Prediction of antibacterial properties of bio-based chitosan nanoparticle functionalized cotton fabric using a fuzzy logic model

The ever-growing demand for eco-friendly, high-performance, and hygienic textiles has resulted in increased interest in the development of bio-based fabrics and artificial intelligence (AI)-based modeling techniques. This study aimed to develop an eco-friendly and sustainable antibacterial cotton fabric functionalized with bio-based chitosan nanoparticles (CNPs) and to construct an AI-based fuzzy logic (FL) model in MATLAB using CNP concentration, cross-linker concentration, and curing temperature as input parameters to predict antibacterial efficacy against Staphylococcus aureus. The model was validated with a new set of experimental data and demonstrated high predictive accuracy, with a mean absolute percentage error (MAPE) of 1.194% and a coefficient of determination (R²) of 0.979 in the nonlinear textile domain. The functionalized fabrics showed over 99% bacterial reduction against Staphylococcus aureus with acceptable tensile strength and crease recovery quality. Field Emission Scanning Electron Microscopy (FESEM) analysis showed uniformly distributed nanoparticles with an average size of 74 nm, and Fourier Transform Infrared Spectroscopy (FTIR) confirmed successful chemical modification of the fabric. It is concluded that this study shows a sustainable, eco-friendly, and resource-efficient approach, combining green nanotechnology with an AI-based fuzzy logic model to predict the antibacterial properties of cotton fabric, providing an effective alternative to conventional trial-and-error optimization methods in functional textile development.