Speaker
Description
Recent studies have identified titanium carbide (Ti2C) MXenes as promising 2D materials for detecting volatile organic compounds (VOCs) present in human breath. These VOCs reflect physiological changes and can serve as early biomarkers for diseases such as lung cancer. Diagnosing lung cancer through breath analysis offers a non-invasive and rapid alternative to conventional diagnostic techniques. In this study, we investigate the adsorption behavior of various VOCs on functionalized Ti2C monolayers using first-principles calculations. Our results reveal distinct adsorption energies and molecule monolayer distances, indicating strong chemisorption, particularly for the OH-functionalized Ti2C surface. Conductance and current-voltage (I-V) analyses further confirm the high selectivity of the OH-functionalized Ti2C monolayer toward aniline, due to its low work function and the electron-donating nature of aniline. To enhance the discovery and classification of VOCs biomarkers, we propose integrating machine learning algorithms trained in computed electronic, structural, and adsorption features. AI models will classify and predict VOCs interactions with various surface terminations, enabling high-throughput screening of candidate biomarkers. This combined approach of 2D materials design and AI-assisted analysis paves the way for advanced, non-invasive diagnostic platforms for early lung cancer sensing.
