Gwangju 61012 Republic of KoreaThin Film Materials Research Center Korea Research Institute of Chemical Technology (KRICT) Daejeon 34114 Republic of KoreaDepartment of AI Mobility Engineering Sangmyung University Cheonan 31066 Republic of KoreaDepartment of AI Mobility Engineering Sangmyung University Cheonan 31066 Republic of KoreaDepartment of Semiconductor Systems Engineering Sejong University Seoul 05006 Republic of KoreaNational Center for Nano Process & Equipments, Energy & Nano Technology Group Korea Institute of Industrial Technology (KITECH) Gwangju 61012 Republic of KoreaDepartment of AI Mobility Engineering Sangmyung University Cheonan 31066 Republic of KoreaSchool of Energy Materials and Chemical Engineering Korea University of Technology and Education (KOREATECH) Cheonan 31253 Republic of KoreaThin Film Materials Research Center Korea Research Institute of Chemical Technology (KRICT) Daejeon 34114 Republic of KoreaAbstract AI‐assisted electronic nose systems often emphasize sensitivity‐driven datasets, overlooking the comprehensive analysis of gaseous chemical attributes critical for precise gas identification. Conventional fabrication methods generate inconsistent datasets and focus primarily on improving classification accuracy through deep learning, neglecting the fundamental role of sensor material design. This study addresses these challenges by developing a highly reliable sensor platform to standardize gas sensing for deep learning applications. Specifically, 1D SnO2 nanonetworks functionalized with Au and Pd nanocatalysts are fabricated via a systematic deposition process, enhancing gas diffusion and reaction kinetics. Stability improvements through controlled aging process reduce the coefficient of variation to below 5% across seven target gases: acetone, hydrogen, ethanol, carbon monoxide, propane, isoprene, and toluene. The platform exhibits exceptional deep learning performance, achieving over 99.5% classification accuracy using a residual network model, even in high‐humidity environments (up to 80% relative humidity) and at parts‐per‐trillion detection limits. This study highlights the synergy between nanostructure engineering and AI, establishing a robust framework for next‐generation bioinspired electronic nose systems with enhanced reliability and analytical capability.https://doi.org/10.1002/advs.202501293artificial olfactory systemdeep learninggas sensorglancing angle depositionnanoarchitecturesSnO2 |
| spellingShingle | Yun‐Haeng Cho Dong‐Su Kim Jung Hwan Seo Jae Han Chung Zion Park Ki Chang Kwon Jae‐Kwon Ko Tae Won Ha Jeong‐O Lee Gyu‐Li Kim Seong‐Jun Ro Hyojung Kim Chil‐Hyoung Lee Kwangjae Lee Young‐Seok Shim Donghwi Cho Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 Nanoarchitectures Advanced Science artificial olfactory system deep learning gas sensor glancing angle deposition nanoarchitectures SnO2 |
| title | Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 Nanoarchitectures |
| title_full | Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 Nanoarchitectures |
| title_fullStr | Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 Nanoarchitectures |
| title_full_unstemmed | Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 Nanoarchitectures |
| title_short | Artificial Olfactory System Enabled by Ultralow Chemical Sensing Variations of 1D SnO2 Nanoarchitectures |
| title_sort | artificial olfactory system enabled by ultralow chemical sensing variations of 1d sno2 nanoarchitectures |
| topic | artificial olfactory system deep learning gas sensor glancing angle deposition nanoarchitectures SnO2 |
| url | https://doi.org/10.1002/advs.202501293 |
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