Optimized PANI@SnO2-Co3O4 ammonia sensor with UV/humidity-modulated response and machine learning-assisted early prediction
In this study, a novel ternary PANI@SnO2-Co3O4 nanocomposite was developed, that synergistically combines the functional advantages of its individual components, to achieve markedly enhanced NH3 sensing performance. At room temperature, the sensor exhibits a high response of 26.02–10 ppm NH3, with a...
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| Vydáno v: | Sensors and actuators. B, Chemical Ročník 449; s. 139171 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
15.02.2026
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| ISSN: | 0925-4005 |
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| Abstract | In this study, a novel ternary PANI@SnO2-Co3O4 nanocomposite was developed, that synergistically combines the functional advantages of its individual components, to achieve markedly enhanced NH3 sensing performance. At room temperature, the sensor exhibits a high response of 26.02–10 ppm NH3, with an ultralow theoretical detection limit of 0.122 ppm. It also shows excellent stability, with only a 1.2 % decrease in response peak over 25 days, making it a promising candidate for practical applications. Furthermore, the sensor demonstrates outstanding environmental adaptability, achieving higher response values and significantly faster response and recovery times under ultraviolet irradiation and moderate humidity (50 % RH). To enable real-time analysis, a Random Forest-based algorithm was implemented to accurately predict NH3 concentrations within the first 30 s of exposure. Overall, this work presents notable progress in NH3 sensing technology through innovative material design, environmental performance optimization, and intelligent prediction capabilities.
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•Ternary PANI@SnO2–Co3O4 nanocomposite enables high NH3 sensitivity.•Room-temperature NH3 sensor shows 26.02 response to 10 ppm, LOD 0.122 ppm.•Sensor remains stable with only 1.2 % signal loss over 25 days.•UV and 50 % RH enhance response magnitude and speed up recovery.•Random Forest enables NH3 prediction within 30 s using resistance data. |
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| AbstractList | In this study, a novel ternary PANI@SnO2-Co3O4 nanocomposite was developed, that synergistically combines the functional advantages of its individual components, to achieve markedly enhanced NH3 sensing performance. At room temperature, the sensor exhibits a high response of 26.02–10 ppm NH3, with an ultralow theoretical detection limit of 0.122 ppm. It also shows excellent stability, with only a 1.2 % decrease in response peak over 25 days, making it a promising candidate for practical applications. Furthermore, the sensor demonstrates outstanding environmental adaptability, achieving higher response values and significantly faster response and recovery times under ultraviolet irradiation and moderate humidity (50 % RH). To enable real-time analysis, a Random Forest-based algorithm was implemented to accurately predict NH3 concentrations within the first 30 s of exposure. Overall, this work presents notable progress in NH3 sensing technology through innovative material design, environmental performance optimization, and intelligent prediction capabilities.
[Display omitted]
•Ternary PANI@SnO2–Co3O4 nanocomposite enables high NH3 sensitivity.•Room-temperature NH3 sensor shows 26.02 response to 10 ppm, LOD 0.122 ppm.•Sensor remains stable with only 1.2 % signal loss over 25 days.•UV and 50 % RH enhance response magnitude and speed up recovery.•Random Forest enables NH3 prediction within 30 s using resistance data. |
| ArticleNumber | 139171 |
| Author | Zhang, Yizhe Qin, Yuxiang Lei, Jing Liu, Xinyi |
| Author_xml | – sequence: 1 givenname: Yuxiang surname: Qin fullname: Qin, Yuxiang email: qinyuxiang@tju.edu.cn organization: School of Microelectronics, Tianjin University, Tianjin 300072, China – sequence: 2 givenname: Yizhe surname: Zhang fullname: Zhang, Yizhe organization: School of Microelectronics, Tianjin University, Tianjin 300072, China – sequence: 3 givenname: Jing orcidid: 0009-0000-0538-2528 surname: Lei fullname: Lei, Jing organization: School of Microelectronics, Tianjin University, Tianjin 300072, China – sequence: 4 givenname: Xinyi surname: Liu fullname: Liu, Xinyi organization: School of Microelectronics, Tianjin University, Tianjin 300072, China |
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