DFT and machine learning investigation of Au/Pt-decorated SnS₂ monolayers for asthma and COPD diagnosis.

Asthma and Chronic Obstructive Pulmonary Disease (COPD) are among the most prevalent chronic respiratory diseases worldwide, affecting hundreds of millions of people and contributing significantly to global morbidity and mortality. This work introduces a novel Au/Pt-SnS₂ heterostructure for exhaled NO₂ detection, representing the new study to explore its role in lung disease diagnostics. It demonstrates ppb level NO₂ detection, a key biomarker for asthma and COPD, enabling early and differentiation of lung conditions by providing quantitative analysis of trace-level gases, which are often elevated in inflamed airways. While 2D SnS₂ offers strong potential as a sensing platform, prior studies relied mainly on Density functional theory (DFT) based gas sensing. Here, we present unprecedented integration of DFT and Machine Learning (ML) to investigate the gas sensing performance of pristine and Au/Pt decorated SnS₂ monolayers. DFT analysis revealed enhanced adsorption and charge transfer upon noble-metal decoration, with Pt-SnS₂ showing optimal characteristics for asthma and COPD detection. Five ML models were trained on DFT and experimental-derived descriptors to rapidly predict the sensing behaviour of multiple gases, including NO₂, among which XGBoost achieving R² = 0.9961. Both ML and DFT methods consistently identified NO₂ as the most sensitive analyte. This novel DFT-ML synergy not only validates fundamental adsorption mechanisms but also provides a scalable pathway for accelerated screening and design of high-performance gas sensors. Our findings establish a new prototype for integrating ML with first-principles simulations in the design of next-generation 2D materialbased sensing devices.