Machine learning-driven stability analysis of eco-friendly superhydrophobic graphene-based coatings on copper substrate.

ABSTRACT: This study inspects the integration of machine learning (ML) techniques with materials science to develop durable, eco-friendly superhydrophobic (SHP) graphene-based coatings for copper. We employed various ML and regression models, including XGBoost, polynomial regression models, Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Regression (SVR), to predict the stability of the contact angle (CA) under different stress conditions, such as NaCl immersion, abrasion cycles, tape peeling tests, sand impact, and open-air exposure. Our findings demonstrate that ensemble learning models, particularly XGBoost and Random Forest, outperform traditional regression techniques by effectively capturing nonlinear dependencies between stress parameters and CA retention. Higher-order poly

SUBMITTER: Mamgain HP 

PROVIDER: S-EPMC12494925 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature