Prediction of Hydrophobic Properties in Biopolymer-Based Coatings via Formulation Data Modelling

The urgent need to replace environmentally persistent and toxic per- and polyfluoroalkyl substances (PFAS) in hydrophobic coatings, has driven the exploration of renewable biopolymers as sustainable alternatives. Biopolymer-based coatings, derived from chitosan, cellulose or starch, are promising alternatives, but optimising their formulations to achieve targeted performance remains challenging. The traditional experimental approaches are time-consuming and resource-intensive. This study integrated a simple linear regression (SLR) technique based on ordinary least squares (OLS) to model the relationship between the formulations` composition and resultant surface water contact angles (WCA) achieved when the coatings are applied on textiles. An SLR/OLS model was applied to experimental data of biopolymer mixtures to predict the WCA, based on the presence and ratios of coating components. The model predicted the WCA values accurately, proving the potential for guiding the design of multifunctional coatings, by enabling rapid screening and optimisation of the formulations, reducing reliance on extensive laboratory experimentation and consumption of chemicals.