On physical analysis of cadmium bismuth sulfide using quadratic regression approach

Antimicrobial resistance and cancer detection are two popular applications for cadmium bismuth sulfide nanoparticles. Cadmium bismuth sulfide nanoparticles have demonstrated antibacterial capabilities and a broad range of antibacterial activity against both gram-positive and gram-negative bacteria. To obtain more insights into its bonding and connectivity patterns, we calculate new Zagreb-type indices. To evaluate the material’s stability and predict its behavior in different situations, we can calculate the entropy measure. By using a quadratic regression model, we create mathematical connections between the Zagreb-type indices and entropy, which helps maximize its utilization in specific applications. Through the regression model, we see the relation between indices and entropy. In the present paper, a new application of quadratic regression models is presented in developing a mathematical relation between Zagreb-type indices and entropy measures to derive a new methodology for predicting and optimizing stability and behavior in cadmium bismuth sulfide nanoparticles. It connects molecular graph theory with material analysis in new ways toward deeper insights into molecular connectivity patterns and enhances the practical utility of topological indices in advanced material science.