Enhancing Fruit Preservation: Fungal Growth Inhibition with Grape Seed-Mediated Ag@AgCl Nanoparticles through Desirability-Based Optimization

Abstract

Biological processes were used to create Ag@AgCl nanoparticles in an eco-friendly manner. Grape seed extracts were used to create Ag@AgCl nanoparticles for this study. The antifungal activity of nanoparticles was tested using fruit-rotting fungi. UV-Vis spectrophotometry was used to analyze the nanoparticles to determine the extent of surface plasmon resonance (SPR) band formation. The phytochemicals employed in nanoparticle synthesis were studied using Fourier transform infrared spectroscopy (FT-IR). X-ray diffraction (XRD) studies revealed that the Ag@AgCl nanoparticles were crystalline. Field-emission scanning electron microscopy (FE-SEM) was used to determine the morphological structure of the nanoparticles. FE-SEM images determined the average particle size of Ag@AgCl. Energy-dispersive X-ray spectroscopy (EDAX was used to examine the elemental composition. Transmission electron microscopy (TEM) and atomic force microscopy (AFM) were employed. Fungi that cause fruit deterioration have been isolated from bananas and papaya fruits. The Ag@AgCl nanoparticles were evaluated for fungal activity against Rhizopus stolonifer, Alternaria species, Aspergillus niger, Aspergillus fumigatus, and Fusarium oxosporum. An agar-well diffusion study indicated that these nanoparticles had good antifungal sensitivity against fungal infections. However, this study was adequate to allow the inhibitory activity to proceed to the optimization phase, where we dealt with RSM-based desirability function prediction. The results also demonstrate that the model performs well, implying that it can provide an accurate estimate of output. usingthe impact,the desirability of each factorzoneson This study presents an eco-friendly method for the synthesis of Ag@AgCl nanoparticles. Assessment of fungal activity revealed significant antifungal sensitivity against fruit-rotting fungi. Transitioning to the optimization phase, this study utilizes RSM-based desirability function prediction. As expected, the results indicated that the model was accurate. They also help us to understand and improve the effectiveness of Ag@AgCl nanoparticles in protecting fruit crops from fungal infections. image