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This study reports a robust sensing platform integrating a NASICON-based solid electrolyte sensor with a random forest (RF) machine learning (ML) architecture for high-fidelity carbon emission monitoring. To achieve superior electrochemical performance, the sensor utilizes surface-optimized catalysts and stabilizers, with NASICON doping to enhance ionic conductivity and structural integrity under thermal cycling. The hardware is coupled with an automotive-grade data acquisition system, enabling in situ signal capture directly from the engine tailpipe. To circumvent the analytical limitations of solid electrolyte sensors\u2014specifically thermal drift and multi-gas cross-interference\u2014a software-defined compensation strategy is proposed. Rather than relying on auxiliary gas sensors, the RF model leverages intrinsic vehicle operating parameters as surrogate multidimensional features to dynamically decouple interfering factors from the CO2 response. The sensor reliability was rigorously validated across multiple combustion platforms under standardized transient cycles, including the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the World Harmonized Transient Cycle (WHTC), and the Real Driving Emissions (RDE) protocols. The ML-enhanced sensor achieved a coefficient of determination (R2) exceeding 0.8 even in highly non-linear transient states. Crucially, the cumulative mass emission error was suppressed to less than 0.4%, demonstrating metrological-grade accuracy in total carbon quantification. By synergizing solid-state electrochemistry with intelligent data analytics, this work provides a scalable, cost-effective solution for real-time carbon footprint tracking, directly supporting global initiatives for transportation decarbonization and environmental compliance.<\/jats:p>","DOI":"10.1021\/acssensors.6c01547","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:18:48Z","timestamp":1784819928000},"page":"7264-7274","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Enhanced High-Temperature CO2 Sensor for In Situ Exhaust Monitoring: Robust Compensation via Real-Time Vehicle Operating Parameters"],"prefix":"10.1021","volume":"11","author":[{"given":"Quanquan","family":"Tang","sequence":"first","affiliation":[{"name":"Jilin University , , 2699 Qianjin Street , , ,","place":["Changchun, Jilin Province, China, 130012"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0506-2298","authenticated-orcid":true,"given":"Zuorong","family":"Huang","sequence":"additional","affiliation":[{"name":"Jilin University , , 2699 Qianjin Street , , ,","place":["Changchun, Jilin Province, China, 130012"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[{"name":"Jilin University , , 2699 Qianjin Street , , ,","place":["Changchun, Jilin Province, China, 130012"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xishuang","family":"Liang","sequence":"additional","affiliation":[{"name":"Jilin University , , 2699 Qianjin Street , , ,","place":["Changchun, Jilin Province, China, 130012"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingliang","family":"Fu","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences , , ,","place":["Beijing, China, 100012"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Jilin University , , 2699 Qianjin Street , , ,","place":["Changchun, Jilin Province, China, 130012"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"issue":"6002","key":"2026090201425779600_cit1","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1126\/science.1190653","article-title":"Atmospheric CO2: Principal Control Knob Governing Earth\u2019s Temperature","volume":"330","author":"Lacis","year":"2010","journal-title":"Science"},{"issue":"3","key":"2026090201425779600_cit2","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/led.2022.3147801","article-title":"Selective Detection of CO2 and H2O Dual Analytes Through Decoupling Surface Density and Shear Modulus Based on Single SAW Resonator","volume":"43","author":"Pang","year":"2022","journal-title":"IEEE Electron Device Lett."},{"issue":"6","key":"2026090201425779600_cit3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jece.2023.111393","article-title":"A comprehensive review on recent trends in carbon capture, utilization, and storage techniques","volume":"11","author":"Yusuf","year":"2023","journal-title":"J. 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