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

Interface-Confined Catalytic Synthesis of Anisotropic Covalent Organic Framework Nanofilm for Ultrafast Molecular Sieving

Covalent organic frameworks (COFs) have emerged as prominent membrane materials for efficiently fractionating organic molecules and ions due to their unique pore structure. However, the fabrication of free-standing COF nanofilms with high crystallinity remains an arduous undertaking, and feasible methods that can enable precise control over the film microstructure are barely reported. This work conceives an exquisite interface-confined catalytic strategy to prepare Tp-BD(OH)2 COF nanofilm with an anisotropic structure analogously to conventional polymeric membranes. Experimental data and molecular simulations reveal that the hydroxyl groups on the framework substantially capture and anchor the acid catalyst through hydrogen bonding interactions at the incipient stage of interfacial polycondensation, instigating confined catalysis and self-termination reaction at the interface. The distinctive asymmetric structure endows the Tp-BD(OH)2 COF nanofilm with a record-breaking pure water permeance of 525.3 L m-2 h-1 bar-1 and unprecedented dye/salt selectivity of 648.6, surpassing other reported COF films and state-of-the-art nanofiltration membranes, as well as enduring structural durability and chemical stability. The implemented interface-confined catalysis strategy opens up a new avenue for regulating the COF nanofilm microstructure and holds broad prospects for the rational design of high-performance membranes for sustainable water purification and treatment.

Rejection of an emerging small neutral organic micropollutant by in-situ nanofiltration membrane modification for water treatment

Nanofiltration (NF) membranes are recognized for their potential in removing organic micropollutants (OMPs). However, the limited efficiency of commercial NF membranes in removing small and neutral emerging OMPs has impeded its wide use. This study explores the effectiveness of in-situ modification of commercial NF270 membranes using two monomers for the removal of 1H-benzotriazole (BTA). For the first time, this work investigates the physicochemical properties of commercial NF270 membranes grafted with these two monomers, 3-(trimethoxysilyl)propyl methacrylate and 2-(diethylamino)ethyl methacrylate, using different surface characterization techniques. The study also evaluates the performance of both unmodified and modified membranes in the rejection of BTA and compares the results with state-of-the-art monomers. The 2-(diethylamino)ethyl methacrylate-grafted membranes show a modest enhancement of 12 % in BTA rejection. In contrast, the 3-(trimethoxysilyl)propyl methacrylate-modified membranes exhibit a remarkable 107 % improvement in BTA rejection compared to the virgin NF270 membrane, achieving the highest increase in OMP removal among current state-of-the-art monomer-modified membranes reported in previous research. This approach effectively removes BTA primarily through the mechanisms of size exclusion and hydrophobic interactions. This research presents a comprehensive strategy for surface modification of NF membranes, offering potential improvements in the rejection of small and neutral OMPs for water treatment.

Esterified Chlorine-Resistant Nanofiltration Membranes with Enhanced Removal of Disinfection Byproducts for Efficient Water Purification

The permeance-selectivity trade-off and chlorine sensitivity of conventional polyamide membranes limit further efficiency improvement and cost reduction of nanofiltration (NF) processes for drinking water treatment. To overcome these challenges, this study proposed a reconstruction-esterification strategy for the development of advanced NF membranes. Results showed that the combination of Na3PO4 solution post-treatment and polyol molecule grafting generated a thinner active layer with smaller and more uniform pores. More importantly, the critical role of alkaline post-treatment in reducing the residual amine groups of polyamide layers was revealed, which enhanced the chlorine resistance of membranes jointly with the effect of surface esterification. In comparison with the surface water purification performance of several commercial NF membranes, the obtained esterified membrane showed excellent selectivity between natural organic matter and salts, along with a reasonable water permeance. Moreover, the higher and stable removal capacity of the esterified membrane for disinfection byproducts and their precursors demonstrated its application advantage in the potential chlorination-NF-coupled process. The developed chlorine-resistant membrane and initially attempted NF filtration of chlorinated water in this study can help promote process innovation and highlight more benefits of NF technology for drinking water treatment.

Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation

Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.

Incorporating functionalized graphene oxide into diethylene triamine-based nanofiltration membranes can improve the removal of emerging organic micropollutants

The presence of emerging organic micropollutants (OMPs) in drinking and potable waters is a matter of great concern due to the health hazards associated with these. In this work, we present the preparation and application of a thin-film nanocomposite (TFN) membrane containing functionalized graphene oxide to effectively remove low-molecular-weight OMPs from water. Graphene oxide was functionalized with amino silane to enhance its cross-linking capability during the formation of the polyamide active layer via interfacial polymerization of diethylene triamine and trimesoyl chloride. The TEM analysis showed that amino silane functionalized GO had 2-3 layered sheets, while non-functionalized graphene oxide appeared multilayered or stacked. XPS analysis confirmed the successful functionalization of GO. Characterization of the membranes with advanced techniques confirmed the successful incorporation of the GO and its functionalization: spectra from Fourier Transform Infra Red spectroscopy had the characteristic peaks of GO and NH groups; scanning Electron Microscopy (SEM) images showed a continuous presence of GO nanosheets. Contact angle measurements showed the TFN membranes to be more hydrophilic than their thin film composite (TFC) counterparts. Incorporating functionalized oxide nanosheets in the active polyamide layer produced additional water permeation channels, resulting in an improvement of ∼25 % in permeate flux compared to the pristine TFC and the TFN membrane with non-functionalized GO. The removal efficiencies of four OMPs commonly found in natural waters: Amitriptylene HCl (ATT HCl) and Bisphenol-A (BPA), Acetaminophen (ACT), and Caffeine (CFN) were determined for the synthesized membranes. The TFN membrane with functionalized GO outperformed its TFC counterpart with ∼100 % removal for BPA, ∼ 90 % for CFN and ATT HCl, and ∼80 % removal for the low molecular weight ACT. The high-efficiency rejection of OMPs was attributed to the synergistic effects of size exclusion as well as the reduced specific interactions between the functional groups.

A machine learning based framework to tailor properties of nanofiltration and reverse osmosis membranes for targeted removal of organic micropollutants

Nanofiltration (NF) and reverse osmosis (RO) membranes play an increasingly important role in the removal of organic micropollutants (OMPs), which puts higher demands on the customization of membranes suitable for OMPs removal based on the rejection mechanisms. Here, the pathways of OMPs-targeted optimization for membranes were constructed by using machine learning (ML) to capture the correlations between OMPs removal efficiency with properties of membranes and OMPs. Through expertise assistance and rigorous modeling methodology, an accurate and robust Extreme Gradient Boosting (XGBoost) model was established, which could well recognize the dominant rejection mechanisms of OMPs (i.e., the size exclusion effect and electrostatic interactions). An exemplary application to another dataset of several high-risk OMPs showed how the optimized model could be used to estimate the overall efficiency of OMPs risk control and, more importantly, to provide quantitative guidance on membrane properties for specific removal targets. The satisfying prediction results demonstrated the good generalization of the ML model and consequently its ability to sensitively define the ideal membrane properties for the targeted removal of these (and any other concerned) OMPs. This study provides a feasible and universal ML-based framework to achieve the tailored selection and design of NF/RO membranes for OMPs risk control.

Biodegradation of organic micropollutants by anoxic denitrification: Roles of extracellular polymeric substance adsorption, enzyme catalysis, and reactive oxygen species oxidation

The control of organic micropollutants (OMPs) in water environments have received significant attention. Denitrification was reported to exhibit good efficiency to remove OMPs, and the mechanisms involved in are too intricate to be well illustrated. In this study, we selected nitrobenzene [NB] and bisphenol A [BPA] as model pollutants and aimed to unravel the mechanisms of Paracoccus Denitrificans in the removal of OMPs, with a specific emphasis on aerobic behavior during denitrification processes. We demonstrated the formation of extracellular superoxide radicals, i.e., extracellular •O2-, using a chemiluminescence probe and found that extracellular polymeric substance adsorption, extracellular •O2-, and microbial assimilation contributed approximately 40 %, 10 %, and 50 % to OMPs removal, respectively. Transcriptome analysis further revealed the high expression and enrichment of several pathways, such as drug metabolism-other enzymes, of which a typical aerobic enzyme of polyphenol oxidase [PPO] participates in the degradation of NB and BPA. Importantly, all the immediate products showed a significant decrease in toxicity during the aerobic activity-related OMPs degradation process based on the proposed degradation pathways. This study demonstrates the formation of extracellular •O2- and the mechanisms of extracellular •O2-- and PPO-mediated OMPs biodegradation, and offers new insights into OMPs control in widely-used denitrification treatment processes.

High-Performance Nanofiltration Membrane with Dual Resistance to Gypsum Scaling and Biofouling for Enhanced Water Purification

Nanofiltration (NF) technology is pivotal for ensuring a sustainable and reliable supply of clean water. To address the critical need for advanced thin-film composite (TFC) polyamide (PA) membranes with exceptional permselectivity and fouling resistance for emerging contaminant purification, we introduce a novel high-performance NF membrane. This membrane features a selective polypiperazine (PIP) layer functionalized with amino-containing quaternary ammonium compounds (QACs) through an in situ interfacial polycondensation reaction. Our investigation demonstrated that precise QAC functionalization enabled the construction of the selective PA layer with increased surface area, enhanced microporosity, stronger electronegativity, and reduced thickness compared to the control PIP membrane. As a result, the QAC NF membrane exhibited an approximately 51% increase in water permeance compared to the control PIP membrane, while achieving superior retention capabilities for divalent salts (>99%) and emerging organic contaminants (>90%). Furthermore, the incorporation of QACs into the PIP selective layer was proved to be effective in mitigating mineral scaling by allowing selective passage of scale-forming cations, while simultaneously exhibiting strong antimicrobial properties to combat biofouling. The in situ QAC incorporation strategy presented in this study provides valuable guidelines for the fit-for-purpose design of the selective PA layer, which is crucial for the development of high-performance NF membranes for efficient water purification.

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.