# Grow p‐type MoS2 on FeNC for CO2 Sensing in Complex Environments with Intelligent Recognition

## TL;DR

This paper introduces a method to grow p-type MoS2 on FeNC nanosheets for highly sensitive and selective CO2 sensing at room temperature, with potential applications in health monitoring.

## Contribution

A chemically tunable strategy for in situ growth of p-MoS2 on FeNC nanosheets with improved CO2 sensing and intelligent recognition.

## Key findings

- The optimized p-type composites detect CO2 levels as low as 50 ppm at room temperature.
- Sulfur vacancies and modulated electron distribution enhance CO2 sensitivity and selectivity.
- A machine learning model enables accurate CO2 discrimination and prediction with over 95% accuracy.

## Abstract

A wealth of theoretical studies demonstrates p‐type MoS2 (p‐MoS2) as a promising candidate for carbon dioxide (CO2) 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‐MoS2 with controlled thickness and n‐/p‐type transition on N‐ and Fe‐enriched carbon (FeNC) nanosheets. The introduced sulfur vacancies (Svacs) enhance the sensitivity to CO2, and the modulated electron distribution suppresses surface oxygen ionization to improve sensing selectivity. The optimized p‐type composites can detect CO2 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 CO2 from interfering and predicts its concentration and humidity with accuracies exceeding 95%. An intelligent sensing system capable of environmental monitoring and tracking exhaled CO2 is demonstrated. The measured fluctuations strongly correlate with physiological indicators, underscoring their potential for non‐invasive health monitoring and medical diagnostics.

In situ growth of p‐type MoS2 (p‐MoS2) on N‐ and Fe‐enriched carbon (FeNC) nanosheets allows for detection of carbon dioxide (CO2) fluctuation levels as low as 50 ppm at 25 °C. The induced S vacancies (Svacs) and the modulated electron distribution jointly improved sensing selectivity to CO2.