Computationally Complemented Insights into New Generation Solvents for Radiation-Induced Graft Polymerization

This study explores the integration of new generation solvents into radiation-induced graft polymerization (RIGP) processes to enhance sustainable materials chemistry. While RIGP is a valuable technique for surface modification without impacting material properties, the adoption of environmentally friendly solvents has been limited due to their higher costs and underdeveloped chemistry. To address this, a machine learning (ML) approach, utilizing Grimme's GFN-xTB semiempirical method, is employed to predict solvent effects on RIGP. By analyzing molecular properties of conventional solvents, the study aims to provide insights into the chemistry of new generation solvents, facilitating their broader application in sustainable material fabrication.