Computational insights into the adsorption of lung cancer biomarkers on graphene-based materials and interfacial phenomena

• DFT study on adsorption of 22 lung cancer biomarkers onto graphene-based materials. • Categorized VOC biomarkers by dipole moments for enhanced e-nose sensor design. • Five graphene-based materials studied: Gr, N-Gr, F-Gr, GO, and Pd-Gr for sensor use. • Band structure analysis reveals diverse electronic properties across sensor materials. • Pd-Gr showed exceptional selectivity towards selected biomarkers. An artificial intelligence-enhanced electronic nose (AI-enhanced e -nose) targeting lung cancer biomarkers—volatile organic compounds (VOCs) present in human breath—represents a revolutionary technology capable of transforming early-stage lung cancer detection. In this context, the choice of sensor materials for the e -nose plays a critical role in ensuring the necessary discriminatory power of the device. This selection must be based on a deep understanding of the physicochemical properties of potential candidate materials and their adsorption characteristics with respect to lung cancer biomarkers. The density functional theory (DFT) method is a powerful tool for achieving this understanding. This study presents the results of a comprehensive DFT investigation of the adsorption of 22 VOC lung cancer biomarkers on five different types of graphene using vdW-BH functional combined with double-zeta plus polarization basis set. The 22 VOCs were classified based on their dipole moments. We demonstrate the importance of applying energy decomposition methods for VOC adsorption, along with coupled charge population analysis and charge density difference analysis approaches, for a more profound understanding of the interactions between VOCs and graphene-based materials. This variability in responses could serve as the foundation for creating an e-nose capable of providing diverse signal outputs when combining these materials.