Boosting the Performance of Nanofiltration Membranes in Removing Organic Micropollutants: Trade-Off Effect, Strategy Evaluation, and Prospective Development

Polydopamine bridging encapsulated laccase on MOF-based mixed-matrix membrane for selective dye/salt separation

Mixed-matrix membranes (MMMs) exhibit significant potential for dye/salt separation. However, overcoming the "trade-off" between permeability and selectivity, as well as membrane fouling, remains a formidable task. In this work, a biocatalytic membrane was prepared using polydopamine (PDA) as a "bridge" connecting the metal-organic framework (MOF)-based MMM and immobilized laccase. The MOF-based MMM featured an interconnected MOF anchoring on the polyvinylidene fluoride (PVDF) skeleton structure, effectively mitigating the "trade-off" phenomenon and enabling efficient separation of dyes and salts. Enzyme-MOF was in situ grown on the MOF-based MMM via coordination reactions between PDA and metal ion, effectively degrading the adhesion of organic pollutants and fouling, ensuring the long-term stable operation of the membrane. The Lac-MOF@PDA MMM exhibited excellent water permeability of 142.4 L·m-2·h-1, 100 % rejection for dye, and less than 10 % rejection for NaCl. Furthermore, the separation mechanism of Lac-MOF@PDA MMM was systematically investigated, and the results suggested a synergistic combination of rejection, adsorption and catalysis processes. This biocatalytic membrane with multiple sieving and biological catalysis is expected to pave a promising way for efficient wastewater treatment applications.

Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering

Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.

Trade-off between Endocrine-Disrupting Compound Removal and Water Permeance of the Polyamide Nanofiltration Membrane: Phenomenon and Molecular Insights

The polyamide (PA) nanofiltration (NF) membrane has the potential to remove endocrine-disrupting compounds (EDCs) from water and wastewater to prevent risks to both the aquatic ecosystem and human health. However, our understanding of the EDC removal-water permeance trade-off by the PA NF membrane is still limited, although the salt selectivity-water permeance trade-off has been well illustrated. This constrains the precise design of a high-performance membrane for removing EDCs. In this study, we manipulated the PA nanostructures of NF membranes by altering piperazine (PIP) monomer concentrations during the interfacial polymerization (IP) process. The upper bound coefficient for EDC selectivity-water permeance was demonstrated to be more than two magnitudes lower than that for salt selectivity-water permeance. Such variations were derived from the different membrane-solute interactions, in which the water/EDC selectivity was determined by the combined effects of steric exclusion and the hydrophobic interaction, while the electrostatic interaction and steric exclusion played crucial roles in water/salt selectivity. We further highlighted the role of the pore number and residual groups during the transport of EDC molecules across the PA membrane via molecular dynamics (MD) simulations. Fewer pores decreased the transport channels, and the existence of residual groups might cause steric hindrance and dynamic disturbance to EDC transport inside the membrane. This study elucidated the trade-off phenomenon and mechanisms between EDC selectivity and water permeance, providing a theoretical reference for the precise design of PA NF membranes for effective removal of EDCs in water reuse.

An efficient solution based on the synergistic effects of nickel foam in NiFe-LDH nanosheets for oil/water separation

Efficient oil-water separation has always been a research hotspot in the field of environmental studies. Employing a one-step hydrothermal approach, NiFe-layered double hydroxides (LDH) nanosheets were synthesized on nickel foam substrates. The resulting NiFe-LDH/NF membrane exhibited rejection rates exceeding 99% across six diverse oil-water mixtures, concurrently demonstrating a remarkable ultra-high flux of 1.4 × 106 L·m-2·h-1. This flux value significantly surpasses those documented in existing literature, maintaining stable performance over 1000 manual filtration cycles. These breakthroughs stem from the synergistic interplay among the three-dimensional channels of the nickel foam, the nanosheets, and the hydration layer. By leveraging the pore size of the foam to enhance the functionality of the hydration layer, the conventional trade-off between permeability and selectivity was transformed into a balanced force relationship between the hydration layer and the oil phase. The operational and failure mechanisms of the hydration layer were examined using the prepared NiFe-LDH/NF membrane, validating the correlation between oil phase viscosity and density with hydration layer rupture. Additionally, an extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory was employed to investigate changes in interaction energy, further reinforcing the study's findings. This research contributes novel insights and assistance to the comprehension and application of hydration layers in other membrane studies dedicated to oil-water separation.

Self-cleaning foulant attachment on near-infrared responsive photocatalytic membrane for continuous dynamic removing antibiotics in sewage effluent environment

Bifunctional photocatalytic nanofiltration (PNF) membrane has become a reliable frontier technique for removing refractory organic micropollutants. However, the active mitigated fouling mechanism from the microscopic perspective during its long-term operation of purifying real micro-polluted water is rarely studied. Herein, with an integrated use of QSense Explorer and confocal laser scanning microscope techniques, self-cleaning foulant attachment on an activated and customized near-infrared responsive polymeric PNF (termed as nPNF) membrane with good service performance for continuous dynamic removing antibiotics in sewage effluent environment was firstly elucidated. Time-dependent changes in dissipation oscillation frequency, sensed mass and the visualized foulant spatial distribution all indicated that there were only sporadic foulant attachment, an extremely low fouling layer thickness and irreversible fouling rate on/of the activated nPNF membrane top surface, thereby endowing it with excellent self-cleaning characteristic. This is probably because the reactive oxygen species (mainly •O2- and •OH) concurrently destroys the integrity of fouling layer and its internal adhesion structure, transforming part of the irreversible fouling on nPNF membrane surface into reversible one that is easy to wash off. These new horizons provided useful insight on the fate of selected antibiotics in the to-be-removed stage and self-cleaning foulant attachment of PNF membrane.

Pressure-driven membrane filtration technology for terminal control of organic DBPs: A review

Disinfection by-products (DBPs), a series of undesired secondary contaminants formed during the disinfection processes, deteriorate water quality, threaten human health and endanger ecological safety. Membrane-filtration technologies are commonly used in the advanced water treatment and have shown a promising performance for removing trace contaminants. In order to gain a clearer understanding of the behavior of DBPs in membrane-filtration processes, this work dedicated to: (1) comprehensively reviewed the retention efficiency of microfiltration (MF), ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) for DBPs. (2) summarized the mechanisms involved size exclusion, electrostatic repulsion and adsorption in the membrane retention of DBPs. (3) In conjunction with principal component analysis, discussed the influence of various factors (such as the characteristics of membrane and DBPs, feed solution composition and operating conditions) on the removal efficiency. In general, the characteristics of the membranes (salt rejection, molecular weight cut-off, zeta potential, etc.) and DBPs (molecular size, electrical property, hydrophobicity, polarity, etc.) fundamentally determine the membrane-filtration performance on retaining DBPs, and the actual operating environmental factors (such as solute concentration, coexisting ions/NOMs, pH and transmembrane pressure) exert a positive/negative impact on performance to some extent. Current researches indicate that NF and RO can be effective in removing DBPs, and looking forward, we recommend that multiple factors should be taken into account that optimize the existed membrane-filtration technologies, rationalize the selection of membrane products, and develop novel membrane materials targeting the removal of DBPs.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

Making waves: Why do we need ultra-permeable nanofiltration membranes for water treatment?

Over the last few decades, developing ultra-permeable nanofiltration (UPNF) membranes has been a focus research area to support NF-based water treatment. Nevertheless, there have been ongoing debates and doubts on the need for UPNF membranes. In this work, we share our perspectives on why UPNF membranes are desired for water treatment. We analyze the specific energy consumption (SEC) of NF processes under various application scenarios, which reveals the potential of UPNF membranes for reducing SEC by 1/3 to 2/3 depending on the prevailing transmembrane osmotic pressure difference. Furthermore, UPNF membranes could potentially enable new process opportunities. Vacuum-driven submerged NF-modules could be retrofitted to existing water/wastewater treatment plants, offering lower SEC and lower cost compared to conventional NF systems. Their use in submerged membrane bioreactors (NF-MBR) can recycle wastewater into high-quality permeate water, which enables energy-efficient water reuse in a single treatment step. The ability for retaining soluble organics may further extend the application of NF-MBR for anaerobic treatment of dilute municipal wastewater. Critical analysis of membrane development reveals huge rooms for UPNF membranes to attain improved selectivity and antifouling performance. Our perspective paper offers important insights for the future development of NF-based water treatment technology, which could potentially lead to a paradigm shift in this burgeoning field.

Progress towards Stable and High-Performance Polyelectrolyte Multilayer Nanofiltration Membranes for Future Wastewater Treatment Applications

The increasing demand for nanofiltration processes in drinking water treatment, industrial separation and wastewater treatment processes has highlighted several shortcomings of current state-of-the-art thin film composite (TFC NF) membranes, including limitations in chemical resistance, fouling resistance and selectivity. Polyelectrolyte multilayer (PEM) membranes provide a viable, industrially applicable alternative, providing significant improvements in these limitations. Laboratory experiments using artificial feedwaters have demonstrated selectivity an order of magnitude higher than polyamide NF, significantly higher fouling resistance and excellent chemical resistance (e.g., 200,000 ppmh chlorine resistance and stability over the 0-14 pH range). This review provides a brief overview of the various parameters that can be modified during the layer-by-layer procedure to determine and fine-tune the properties of the resulting NF membrane. The different parameters that can be adjusted during the layer-by-layer process are presented, which are used to optimize the properties of the resulting nanofiltration membrane. Substantial progress in PEM membrane development is presented, particularly selectivity improvements, of which the most promising route seems to be asymmetric PEM NF membranes, offering a breakthrough in active layer thickness and organic/salt selectivity: an average of 98% micropollutant rejection coupled with a NaCl rejection below 15%. Advantages for wastewater treatment are highlighted, including high selectivity, fouling resistance, chemical stability and a wide range of cleaning methods. Additionally, disadvantages of the current PEM NF membranes are also outlined; while these may impede their use in some industrial wastewater applications, they are largely not restrictive. The effect of realistic feeds (wastewaters and challenging surface waters) on PEM NF membrane performance is also presented: pilot studies conducted for up to 12 months show stable rejection values and no significant irreversible fouling. We close our review by identifying research areas where further studies are needed to facilitate the adoption of this notable technology.

Molecular Design of the Polyamide Layer Structure of Nanofiltration Membranes by Sacrificing Hydrolyzable Groups toward Enhanced Separation Performance

Nanofiltration (NF) is an effective technology for removing trace organic contaminants (TrOCs), while the inherent trade-off effect between water permeance and solute rejections hinders its widespread application in water treatment. Herein, we propose a novel scheme of "monomers with sacrificial groups" to regulate the microstructure of the polyamide active layer via introducing a hydrolyzable ester group onto piperazine to control the diffusion and interfacial polymerization process. The achieved benefits include narrowing the pore size, improving the interpore connectivity, enhancing the microporosity, and reducing the active layer thickness, which collectively realized the simultaneous improvement of water permeance and enhancement of TrOCs rejection performance. The resulting membranes were superior to both the control and commercial membranes, especially in water-TrOCs selectivity. The effects of using the new monomers on the membrane physicochemical properties were systematically studied, and underlying mechanisms for the enhanced separation performance were further revealed by simulating the polymerization process through density functional theory calculation and measuring the trans-interface diffusion rate of monomers. This study demonstrates a novel promising NF membrane synthesis strategy by designing the structure of reaction monomers for achieving excellent rejection of TrOCs with a low energy input in water treatment.