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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Bibliographic Details
Published in:Sensors and actuators. B, Chemical Vol. 449; p. 139171
Main Authors: Qin, Yuxiang, Zhang, Yizhe, Lei, Jing, Liu, Xinyi
Format: Journal Article
Language:English
Published: Elsevier B.V 15.02.2026
Subjects:
Gas sensors
Humidity effect
Machine learning algorithms
UV irradiation
Machine learning algorithms
UV irradiation
Humidity effect
Gas sensors
ISSN:0925-4005
Online Access:Get full text
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Summary: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.
ISSN:0925-4005
DOI:10.1016/j.snb.2025.139171