Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model
Abstract Detecting individual gases with various sensors is a well‐established field in gas sensing. However, substantial challenges and opportunities remain in the simultaneous detection and classification of multiple gases. Artificial intelligence (AI) integrated gas sensor systems effectively ena...
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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.202500412 |
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| author | Tianci Liu Yun Ji Hwang Lu Zhang Jongwoo Hong Teajong Hwang Seong Chan Jun |
| author_facet | Tianci Liu Yun Ji Hwang Lu Zhang Jongwoo Hong Teajong Hwang Seong Chan Jun |
| author_sort | Tianci Liu |
| collection | DOAJ |
| description | Abstract Detecting individual gases with various sensors is a well‐established field in gas sensing. However, substantial challenges and opportunities remain in the simultaneous detection and classification of multiple gases. Artificial intelligence (AI) integrated gas sensor systems effectively enable multi‐gas detection using specialized algorithms. Nevertheless, these algorithms are prone to overfitting owing to their high model complexity; this study proposes a sensor array that engineers carbon vacancies in graphene oxide via metal ion doping and high‐temperature reduction, enabling high‐sensitivity, simultaneous detection of various gases at low temperatures (20 °C). By integrating an advanced artificial intelligence framework, the acquired electrical signals are transformed, and a multi‐task learning (MTL) approach is applied to achieve instantaneous identification of four gas types and four‐level concentrations. The proposed MTL framework demonstrates superior performance by effectively mitigating overfitting and improving generalization through feature sharing and mutual regularization between gas type classification and concentration estimation tasks. Experimental validation on vehicle exhaust gas fault diagnosis highlights the method's effectiveness and applicability in complex conditions, achieving 98.22% accuracy and 48% faster inference compared to traditional single‐task models. This study provides a basis for developing more intelligent and adaptable sensor systems capable. |
| format | Article |
| id | doaj-art-c5bfcdf26bdb4b2e884f36aa37acd11c |
| institution | DOAJ |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-c5bfcdf26bdb4b2e884f36aa37acd11c2025-08-20T02:43:09ZengWileyAdvanced Science2198-38442025-07-011225n/an/a10.1002/advs.202500412Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task ModelTianci Liu0Yun Ji Hwang1Lu Zhang2Jongwoo Hong3Teajong Hwang4Seong Chan Jun5School of Mechanical Engineering Yonsei University 50, Yonsei‐ro, Seodaemun‐gu Seoul 03722 Republic of KoreaSchool of Mechanical Engineering Yonsei University 50, Yonsei‐ro, Seodaemun‐gu Seoul 03722 Republic of KoreaSchool of Electrical and Electronic Engineering Yonsei University 50, Yonsei‐ro, Seodaemun‐gu Seoul 03722 Republic of KoreaSchool of Mechanical Engineering Yonsei University 50, Yonsei‐ro, Seodaemun‐gu Seoul 03722 Republic of KoreaSchool of Mechanical Engineering Yonsei University 50, Yonsei‐ro, Seodaemun‐gu Seoul 03722 Republic of KoreaSchool of Mechanical Engineering Yonsei University 50, Yonsei‐ro, Seodaemun‐gu Seoul 03722 Republic of KoreaAbstract Detecting individual gases with various sensors is a well‐established field in gas sensing. However, substantial challenges and opportunities remain in the simultaneous detection and classification of multiple gases. Artificial intelligence (AI) integrated gas sensor systems effectively enable multi‐gas detection using specialized algorithms. Nevertheless, these algorithms are prone to overfitting owing to their high model complexity; this study proposes a sensor array that engineers carbon vacancies in graphene oxide via metal ion doping and high‐temperature reduction, enabling high‐sensitivity, simultaneous detection of various gases at low temperatures (20 °C). By integrating an advanced artificial intelligence framework, the acquired electrical signals are transformed, and a multi‐task learning (MTL) approach is applied to achieve instantaneous identification of four gas types and four‐level concentrations. The proposed MTL framework demonstrates superior performance by effectively mitigating overfitting and improving generalization through feature sharing and mutual regularization between gas type classification and concentration estimation tasks. Experimental validation on vehicle exhaust gas fault diagnosis highlights the method's effectiveness and applicability in complex conditions, achieving 98.22% accuracy and 48% faster inference compared to traditional single‐task models. This study provides a basis for developing more intelligent and adaptable sensor systems capable.https://doi.org/10.1002/advs.202500412deep leaninggas sensorsgraphene oxidesmetal oxidesmulti‐task classificationsensor arrays |
| spellingShingle | Tianci Liu Yun Ji Hwang Lu Zhang Jongwoo Hong Teajong Hwang Seong Chan Jun Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model Advanced Science deep leaning gas sensors graphene oxides metal oxides multi‐task classification sensor arrays |
| title | Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model |
| title_full | Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model |
| title_fullStr | Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model |
| title_full_unstemmed | Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model |
| title_short | Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model |
| title_sort | hybrid series of carbon vacancy electrodes for multi chemical vapors diagnosis using a residual multi task model |
| topic | deep leaning gas sensors graphene oxides metal oxides multi‐task classification sensor arrays |
| url | https://doi.org/10.1002/advs.202500412 |
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