{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T06:03:27Z","timestamp":1789625007327,"version":"build-2803163510"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31927802"],"award-info":[{"award-number":["31927802"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Science"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Gas monitoring is a promising method for early warning of thermal runaway, yet existing sensor systems suffer from insufficient miniaturization, inadequate discrimination ability, and severe drift\u2010induced performance degradation. This work presents a 2\u00a0\u00d7\u00a02 integrated single\u2010chip MEMS gas sensor array with temperature modulation and deep learning for discriminating thermal\u2010runaway characteristic gases. The array comprises four distinct temperature zones achieved by varying the line width of the heating electrodes, with the entire array measuring 1.8\u00a0\u00d7\u00a01.8\u00a0\u00d7\u00a00.5 mm\n                    <jats:sup>3<\/jats:sup>\n                    . Furthermore, it is capable of detecting four critical thermal\u2010runaway characteristic gases (H\n                    <jats:sub>2<\/jats:sub>\n                    , CO, CH\n                    <jats:sub>4<\/jats:sub>\n                    , and CO\n                    <jats:sub>2<\/jats:sub>\n                    ) and exhibits distinct temperature\u2010modulated response patterns. To achieve reliable multi\u2010gas discrimination, we develop a Hierarchical Kolmogorov\u2010Arnold Mixture\u2010of\u2010Experts (HK\u2010MoE) model. Under single\u2010gas conditions, the model achieved initial F1\u2010scores of 99.53% for target gas presence detection, 99.88% for gas classification, and 98.09% for concentration determination. While performance experienced some degradation following a 30\u2010day continuous operational aging, the model consistently demonstrated satisfactory resilience and significantly outperformed conventional baselines and state\u2010of\u2010the\u2010art Transformer\u2010based time\u2010series models. Further validation using multi\u2010component gas mixtures yielded overall F1\u2010scores above 99.83% for all three tasks, demonstrating reliable mixed\u2010gas discrimination capability. This integrated framework offers a compact solution to drift\u2010induced instability, demonstrating its potential for reliable and early\u2010stage battery safety monitoring.\n                  <\/jats:p>","DOI":"10.1002\/advs.77784","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:27:56Z","timestamp":1789622876000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Temperature\u2010Modulated MEMS Gas Sensor Array Enabled by Physics\u2010Informed Deep Learning for Thermal\u2010Runaway Characteristic Gas Discrimination"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9975-950X","authenticated-orcid":false,"given":"Qin","family":"Luo","sequence":"first","affiliation":[{"name":"College of Semiconductors (National Graduate College for Engineers) Southern University of Science and Technology  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4069-9819","authenticated-orcid":false,"given":"Qi","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Semiconductors (National Graduate College for Engineers) Southern University of Science and Technology  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongyue","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Materials Science and Engineering Southern University of Science and Technology  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Semiconductors (National Graduate College for Engineers) Southern University of Science and Technology  Shenzhen 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of Electrical and Electronic Engineering Southern University of Science and Technology  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2704-1323","authenticated-orcid":false,"given":"Xing","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Semiconductors (National Graduate College for Engineers) Southern University of Science and Technology  Shenzhen China"},{"name":"Department of Materials Science and Engineering Southern University of Science and Technology  Shenzhen China"},{"name":"Advanced Materials Innovation Center Jiaxing Research Institute of Southern University of Science and Technology  Jiaxing 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