Speaker
Description
Fouling release performance is a critical factor in marine coatings, influencing their effectiveness in preventing biofouling. In this study, Gradient Boosting Regressor (GBM) models were developed to predict fouling release properties based on experimental data for assays performed for C. lytica at 10 psi and 20 psi and N. incerta at 20 psi. The coating systems analyzed consisted of SBMA (sulfobetaine methacrylate) and PDMS (polydimethylsiloxane) in different molecular weights, which are widely used for their antifouling properties. The GBR models were trained using the weights of PDMS and SBMA in each system as weighting schemes and the traditional calculated molecular descriptors to transform into mixture-based molecular descriptors and then, use it as key input features to the GBR models. This mixture-based approach demonstrated high predictive accuracy, outperforming traditional regression models in terms of R² and, RMSE values. Feature importance analysis revealed that the difference in molecular weights of PDMS and SBMA influence fouling release behavior, providing valuable insights into structure-property relationships. To improve accessibility and practical implementation, a web application was developed, allowing users to input/tune different molecular weight values for SBMA and PDMS to obtain fouling release predictions in real time. This tool provides a reliable and user-friendly platform for researchers and industry professionals, facilitating the rational design and optimization of next-generation SBMA-PDMS fouling release coatings.
