Sustainable double-synergistic silver-hydroxyapatite composite catalyst derived from fish bones for efficient disinfection of Vibrio parahaemolyticus.

Vibrio parahaemolyticus is a food-borne pathogen that poses a serious threat to seafood safety and human health. An efficient, nontoxic, and sustainable disinfection material with a stable structure is urgently needed. Herein, silver (Ag)-hydroxyapatite (HAP) composite catalysts were prepared using HAP derived from waste fish bones. The Ag2.50%-HAP showed a 100% disinfection rate against V. parahaemolyticus, disinfecting nearly 7.0 lg CFU mL-1 within 15 min at a low concentration of 300 µg mL-1. This efficient disinfection activity could be attributed to the double-synergistic effect of Ag and superoxide radicals, which resulted in the destruction of bacterial cell structures and the leakage of intracellular proteins. Importantly, the composite also exhibited high activity in controlling the growth of pathogens during the storage process of Penaeus vannamei. These findings provided sustainable composite catalysts for disinfecting V. parahaemolyticus in seafood and a high-value utilization strategy for waste fish bones.

Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning.

Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntoteff) and nitrate nitrogen (NO3-Neff) concentrations of the WWTP. The input values for the ML models were honed through FA to optimize factors, thereby significantly enhancing the ML prediction accuracy. The prediction model achieved a highest coefficient of determination (R2) of 97.43 % (Ntoteff) and 99.38 % (NO3-Neff), demonstrating satisfactory generalization ability for predictions up to three days ahead (R2 >80 %). Moreover, the interpretability analysis identified that the denitrification factor, the pollutant load factor, and the meteorological factor were significant. The model framework proposed in this study provides a valuable reference for optimizing the operation and management of wastewater treatment.