Artificial neural network-guided phyto-synthesis of Pd/Pt bimetallic nanoparticles on cotton: sustainable textile functionalization with antibacterial and colorimetric properties from saffron waste

The sustainable synthesis of palladium–platinum bimetallic nanoparticles (Pd/Pt NPs) using agricultural waste offers an eco-friendly approach to develop functional textiles with enhanced antibacterial and colorfastness properties. In this study, saffron waste comprising petals (SP) and stamens (SS) was employed as a green reducing and stabilizing agent in a microwave-assisted phyto-synthesis method to deposit Pd/Pt NPs onto cotton fabrics. To optimize and accurately predict the color strength (K/S) of the treated textiles, an artificial neural network (ANN) coupled with a genetic algorithm (GA) was implemented, outperforming traditional response surface methodology (RSM) with a high correlation coefficient (R² = 0.99). Comprehensive characterization using dynamic light scattering (DLS), UV–Visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), Field emission scanning electron microscopy with Energy Dispersive X-ray Spectroscopy (FESEM-EDX), and X-ray diffraction (XRD) confirmed the successful formation and uniform distribution of Pd/Pt NPs on cotton fibers. The treated fabrics exhibited superior antibacterial activity, achieving 99% inhibition against both Gram-positive S. aureus and Gram-negative E. coli, alongside excellent colorfastness to rubbing, washing, and light exposure. This work demonstrates the integration of green nanotechnology and machine learning for the fabrication of sustainable, smart antibacterial textiles, contributing to reduced environmental impact and advancing the development of next-generation functional fabrics.