Computational Analysis of Sn-Doped hBN for Detection of Lung Cancer-Related VOCs

As a serious worldwide health concern, lung cancer requires novel methods for early detection. This study investigates the potential of Sn-doped hexagonal boron nitride (hBN) as a sensing material, with a focus on its use in the detection of volatile organic compounds (VOCs) such as isoprene (C5H8), benzene (C6H6), and acetone (C3H6O) that are associated with lung cancer. The study uses the Cambridge Sequential Total Energy Package (CASTEP) based on Density Functional Theory (DFT) for computation study. Subsequently, we introduced tin (Sn) atoms into these vacancies to evaluate their stability and reactivity. The investigation of critical parameters that are crucial for reliable sensing, such as bandgap, density of states, adsorption energy, and binding energy, reveals promising properties of Sn-doped hBN for the effective detection of VOCs linked to lung cancer. Moreover, the analysis of adsorption energies reveals strong bonding relationships between the target VOCs and Sn-doped hBN. Compared to Sn-doped N vacancy hBN (−1.03 eV), the Sn-doped B vacancy hBN has a binding energy of −5.14 eV, showing higher stability. The adsorption energies on Sn-doped hBN for C3H6O, C6H6 and C5H8 are determined to be −0.18 eV, −1.6 eV, and −1.5 eV, respectively. Based on these results, Sn-doped hBN is a promising choice for sensing applications. With implications for the early identification and treatment of lung cancer, this study is a significant advancement in the search for non-invasive diagnostic tools in the rapidly developing field of nanotechnology.