Synthetic dyes from industrial effluents are persistent pollutants requiring sustainable removal strategies. This study investigates the visible-light photocatalytic degradation of Brilliant Blue R using biogenic silver nanoparticles synthesized from microalgal extracts (Spirulina platensis and Chlorella vulgaris), reporting both experimental results and a hybrid modeling approach. Experimental analyses confirmed the effectiveness of the synthesized nanoparticles and their favorable physicochemical properties for photocatalytic applications. A simplified mechanistic model describing adsorption–desorption and degradation kinetics was developed to estimate key parameters, which were subsequently used to train artificial neural networks linking operating conditions to degradation performance. To address limited datasets, a Gaussian-noise-based augmentation strategy was introduced, significantly improving predictive accuracy. The proposed framework integrates experimental evidence with mechanistic and data-driven modeling, providing a reliable tool for optimizing sustainable photocatalytic processes based on microalgae-derived nanomaterials.
Green Synthesis of Ag Nanomaterials using Microalgal Extracts for Photocatalytic Degradation of Contaminants: Experiments and Modeling
Bartolomeo Cosenza;
2026-01-01
Abstract
Synthetic dyes from industrial effluents are persistent pollutants requiring sustainable removal strategies. This study investigates the visible-light photocatalytic degradation of Brilliant Blue R using biogenic silver nanoparticles synthesized from microalgal extracts (Spirulina platensis and Chlorella vulgaris), reporting both experimental results and a hybrid modeling approach. Experimental analyses confirmed the effectiveness of the synthesized nanoparticles and their favorable physicochemical properties for photocatalytic applications. A simplified mechanistic model describing adsorption–desorption and degradation kinetics was developed to estimate key parameters, which were subsequently used to train artificial neural networks linking operating conditions to degradation performance. To address limited datasets, a Gaussian-noise-based augmentation strategy was introduced, significantly improving predictive accuracy. The proposed framework integrates experimental evidence with mechanistic and data-driven modeling, providing a reliable tool for optimizing sustainable photocatalytic processes based on microalgae-derived nanomaterials.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


