Grow p‐type MoS ₂ on FeNC for CO ₂ Sensing in Complex Environments with Intelligent Recognition

Abstract A wealth of theoretical studies demonstrates p‐type MoS ₂ (p‐MoS ₂ ) as a promising candidate for carbon dioxide (CO ₂ ) detection at room temperature. Its applications are retarded by issues associated with its practical chemical synthesis and sensing selectivity. Herein, a chemically tunable strategy is established for in situ growth of p‐MoS ₂ with controlled thickness and n‐/p‐type transition on N‐ and Fe‐enriched carbon (FeNC) nanosheets. The introduced sulfur vacancies (S _vacs ) enhance the sensitivity to CO ₂ , and the modulated electron distribution suppresses surface oxygen ionization to improve sensing selectivity. The optimized p‐type composites can detect CO ₂ fluctuation levels as low as 50 ppm at room temperature. Density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations clarify the underlying mechanisms. A visualized machine learning (ML) model is developed using a hybrid ML strategy that generates regression surfaces from linear/nonlinear data. Through this model, a single sensor accurately discriminates CO ₂ from interfering and predicts its concentration and humidity with accuracies exceeding 95%. An intelligent sensing system capable of environmental monitoring and tracking exhaled CO ₂ is demonstrated. The measured fluctuations strongly correlate with physiological indicators, underscoring their potential for non‐invasive health monitoring and medical diagnostics.