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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Bibliographic Details
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
Subjects:
Functionalization
Next-generation gas sensors
Pattern recognition algorithm
Sensor array
Pattern recognition algorithm
Next-generation gas sensors
Functionalization
Sensor array
ISSN:0169-4332
Online Access:Get full text
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Summary:[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.
ISSN:0169-4332
DOI:10.1016/j.apsusc.2025.162750