Enhancing selectivity and sensitivity in gas sensors through noble metal-decorated ZnO and machine learning

[Display omitted] •ZnO thin films with Ir, Ru, and IrRu alloys via ALD were explored for hazardous gas detection with enhanced performance.•Noble metals improved gas-sensing via electronic and chemical sensitization, ensuring stable, reliable, and selective responses.•Surface-engineered metal oxide...

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Published in:Applied surface science Vol. 693; p. 162750
Main Authors: Kwon, Yeong Min, Son, Yeseul, Lee, Do Hyung, Lim, Min Hyeok, Han, Jin Kyu, Jang, Moonjeong, Park, Seoungwoong, Kang, Saewon, Yim, Soonmin, Myung, Sung, Lim, Jongsun, Lee, Sun Sook, Bae, Garam, Kim, Soo-Hyun, Song, Wooseok
Format: Journal Article
Language:English
Published: Elsevier B.V 01.06.2025
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ISSN:0169-4332
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Abstract [Display omitted] •ZnO thin films with Ir, Ru, and IrRu alloys via ALD were explored for hazardous gas detection with enhanced performance.•Noble metals improved gas-sensing via electronic and chemical sensitization, ensuring stable, reliable, and selective responses.•Surface-engineered metal oxide gas sensors exhibited exceptional stability, reliability, and selectivity in gas detection.•Machine learning-based data analysis enabled 100% accurate classification, enhancing gas selectivity in metal oxide sensors. The growing need for highly sensitive and selective gas sensors has spurred extensive research on enhancing metal–oxide–semiconductor-based sensors. In this study, we explored the gas-sensing performance of ZnO thin films functionalized with noble metals (Ir, Ru, and IrRu alloys) via atomic layer deposition for the detection of hazardous gases. The incorporation of noble metals led to significant improvements in the gas-sensing behavior driven by both electronic and chemical sensitization mechanisms. To further enhance gas selectivity, machine learning-based data analysis was employed, enabling precise classification of various gases with 100 % accuracy. These findings underscore the potential of noble metal-functionalized ZnO sensors for advanced gas detection, illustrating the effective combination of material engineering and cutting-edge data analysis techniques for the development of intelligent, selective, and stable gas sensor platforms.
AbstractList [Display omitted] •ZnO thin films with Ir, Ru, and IrRu alloys via ALD were explored for hazardous gas detection with enhanced performance.•Noble metals improved gas-sensing via electronic and chemical sensitization, ensuring stable, reliable, and selective responses.•Surface-engineered metal oxide gas sensors exhibited exceptional stability, reliability, and selectivity in gas detection.•Machine learning-based data analysis enabled 100% accurate classification, enhancing gas selectivity in metal oxide sensors. The growing need for highly sensitive and selective gas sensors has spurred extensive research on enhancing metal–oxide–semiconductor-based sensors. In this study, we explored the gas-sensing performance of ZnO thin films functionalized with noble metals (Ir, Ru, and IrRu alloys) via atomic layer deposition for the detection of hazardous gases. The incorporation of noble metals led to significant improvements in the gas-sensing behavior driven by both electronic and chemical sensitization mechanisms. To further enhance gas selectivity, machine learning-based data analysis was employed, enabling precise classification of various gases with 100 % accuracy. These findings underscore the potential of noble metal-functionalized ZnO sensors for advanced gas detection, illustrating the effective combination of material engineering and cutting-edge data analysis techniques for the development of intelligent, selective, and stable gas sensor platforms.
ArticleNumber 162750
Author Lim, Min Hyeok
Han, Jin Kyu
Park, Seoungwoong
Lim, Jongsun
Kwon, Yeong Min
Song, Wooseok
Kang, Saewon
Kim, Soo-Hyun
Lee, Sun Sook
Jang, Moonjeong
Myung, Sung
Lee, Do Hyung
Yim, Soonmin
Bae, Garam
Son, Yeseul
Author_xml – sequence: 1
  givenname: Yeong Min
  surname: Kwon
  fullname: Kwon, Yeong Min
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
– sequence: 2
  givenname: Yeseul
  surname: Son
  fullname: Son, Yeseul
  organization: School of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
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  givenname: Do Hyung
  surname: Lee
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  givenname: Min Hyeok
  surname: Lim
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  givenname: Jin Kyu
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  givenname: Moonjeong
  surname: Jang
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  organization: National Nano Fab Center (NNFC), Daejeon 34141, Republic of Korea
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  givenname: Seoungwoong
  orcidid: 0000-0002-5958-4050
  surname: Park
  fullname: Park, Seoungwoong
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
– sequence: 8
  givenname: Saewon
  surname: Kang
  fullname: Kang, Saewon
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
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  givenname: Soonmin
  surname: Yim
  fullname: Yim, Soonmin
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
– sequence: 10
  givenname: Sung
  surname: Myung
  fullname: Myung, Sung
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
– sequence: 11
  givenname: Jongsun
  surname: Lim
  fullname: Lim, Jongsun
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
– sequence: 12
  givenname: Sun Sook
  surname: Lee
  fullname: Lee, Sun Sook
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
– sequence: 13
  givenname: Garam
  surname: Bae
  fullname: Bae, Garam
  email: grbae@dankook.ac.kr
  organization: Department of Physics, Dankook University, Cheonan 31116, Republic of Korea
– sequence: 14
  givenname: Soo-Hyun
  orcidid: 0000-0003-3748-2755
  surname: Kim
  fullname: Kim, Soo-Hyun
  email: soohyunsq@unist.ac.kr
  organization: School of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
– sequence: 15
  givenname: Wooseok
  orcidid: 0000-0002-0487-2055
  surname: Song
  fullname: Song, Wooseok
  email: wssong@krict.re.kr
  organization: Thin Film Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea
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Keywords Pattern recognition algorithm
Next-generation gas sensors
Functionalization
Sensor array
Language English
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Snippet [Display omitted] •ZnO thin films with Ir, Ru, and IrRu alloys via ALD were explored for hazardous gas detection with enhanced performance.•Noble metals...
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StartPage 162750
SubjectTerms Functionalization
Next-generation gas sensors
Pattern recognition algorithm
Sensor array
Title Enhancing selectivity and sensitivity in gas sensors through noble metal-decorated ZnO and machine learning
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