Abstract
Glucose biosensors offer a viable means for the early diagnosis and treatment of diabetes. Highly sensitive and selective sensing materials must be developed to improve biosensor performance. This research intends to computationally analyse and compare the applicability of various nanomaterials—namely, Molybdenum Disulfide (MoS₂), Zinc Oxide (ZnO), and Titanium Carbide (Ti₃C₂) MXene—for incorporation into nanomaterial-based glucose biosensors employing glucose oxidase (GOx) as the biorecognition component. Utilising a simulation workflow founded entirely on open-source tools, the study emphasises modelling enzyme–surface interactions, assessing binding affinity through molecular docking and density functional theory (DFT), and investigating electronic property alterations upon enzyme adsorption.
Surface hydroxylation greatly increases GOx adsorption on all nanomaterials, according to docking simulations, with ZnO and Ti₃C₂ exhibiting the most favourable energetics in the approximate range of −4 to −4.5 kcal/mol. Stable GOx immobilisation was confirmed by later DFT simulations, which also increased charge transfer and electronic coupling at the interface. Ti₃C₂ MXene demonstrated the strongest enzyme binding of −1.10 eV and maintained metallic conductivity after adsorption, indicating greater charge-transfer capabilities among the materials studied. Based on the results, ZnO is a great substitute for semiconducting sensor platforms, but Ti₃C₂ is the most promising option for BioFET-based glucose detection.
The study demonstrates the viability of using free computational tools to guide material selection for next-generation biosensor design. The results should be seen as a theoretical screening tool to direct future empirical research rather than final performance benchmarks because this study, which focuses on molecular-level interactions, is still restricted to atomistic modelling and lacks device-scale signal transduction simulations or experimental validation.