Nature-mimicking fabrication of antifouling photocatalytic membrane based on Ti/BiOI and polydopamine for synergistically enhanced photocatalytic degradation of tetracycline

Innovative approaches to greywater micropollutant removal: AI-driven solutions and future outlook

Greywater constitutes a significant portion of urban wastewater and is laden with numerous emerging contaminants that have the potential to adversely impact public health and the ecosystem. Understanding greywater's characteristics and measuring the contamination levels is crucial for designing an effective recycling system. However, wastewater treatment is an intricate process involving significant uncertainties, leading to variations in effluent quality, costs, and environmental risks. This review addresses the existing knowledge gap in utilising artificial intelligence (AI) to enhance the laundry greywater recycling process and elucidates the optimal treatment technologies for the most prevalent micropollutants, including microplastics, nutrients, surfactants, synthetic dyes, pharmaceuticals, and organic matter. The development of laundry greywater treatment technologies is also highlighted with a critical discussion of physicochemical, biological, and advanced oxidation processes (AOPs) based on their functions, methods, associated limitations, and future trends. Artificial neural networks (ANN) stand out as the most prevalent and extensively applied AI model in the domain of wastewater treatment. Utilising ANN models mitigates certain limitations inherent in traditional adsorption models, particularly by offering enhanced predictive accuracy under varied operating conditions and multicomponent adsorption systems. Moreover, tremendous success has been recorded with the random forest (RF) model, exhibiting 100% prediction accuracy for both sessile and effluent microbial communities within a bioreactor. The precise prediction or simulation of membrane fouling behaviours using AI techniques is also of paramount importance for understanding fouling mechanisms and formulating efficient strategies to mitigate membrane fouling.

Pharmaceutical Removal with Photocatalytically Active Nanocomposite Membranes

The advancement of pharmaceutical science has resulted in the development of numerous tailor-made compounds, i.e., pharmaceuticals, tuned for specific drug targets. These compounds are often characterized by their low biodegradability and are commonly excreted to a certain extent unchanged from the human body. Due to their low biodegradability, these compounds represent a significant challenge to wastewater treatment plants. Often, these compounds end up in effluents in the environment. With the advancement of membrane technologies and advanced oxidation processes, photocatalysis in particular, a synergistic approach between the two was recognized and embraced. These hybrid advanced water treatment processes are the focus of this review, specifically the removal of pharmaceuticals from water using a combination of a photocatalyst and pressure membrane process, such as reverse osmosis or nanofiltration employing photocatalytic nanocomposite membranes.

Fabrication of silver vanadate quantum dots/reduced graphene oxide/graphitic carbon nitride Z-scheme heterostructure modified polyvinylidene fluoride self-cleaning membrane for enhancing photocatalysis and mechanism insight

The enhancement of the self-cleaning ability of photocatalytic membranes and their degradation efficiency over tetracycline (TC) still remains a challenge. In this study, an alternative silver vanadate quantum dots (AgVO₃ QDs) doped reduced graphene oxide (RGO) and graphitic carbon nitride (C₃N₄) nanocomposites modified polyvinylidene fluoride (PVDF) membrane (AgVO₃/RGO/C₃N₄-PVDF) was successfully fabricated to enhance the photocatalytic activity. The AgVO₃/RGO/C₃N₄ nanocomposites were functioned as the active component for the photocatalytic membrane. The unique Z-scheme heterostructure of AgVO₃/RGO/C₃N₄ and the porous PVDF framework synergistically enhanced the separation and transport efficiency of photogenerated carriers and facilitated the interaction between the photocatalyst and the pollutant. As a result, the degradation efficiency of TC for the AgVO₃/RGO/C₃N₄-PVDF reached 88.53% within 120 min, which was higher than those of the binary component membranes (64.8% for RGO/C₃N₄-PVDF and 79.18% AgVO₃/C₃N₄-PVDF). In addition, AgVO₃/RGO/C₃N₄-PVDF exhibited high permeability (1977 L·m^-2·h^-1·bar^-1) and excellent antifouling activity. Under visible-light irradiation, the flux recovery rate (FRR) increased from 92.4% to 99.1%. Furthermore, AgVO₃/RGO/C₃N₄-PVDF could reject 97.4% of Escherichia coli (E. coli) owning to its self-cleaning capacity, and eliminated the E. coli under visible-light irradiation trough the photogeneration of h⁺. This study highlights a highly efficient photocatalytic membrane based on a Z-scheme heterostructure, which may have a great potential application in practical wastewater treatment.