Machine Learning-Enhanced High-Temperature CO <sub>2</sub> Sensor for In Situ Exhaust Monitoring: Robust Compensation via Real-Time Vehicle Operating Parameters
) 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.
Authors
- Quanquan Tang
- Zuorong Huang (ORCID: https://orcid.org/0009-0009-0506-2298)
- Wu B
- Geyu Lu
- Xishuang Liang
- Mingliang Fu
Institutions
- Jilin University (CN)
- Ministry of Ecology and Environment (CN)
- State Key Laboratory on Integrated Optoelectronics (CN)
- Chinese Research Academy of Environmental Sciences (CN)
Publication Details
- Journal
- ACS Sensors
- Published
- 2026-07-23
- DOI
- https://doi.org/10.1021/acssensors.6c01547
- Primary Topic
- Vehicle emissions and performance
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China