A multi-model kinetic framework for performance prediction and optimization of chitosan nanoparticles as antifungal agents

Chitosan nanoparticles derived from Archachatina marginata shell biowaste were characterized and tested against Aspergillus flavus to assess their antifungal performance under time dependent conditions. The material had a degree of deacetylation of 75%, mean particle sizes of 49.90 nm by TEM and 34.36 nm by SEM, a crystallite size of 22.27 nm, a crystallinity index of 68%, and thermal degradation onset at approximately 200 °C with the main decomposition stage extending to 360 °C. Antifungal activity was evaluated over 24 days at concentrations of 5 to 40 ppm, and the resulting inhibition data were fitted with linear, exponential, logistic, zero order, first order, and second order models. Inhibition increased with concentration, although the response was not equally consistent across all treatments. Among the tested models, the second order equation gave the best fit, with the highest performance at 20 ppm, where R² was 0.98 and k₂ was 2 × 10⁻² ppm⁻¹ day⁻¹. This concentration gave sustained inhibition while using less material than the 40 ppm treatment. The results show that shell derived chitosan nanoparticles can inhibit A. flavus under the conditions tested and that comparative kinetic fitting can be used to identify a practical concentration range for preservation related applications. Graphical summary of the preparation, characterization and antifungal evaluation of shell derived chitosan nanoparticles against Aspergillus flavus. Chitosan nanoparticles were produced from Archachatina marginata shell waste and evaluated using physicochemical characterization and concentration dependent antifungal testing. The kinetic comparison indicated that 20 ppm provided the most suitable balance between antifungal response and model fit, with the second order model giving the strongest fit among the tested model