Molecular insights into TEOS-driven interface engineering in PVA/Al₂O₃ hybrid materials: A multi-method computational study

This study presents the first systematic computational investigation of tetraethyl orthosilicate (TEOS) as a transformative surface modifier in polyvinyl alcohol (PVA)/aluminum oxide hybrid materials, addressing fundamental challenges in organic-inorganic interface engineering. Through an integrated computational framework combining density functional theory (DFT), COSMO-RS predictions, atoms in molecules (AIM) analysis, and molecular dynamics (MD) simulations, the research elucidated the quantum-level mechanisms underlying TEOS-mediated surface modification. DFT calculations revealed that TEOS functions as an electron donor, establishing directional electron transfer pathways that significantly enhance matrix-filler compatibility. This is evidenced by a reduced HOMO-LUMO gap of 4.033 eV in the hybrid system. COSMO-RS σ-profile and σ-potential analysis demonstrated how TEOS modification dramatically improves system hydrophilicity through increased interactions, leading to more favorable Gibbs free energy of solvation. Topological analysis through AIM revealed an extensive network of hydrogen bonding interactions, with electron density values ranging from 0.003 to 0.01 atomic units at bond critical points, quantitatively confirming TEOS's role in interface stabilization. MD simulations demonstrated that TEOS incorporation systematically enhances binding energies from 4924 kcal/mol in the unmodified system to 8980 kcal/mol with optimal TEOS content, while non-bonded interaction energies improve from -17,860 to -23,995 kcal/mol. This comprehensive understanding enables the rational design of enhanced hybrid materials for critical applications, including high-performance proton exchange membranes for fuel cells, durable ceramic membranes for water purification, and advanced protective coatings for corrosion prevention. Furthermore, the computational framework reported herein provides a powerful predictive blueprint for optimizing hybrid material properties before synthesis, accelerating the development of next-generation materials for emerging technological challenges.