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Express"],"published-print":{"date-parts":[[2025,6,16]]},"abstract":"<jats:p>\n                    Micro-ring resonator (MRR) platforms based on silicon-on-insulator substrates have shown great potential for gas detection applications. However, challenges such as weak signal intensity and insufficient selectivity remain in the detection of low-concentration mixed gases. To overcome these limitations, this study proposes a machine learning-enhanced silicon nitride-based micro-ring resonator chip for the detection and recognition of methane (CH\n                    <jats:sub>4<\/jats:sub>\n                    ), carbon dioxide (CO\n                    <jats:sub>2<\/jats:sub>\n                    ), and hydrogen sulfide (H\n                    <jats:sub>2<\/jats:sub>\n                    S) gas mixtures. By combining micro-ring resonator sensing data with machine learning models, the detection performance of the optical waveguide sensor was substantially improved. Experimental results show that the sensing chip can accurately identify CH\n                    <jats:sub>4<\/jats:sub>\n                    , CO\n                    <jats:sub>2<\/jats:sub>\n                    , and H\n                    <jats:sub>2<\/jats:sub>\n                    S, with limits of detection (LODs) of 153 ppb, 184 ppb, and 83 ppb, respectively. With the aid of machine learning algorithms, the sensor achieves a classification accuracy of 91.4% in complex multi-component gas environments and can precisely determine methane concentration in unknown gas mixtures, with an average error of only 4.7%. This study not only provides an innovative solution for the detection of low-concentration gas mixtures but also demonstrates the broad application prospects of silicon photonics in the field of gas sensing.\n                  <\/jats:p>","DOI":"10.1364\/oe.563058","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T12:00:01Z","timestamp":1747915201000},"page":"24844","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Machine learning-enhanced on-chip micro-ring resonator platform for detection and recognition of low-concentration gas mixtures"],"prefix":"10.1364","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5323-7558","authenticated-orcid":true,"given":"Peng","family":"Qin","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01y0j0j86","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Yangtze River Delta Research Institute of NPU"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Kang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01y0j0j86","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuetao","family":"Gan","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01y0j0j86","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"oe-33-12-24844-R1","doi-asserted-by":"publisher","first-page":"101757","DOI":"10.1016\/j.gsf.2023.101757","volume":"15","author":"Wang","year":"2024","journal-title":"Geosci. 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