Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification
Smart gas identification is vital in medical, environmental, and manufacturing fields. However, traditional gas‐sensing technologies either lack portability or demand high working temperatures, devoid of in situ and instant sensing capability. In this work, Pd‐Au/MXene sensors with excellent gas‐sen...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-07-01
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| Series: | Small Structures |
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| Online Access: | https://doi.org/10.1002/sstr.202400619 |
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| author | Yiheng Chen Jiawang Hu Nanlin Hu Shikai Wu Yuan Lu |
| author_facet | Yiheng Chen Jiawang Hu Nanlin Hu Shikai Wu Yuan Lu |
| author_sort | Yiheng Chen |
| collection | DOAJ |
| description | Smart gas identification is vital in medical, environmental, and manufacturing fields. However, traditional gas‐sensing technologies either lack portability or demand high working temperatures, devoid of in situ and instant sensing capability. In this work, Pd‐Au/MXene sensors with excellent gas‐sensing properties are fabricated utilizing an in situ growth strategy. A bionic sensor array is developed and further integrated into an instant and in situ sensing platform (IISP). Moreover, machine learning (ML) algorithms are employed to strengthen IISP's capacity for gas identification in complex application scenarios. Owing to the electron sensitization and catalysis function of noble metal sites, the Pd‐Au/MXene nanocomposite demonstrates enhanced gas‐sensing characteristics, with a response up to 2.73 times the response of pristine Ti3C2Tx and a response speed 1.81 times as the pristine Ti3C2Tx. In addition, the sensor array successfully distinguishes 14 odor molecules common in life by pattern recognition algorithms. Eventually, with the assistance of ML, the IISP exhibits 89.2% accuracy in detecting different food odors. Also, it achieves 92.0% accuracy in identifying the breath odor of healthy individuals and gastric cancer patients. In all, a portable, cost‐effective, and high‐performance IISP is established as a prototype, providing a promising solution for versatile gas‐sensing application scenarios. |
| format | Article |
| id | doaj-art-0e9045422faa4f8ab00a40dc689fa831 |
| institution | Kabale University |
| issn | 2688-4062 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Small Structures |
| spelling | doaj-art-0e9045422faa4f8ab00a40dc689fa8312025-08-20T03:28:40ZengWiley-VCHSmall Structures2688-40622025-07-0167n/an/a10.1002/sstr.202400619Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas IdentificationYiheng Chen0Jiawang Hu1Nanlin Hu2Shikai Wu3Yuan Lu4Department of Chemical Engineering Tsinghua University Beijing 100084 ChinaDepartment of Chemical Engineering Tsinghua University Beijing 100084 ChinaDepartment of Medical Oncology Peking University First Hospital Beijing 100034 ChinaDepartment of Medical Oncology Peking University First Hospital Beijing 100034 ChinaDepartment of Chemical Engineering Tsinghua University Beijing 100084 ChinaSmart gas identification is vital in medical, environmental, and manufacturing fields. However, traditional gas‐sensing technologies either lack portability or demand high working temperatures, devoid of in situ and instant sensing capability. In this work, Pd‐Au/MXene sensors with excellent gas‐sensing properties are fabricated utilizing an in situ growth strategy. A bionic sensor array is developed and further integrated into an instant and in situ sensing platform (IISP). Moreover, machine learning (ML) algorithms are employed to strengthen IISP's capacity for gas identification in complex application scenarios. Owing to the electron sensitization and catalysis function of noble metal sites, the Pd‐Au/MXene nanocomposite demonstrates enhanced gas‐sensing characteristics, with a response up to 2.73 times the response of pristine Ti3C2Tx and a response speed 1.81 times as the pristine Ti3C2Tx. In addition, the sensor array successfully distinguishes 14 odor molecules common in life by pattern recognition algorithms. Eventually, with the assistance of ML, the IISP exhibits 89.2% accuracy in detecting different food odors. Also, it achieves 92.0% accuracy in identifying the breath odor of healthy individuals and gastric cancer patients. In all, a portable, cost‐effective, and high‐performance IISP is established as a prototype, providing a promising solution for versatile gas‐sensing application scenarios.https://doi.org/10.1002/sstr.202400619bionic sensor arraysinstant and in situ gas sensingmachine learningnoninvasive diagnosis for gastric cancerPd‐Au/MXene nanocomposites |
| spellingShingle | Yiheng Chen Jiawang Hu Nanlin Hu Shikai Wu Yuan Lu Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification Small Structures bionic sensor arrays instant and in situ gas sensing machine learning noninvasive diagnosis for gastric cancer Pd‐Au/MXene nanocomposites |
| title | Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification |
| title_full | Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification |
| title_fullStr | Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification |
| title_full_unstemmed | Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification |
| title_short | Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification |
| title_sort | machine learning assisted pd au mxene sensor array for smart gas identification |
| topic | bionic sensor arrays instant and in situ gas sensing machine learning noninvasive diagnosis for gastric cancer Pd‐Au/MXene nanocomposites |
| url | https://doi.org/10.1002/sstr.202400619 |
| work_keys_str_mv | AT yihengchen machinelearningassistedpdaumxenesensorarrayforsmartgasidentification AT jiawanghu machinelearningassistedpdaumxenesensorarrayforsmartgasidentification AT nanlinhu machinelearningassistedpdaumxenesensorarrayforsmartgasidentification AT shikaiwu machinelearningassistedpdaumxenesensorarrayforsmartgasidentification AT yuanlu machinelearningassistedpdaumxenesensorarrayforsmartgasidentification |