A comprehensive review on the inherent and enhanced antifouling mechanisms of hydrogels and their applications.

Biofouling remains a persistent challenge within the domains of biomedicine, tissue engineering, marine industry, and membrane separation processes. Multifunctional hydrogels have garnered substantial attention due to their complex three-dimensional architecture, hydrophilicity, biocompatibility, and flexibility. These hydrogels have shown notable advances across various engineering disciplines. The antifouling efficacy of hydrogels typically covers a range of strategies to mitigate or inhibit the adhesion of particulate matter, biological entities, or extraneous pollutants onto their external or internal surfaces. This review provides a comprehensive review of the antifouling properties and applications of hydrogels. We first focus on elucidating the fundamental principles for the inherent resistance of hydrogels to fouling. This is followed by a comprehensive investigation of the methods employed to enhance the antifouling properties enabled by the hydrogels' composition, network structure, conductivity, photothermal properties, release of reactive oxygen species (ROS), and incorporation of silicon and fluorine compounds. Additionally, we explore the emerging prospects of antifouling hydrogels to alleviate the severe challenges posed by surface contamination, membrane separation and wound dressings. The inclusion of detailed mechanistic insights and the judicious selection of antifouling hydrogels are geared toward identifying extant gaps that must be bridged to meet practical requisites while concurrently addressing long-term antifouling applications.

Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application.

The accurate prediction and assessment of effluent quality in wastewater treatment plants (WWTPs) are paramount for the efficacy of sewage treatment processes. Neural network models have exhibited promise in enhancing prediction accuracy by simulating and analyzing diverse influent parameters. In this study, a back propagation neural network hybrid model based on a tent chaotic map and sparrow search algorithm (Tent_BP_SSA) was developed to predict the effluent quality of sewage treatment processes. The prediction performance of the propose hybrid model was compared with traditional neural network models using five performance indicators (MAE, RMSE, SSE, MAPE and R2). Specifically, in comparison with the prior Tent_BP_SSA, Tent_BP_SSA2 demonstrated notable enhancements, with the R2 increasing from 0.9512 to 0.9672, while MAE, RMSE, SSE, and MAPE decreased by 9.62%, 18.84%, 24.80%, and 47.10%, respectively. These indicators collectively affirm that the utilization of higher-order input parameters ensures improved accuracy of the Tent_BP_SSA2 hybrid model in predicting effluent quality. Moreover, the Tent_BP_SSA2 model exhibited robust prediction ability (R2 of 0.9246) when applied to assess the effluent quality of an actual sewage treatment plant. The incorporation of integrated models comprising the sparrow search optimizing algorithm, tent chaotic mapping, and higher-order magnitude decomposition of input parameters has demonstrated the capacity to enhance the accuracy of effluent quality prediction. This study illuminates novel perspectives on the prediction of effluent quality and the assessment of effluent warnings in WWTPs.