Hybrid Series of Carbon-Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi-Task Model.

ABSTRACT: Detecting individual gases with various sensors is a well-established field in gas sensing. However, substantial challenges and opportunities remain in the simultaneous detection and classification of multiple gases. Artificial intelligence (AI) integrated gas sensor systems effectively enable multi-gas detection using specialized algorithms. Nevertheless, these algorithms are prone to overfitting owing to their high model complexity; this study proposes a sensor array that engineers carbon vacancies in graphene oxide via metal ion doping and high-temperature reduction, enabling high-sensitivity, simultaneous detection of various gases at low temperatures (20 °C). By integrating an advanced artificial intelligence framework, the acquired electrical signals are transformed, and a multi-task

SUBMITTER: Liu T 

PROVIDER: S-EPMC12224976 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature