Polyamide Membranes Research Articles

PolyMOF interlayers modulated interfacial polymerization of ultra-thin nanofiltration membranes with efficient and stable desalination performance

Ultra-thin nanofiltration (NF) membranes exhibit excellent potential in the high-efficiency removal of ions due to their superior water permeability and selectivity. However, the stability of these membranes under pressure still poses a significant challenge. In this study, we have developed an innovative polyMOF interlayer using trimesoyl chloride (TMC) cross-linked UiO-66-NH2 (denoted as UiO-66-NH2-TMC) to bolster the construction of a pressure-resistant and ultra-thin polyamide (PA) membrane, achieving stable and high-permeance desalination performance. The introduced hydrophilic and structurally reinforced UiO-66-NH2-TMC interlayer with high pore density and narrow pore size distribution provides essential benefits: (1) it ensures a uniform distribution of amine monomers, which are critical for creating defect-free PA membranes; (2) it optimizes the diffusion of amine monomers, encouraging the formation of an ultra-thin PA membrane; and (3) it enhances the overall compatibility and structural support of the membrane, significantly improving pressure resistance. Consequently, our interlayer-modulated thin-film composite (i-TFC) membrane, with 13 nm thickness, demonstrates exceptional water permeance at 25 L m−2 h−1⋅bar−1 and maintains a high sodium sulfate (Na2SO4) rejection of 96 %. Moreover, the i-TFC membranes exhibit stable performance under pressures ranging from 2 to 10 bar, display excellent long-term operational stability over 36 h, and show an anti-fouling propensity with a notable flux recovery ratio of 81.5 %. This research offers a novel approach to polyMOF interlayer application, carving a path toward designing and fabricating ultra-thin NF membranes with efficient and stable desalination properties.

Beyond nothingness in the formation and functional relevance of voids in polymer films

Voids—the nothingness—broadly exist within nanomaterials and impact properties ranging from catalysis to mechanical response. However, understanding nanovoids is challenging due to lack of imaging methods with the needed penetration depth and spatial resolution. Here, we integrate electron tomography, morphometry, graph theory and coarse-grained molecular dynamics simulation to study the formation of interconnected nanovoids in polymer films and their impacts on permeance and nanomechanical behaviour. Using polyamide membranes for molecular separation as a representative system, three-dimensional electron tomography at nanometre resolution reveals nanovoid formation from coalescence of oligomers, supported by coarse-grained molecular dynamics simulations. Void analysis provides otherwise inaccessible inputs for accurate fittings of methanol permeance for polyamide membranes. Three-dimensional structural graphs accounting for the tortuous nanovoids within, measure higher apparent moduli with polyamide membranes of higher graph rigidity. Our study elucidates the significance of nanovoids beyond the nothingness, impacting the synthesis‒morphology‒function relationships of complex nanomaterials.

Expression of Concern: Sustainable wastewater treatment by RO and hybrid organic polyamide membrane nanofiltration system for clean environment

Expression of Concern: Sustainable wastewater treatment by RO and hybrid organic polyamide membrane nanofiltration system for clean environment

A sub-10 nm polyamide nanofiltration membrane from polyvinylpyrrolidone-mediated interfacial polymerization

In conventional interfacial polymerization (IP) for thin-film composite nanofiltration membranes, the separation layer is typically thick (∼100 nm), which limits its water permeance. In this study, we successfully synthesized an ultrathin polyamide layer (∼8.6 nm) from polyvinylpyrrolidone (PVP)-mediated IP. With the surfactant concentration over its critical micelle concentration, PVP not only formed a monolayer at the water-heptane interface, but also stayed in the aqueous solution. The CO groups of PVP formed the N–H⋯O hydrogen bond interactions with N–H bonds of piperazine (PIP) monomers in the aqueous phase to slow down the diffusion of PIP monomers. Besides, PVP with amphiphilic groups minimized the interfacial tension at the water-heptane interface to regulate IP processes. By adjusting the concentration of PVP, we obtained a sub-10 nm polyamide nanofiltration membrane. Moreover, hydrophilic PVP macromolecules improved the membrane surface hydrophilicity. Due to the ultrathin thickness and improved hydrophilicity, the optimized polyamide membrane demonstrated a threefold increase in permeance compared to that of the pristine polyamide membrane without PVP addition, and maintained 98.4% rejection for 1000 ppm Na2SO4 solution. This study provides a new insight into fabricating sub-10 nm polyamide membranes for nanofiltration processes.

Ultrahighly Li-selective nanofiltration membranes prepared via tailored interfacial polymerization

Nanofiltration (NF) membrane-based separation has gained significant attention as an efficient technology for recovering lithium (Li) from salt-lake brines. However, many previously developed NF membranes are not commercially feasible because they lack sufficient Li selectivity and require the use of new monomers/chemicals or complicated fabrication processes. Herein, we propose a commercially viable method to fabricate ultrahighly Li-selective polyamide (PA) membranes by carefully tailoring a conventional interfacial polymerization process using an established piperazine (PIP)/trimesoyl chloride monomer system. The use of excess PIP endowed the fabricated membrane with enhanced PA crosslinking density and a positive surface charge, reinforcing both its size and Donnan exclusion mechanisms. Furthermore, the addition of benzyltributylammonium chloride, a cationic surfactant, to the PIP solution effectively improved the water permeance of the membrane without impairing its magnesium ion (Mg2+) rejection by loosening its PA network while enhancing its positive surface charge. Consequently, our tailor-made PA membranes with the proper pore structures and strong positive surface charges exhibited ultrahigh Li+/Mg2+ selectivity of up to 150 (under single-salt conditions) and 887 (under mixed-salt conditions), significantly outperforming commercial and other reported laboratory-made NF membranes. Our strategy provides a facile and effective means to manufacture Li-selective membranes with high commercial viability.

Fabrication of Oligoaniline-Based Electrically Responsive Polyamide Membranes for High-Performance Molecular Nanofiltration

Fabrication of Oligoaniline-Based Electrically Responsive Polyamide Membranes for High-Performance Molecular Nanofiltration

Molecular-level manipulation of polyamide membranes for high-performance H2/CO2 separation

The preparation of high-performance H2/CO2 polymer separation membranes is an urgent need to reduce the global greenhouse effect. Here, polyamide (PA) membranes with excellent H2/CO2 separation performance are produced using a facile interfacial polymerization (IP) plus heat treatment approach. The synergistic effect of the substrate-confinement and the control of the d-spacing and crosslinking degree of PA membranes creates sufficient H2 selective channels. The effects of test parameters such as feed temperature and feed pressure on membrane performance were investigated. The ultra-high H2/CO2 selectivity of 110 with excellent H2 permeance of 180 GPU is observed at 150 °C and feed pressure of 2 bar. In addition, PA membranes have high-pressure resistance (16 bar), high-temperature resistance (300 °C), and attractive durability (210 h). The excellent performance and facile preparation process make them expected to be one of the most promising membranes for H2/CO2 separation.

Tailoring thin film composite membranes for clean water production: A study on structural variations and predictive insights using machine learning

This study focuses on the fabrication of three thin film composite membranes by interfacial polymerization on polyacrylonitrile support. The objective is to explore the influence of different monomers on structural and functional features of resultant membranes for treating saline water and removing micropollutants. A new linear aliphatic amine 4A–2E was used as an aqueous monomer for the first time. Trimesoyl chloride, isophthaloyl chloride, and terephthaloyl chloride were used as organic phase crosslinkers. The resulting membranes exhibited distinctive structural features such as varied roughness and hydrophilicity, which correlated with their functional performance. The membrane roughness for 4A–2E-TPC@PAN/PET reached the highest value of Ra = 56 nm among the fabricated membranes. Moreover, permeate flux of 32, 56, and 49 L m−2 h−1 at 25 bar was measured for 4A–2E-TPC@PAN/PET, 4A–2E-IPC@PAN/PET, and 4A–2E-TMC@PAN/PET, respectively. Notably, 4A–2E-TPC@PAN/PET membrane exhibited 98% rejection of MgCl2 among the tested divalent salts. Furthermore, the membrane displayed high rejections of micropollutants, with 85% rejection for Amitriptyline. Upon chlorine exposure, the 4A–2E-TPC@PAN/PET membrane proved more resistant than both 4A–2E-IPC@PAN/PET and 4A–2E-TMC@PAN/PET membranes. Machine learning predictive insight was developed using Matérn Gaussian Process Regression model to simulate flux before and after chlorination of membranes. The results in both training and testing proved reliable predictive insight with 99% accuracy in terms of Nash Sutcliffe Efficiency. Hence, current work highlights the use of a linear amine 4A–2E and TPC crosslinker to fabricate a dense polyamide membrane and the application of ML algorithms to desalination data makes it possible to predict the membrane performance.

Understanding Rejection Mechanisms of Trace Organic Contaminants by Polyamide Membranes via Data-Knowledge Codriven Machine Learning.

Data-driven machine learning (ML) provides a promising approach to understanding and predicting the rejection of trace organic contaminants (TrOCs) by polyamide (PA). However, various confounding variables, coupled with data scarcity, restrict the direct application of data-driven ML. In this study, we developed a data-knowledge codriven ML model via domain-knowledge embedding and explored its application in comprehending TrOC rejection by PA membranes. Domain-knowledge embedding enhanced both the predictive performance and the interpretability of the ML model. The contribution of key mechanisms, including size exclusion, charge effect, hydrophobic interaction, etc., that dominate the rejections of the three TrOC categories (neutral hydrophilic, neutral hydrophobic, and charged TrOCs) was quantified. Log D and molecular charge emerge as key factors contributing to the discernible variations in the rejection among the three TrOC categories. Furthermore, we quantitatively compared the TrOC rejection mechanisms between nanofiltration (NF) and reverse osmosis (RO) PA membranes. The charge effect and hydrophobic interactions possessed higher weights for NF to reject TrOCs, while the size exclusion in RO played a more important role. This study demonstrated the effectiveness of the data-knowledge codriven ML method in understanding TrOC rejection by PA membranes, providing a methodology to formulate a strategy for targeted TrOC removal.

Double charge flips of polyamide membrane by ionic liquid-decoupled bulk and interfacial diffusion for on-demand nanofiltration

Fine design of surface charge properties of polyamide membranes is crucial for selective ionic and molecular sieving. Traditional membranes face limitations due to their inherent negative charge and limited charge modification range. Herein, we report a facile ionic liquid-decoupled bulk/interfacial diffusion strategy to elaborate the double charge flips of polyamide membranes, enabling on-demand transformation from inherently negative to highly positive and near-neutral charges. The key to these flips lies in the meticulous utilization of ionic liquid that decouples intertwined bulk/interfacial diffusion, enhancing interfacial while inhibiting bulk diffusion. These charge-tunable polyamide membranes can be customized for impressive separation performance, for example, profound Cl−/SO42− selectivity above 470 in sulfate recovery, ultrahigh Li+/Mg2+ selectivity up to 68 in lithium extraction, and effective divalent ion removal in pharmaceutical purification, surpassing many reported polyamide nanofiltration membranes. This advancement adds a new dimension to in the design of advanced polymer membranes via interfacial polymerization.

A homogeneous carbon nitride nanomodifier for promoting the water permeation of polyamide desalination membranes

Nanomaterial modification provides an effective way to enhance the separation performance of nanofiltration membranes, but the limited hydrophilicity of conventional nanostructures causes their poor solubility in solvents, affecting the quality of polyamide membranes for performance enhancement. Herein, we explored quasi-water soluble carbon nitride as a homogeneous modifier to improve the nanofiltration performance of polyamide membranes. With sufficient hydroxyl, amino, and cyano groups, the introduced quasi-water soluble carbon nitride significantly enhanced the surface hydrophilicity of the support membrane, resulting in the formation of an ultrathin functional layer for high-efficiency desalination. The high permeation flux of 108.64 L m-2h−1 (4 bar) was even twice and three times higher than those of the pristine polyamide membrane and commercial nanofiltration membranes, with a high Na2SO4 barrier rate of 98.84 % and a Cl-/SO42- selectivity of 76. This work provides an effective way to precisely regulate the structure of nanofiltration membranes for high-performance water purification.

Construction of skyloft-like polyamide membrane on tubular ceramic support for high-flux nanofiltration

Polyamide (PA) thin-film composite membrane has been widely used in water/wastewater treatment, but construction of such membrane on a three-dimensional inorganic macroporous support remains a challenge, thus limiting the further application under harsh conditions. Herein, we propose a skyloft-like PA membrane on a commercial tubular ceramic macrofiltration support. This membrane built on stilts comprises an ultrathin PA canopy of ∼ 33 nm and pillars (∼1 μm-thick) of vertically aligned CoAl-layered double hydroxide (LDH) nanosheets and covalent organic framework (COF) nanospheres. The prepared PA/OH-COF/F-COF/LDH membrane shows a high water permeance of up to 736.6 L m-2h−1 MPa−1 together with a satisfactory rejection rate of > 90 % for various small molecules including dyes and antibiotics (> ∼1 nm). The resultant membrane also demonstrates a good dye desalination performance (with a selectivity of up to 197), illustrating an outstanding permselectivty. A long-term and continuous cross-flow nanofiltration indicates a robust stability of the membrane.

Polyamide Membranes with Tunable Surface Charge Induced by Dipole-Dipole Interaction for Selective Ion Separation.

Nanofiltration (NF) has the potential to achieve precise ion-ion separation at the subnanometer scale, which is necessary for resource recovery and a circular water economy. Fabricating NF membranes for selective ion separation is highly desirable but represents a substantial technical challenge. Dipole-dipole interaction is a mechanism of intermolecular attractions between polar molecules with a dipole moment due to uneven charge distribution, but such an interaction has not been leveraged to tune membrane structure and selectivity. Herein, we propose a novel strategy to achieve tunable surface charge of polyamide membrane by introducing polar solvent with a large dipole moment during interfacial polymerization, in which the dipole-dipole interaction with acyl chloride groups of trimesoyl chloride (TMC) can successfully intervene in the amidation reaction to alter the density of surface carboxyl groups in the polyamide selective layer. As a result, the prepared positively charged (PEI-TMC)-NH2 and negatively charged (PEI-TMC)-COOH composite membranes, which show similarly high water permeance, demonstrate highly selective separations of cations and anions in engineering applications, respectively. Our findings, for the first time, confirm that solvent-induced dipole-dipole interactions are able to alter the charge type and density of polyamide membranes and achieve tunable surface charge for selective and efficient ion separation.

Affinity-based engineering of carbon nanotube embedded polyamide membranes for simultaneous desalination and boron removal

Reverse osmosis (RO) is the leading technology for obtaining drinking and irrigation water from seawater. Although currently developed RO membranes achieve high salt rejection; several challenges exist, such as permselectivity trade-off, biological/chemical contamination, and insufficient retention of small and neutral molecules like boric acid. Due to its toxicity to living organisms and strictly monitored levels in potable water, removal of boron has gained considerable attention in RO industry. Thin film nanocomposite (TFN) membranes have emerged by incorporating nanomaterials with superior water/salt selectivity into polyamide (PA) layer of composite membranes. For the first time, our group investigated the potential of carboxylated carbon nanotube (CNT) embedding in the selective layer of TFN membranes for boron removal. Within this frame, we hypothesize that embedding fillers with low boron affinity accompanied by high water permeability in the selective layer of TFN membranes improves boron removal without suffering from the permselectivity trade-off. In this study, we investigated the boron removal performance of functionalized CNT (f-CNT) embedded TFN membranes. Initially, CNTs were functionalized with three different functional groups (namely, biotin (BIO), 8-amino caprylic acid (ACA), and zwitterion (ZWT)) to narrow down the tube entrance, provide repulsive electrostatic interactions with boron, and interfere with the hydrogen bonding between water and boric acid molecules. Then, TFN membranes incorporating f-CNTs were fabricated, characterized, and tested. Showing high compatibility with PA, ZWT functionalization has increased water/boron selectivity of TFN membranes by 66% and water permeability by 44%, while maintaining the salt rejection at 98%. Furthermore, we employed molecular dynamics simulations and potential of mean force calculations to elucidate the boron exclusion mechanism in f-CNTs. With its flexible structure and “gate-keeper” ability, ZWT functional group was found to show the highest energy barrier against boron transport. Hence, engineering CNT/PA TFN membranes to promote steric hindrance and decrease solute affinity is promising for efficient removal of detrimental small molecules from seawater.

Effect of titanium oxide/reduced graphene (TiO2/rGO) addition onto water flux and reverse salt diffusion thin-film nanocomposite forward osmosis membranes

Thin-film nanocomposite (TFN) forward osmosis (FO) membranes have attracted significant attention due to their potential for solving global water scarcity problems. In this study, we investigate the impact of titanium oxide (TiO2) and titanium oxide/reduced graphene (TiO2/rGO) additions on the performance of TFN-FO membranes, specifically focusing on water flux and reverse salt diffusion. Membranes with varying concentrations of TiO2 and TiO2/rGO were fabricated as interfacial polymerizing M-phenylenediamine (MPD) and benzenetricarbonyl tricholoride (TMC) monomers with TiO2 and its reduced graphene composites (TiO2/rGO). The TMC solution was supplemented with TiO2 and its reduced graphene composites (TiO2/rGO) to enhance FO performance and reverse solute flux. All MPD/TMC polyamide membranes are characterized using various techniques such as scanning electron microscopy (SEM), atomic force microscopy (AFM), and contact angle measurements. The results demonstrate that incorporating TiO2/rGO into the membrane thin layer improves water flux and reduces reverse salt diffusion. In contrast to the TFC membrane (10.24 L m−2h−1 and 6.53 g/m2 h), higher water flux and higher reverse solute flux were detected in the case of TiO2and TiO2/rGO-merged TFC skin membranes (18.81 and 24.52 L m−2h−1 and 2.74 and 2.15 g/m2 h, respectively). The effects of TiO2 and TiO2/rGO stacking on the skin membrane and the performance of TiO2 and TiO2/rGO skin membranes have been thoroughly studied. Additionally, being investigated is the impact of draw solution concentration.Graphical

Amphiphilic Interlayer Regulated Interfacial Polymerization for Constructing Polyamide Nanofiltration Membranes with High Perm-Selectivity of Mono-/Divalent Salts.

High-quality thin-film composite (TFC) membranes with high selectivity and permeability have great significance owing to their practical applications, specifically for the accurate differentiation of monovalent and divalent ions. However, the trade-off effect between selectivity and permeability is still a big challenge due to the difficult structure adjustment of the selective layer. Herein, polydopamine (PDA) functionalized with a hydrophobic long alkane chain was first explored as a functional amphiphilic interlayer to synthesize high-quality TFC membranes via a confined interfacial polymerization (IP) reaction. The amphiphilic interlayer not only restricted the formation of the polyamide (PA) matrix in the pores of the substrate but also accelerated spatially more homogeneous polymerization and formed a PA active layer with a more uniform pore size distribution. The method may provide an effective principle for the construction of versatile polyamide-based membranes with high perm-selectivity on various supports. The NaCl/Na2SO4 separation factor of the D-8/PA membrane reached as high as 204.07, while the flux increased up to 25.71 L m-2 h-1 bar-1. This progress provides a more feasible way for the construction of high-quality TFC membranes with a devisable and creative amphiphilic interlayer for industrial application.

High performance mixed-dimensional assembled MXene composite membranes for molecular sieving

Two-dimensional (2D) separation membranes have proved their superiorities in addressing global water scarcity and recycling resources. However, current 2D membranes usually suffer from swelling and irregular stacking issues, which result in poor separation performance. Herein, we developed novel mix-dimensional assembled MXene composite membranes with high permeability and selectivity by intercalating 1D carboxylated cellulose nanofibers (CNFs) into 2D lamellar MXene nanosheets. The effects of chain length of CNFs and MXene/CNFs mass ratios on the morphologies, physicochemical properties, and separation performances of composite membranes were systematically investigated. The optimized MXC-3-L membrane exhibited superior rejection to Congo red (CR, >99%) and remarkable separation efficiency of dye/salt. The separation factors for the CR/NaCl and CR/Na2SO4 mixtures were 512.0 and 517.2, respectively. Moreover, the MXC-3-L membrane showed distinct rejections to perfluoroalkyl substances (PFAS) (>95%) with a nearly 8-fold increase of permeance in comparison with the commercial polyamide membrane. The 2D X-ray diffraction analysis verified that the intercalation of 1D CNFs into 2D MXene nanosheets via strong hydrogen bonding can regulate the nanochannel into more stable and regularly stacking lamellar structures, which plays a key role in high permeance and excellent molecular separation efficiency. In addition, 1D CNFs play an essential role as “threading” and “stitching”, shielding the defects between adjacent MXene nanosheets and improving the separation efficiencies. Our work provides new insights into design of defect-free and highly selective 2D materials-based membranes for enhanced wastewater treatment and molecular sieving.

Harmonic amide bond density as a game-changer for deciphering the crosslinking puzzle of polyamide

It is particularly essential to analyze the complex crosslinked networks within polyamide membranes and their correlation with separation efficiency for the insightful tailoring of desalination membranes. However, using the degree of network crosslinking as a descriptor yields abnormal analytical outcomes and limited correlation with desalination performance due to imperfections in segmentation and calculation methods. Herein, we introduce a more rational parameter, denoted as harmonic amide bond density (HABD), to unravel the relationship between the crosslinked networks of polyamide membranes and their desalination performance. HABD quantifies the number of distinct amide bonds per unit mass of polyamide, based on a comprehensive segmentation of polyamide structure and consistent computational protocols derived from X-ray photoelectron spectroscopy data. Compared to its counterpart, HABD overcomes the limitations and offers a more accurate depiction of the crosslinked networks. Empirical data validate that HABD exhibits the expected correlation with the salt rejection and water permeance of reverse osmosis and nanofiltration polyamide membranes. Notably, HABD is applicable for analyzing complex crosslinked polyamide networks formed by highly functional monomers. By offering a powerful toolbox for systematic analysis of crosslinked polyamide networks, HABD facilitates the development of permselective membranes with enhanced performance in desalination applications.

Acid and chlorine-resistant cations selective nanofiltration membranes derived from Hoffmann alkylation reaction

Conventional polyamide and polyester thin film composite membranes (TFCMs) are susceptible to harsh conditions, because the amide and ester bonds in them will degrade under extreme pH or chlorine oxidants. This work addressed this problem by preparing TFCMs through the Hoffmann alkylation reaction. Two monomers, i.e., 1,4,7,10-Tetraazacyclododecane (TAD) and 1,2,4,5-Tetrakis(bromomethyl)benzene (TBB), were designed and underwent interfacial alkylation on polyacrylonitrile substrate. The TAD-TBB TFCMs feature high positive charge, and show good repulsion (>90%) to divalent cations. Benefiting from the stability of “C-N-C” bonds in TAD-TBB network, both the permeance (13.7 L m−2 h−1 bar−1) and cation rejection (90.3%–91.4%) of the TAD-TBB TFCMs are stable after being immersed in 2 M HCl for 21 days. Meanwhile, the TAD-TBB TFCMs maintained good separation performance (permeance: 13.9 L m−2 h−1 bar−1, MgCl2 rejection: 90.5%–92.2%) after a total free chlorine exposure of 96000 ppm h (200 ppm for 480 h). This work reported the preparation of cation-selective membranes stable in harsh environments.

Physics-informed deep learning for multi-species membrane separations

Conventional continuum models for ion transport across polyamide membranes require solving partial differential equations (PDEs). These models typically introduce a host of assumptions and simplifications to improve the computational tractability of existing solvers. As a consequence of these constraints, conventional models struggle to generalize predictive performance to new unseen conditions. Deep learning has recently shown promise in alleviating many of these concerns, making it a promising avenue for surrogate models that can replace conventional PDE-based approaches. In this work, we develop a physics-informed deep learning model to predict ion transport across diverse membrane types. The proposed architecture leverages neural differential equations in conjunction with classical closure models as inductive biases directly encoded into the neural framework. The neural methods are pre-trained on simulated data from continuum models and fine-tuned on independent experiments to learn multi-ionic rejection behaviour. We also harness the attention mechanism, commonly observed in language modelling, to learn and infer key paired transport relationships. Gaussian noise augmentations from experimental uncertainty estimates are also introduced into the measured data to improve robustness and generalization. We study the neural framework’s performance relative to conventional PDE-based methods, and also compare the use of hard/soft inductive bias constraints on prediction accuracy. Lastly, we compare our approach to other competitive deep learning architectures and illustrate strong agreement with experimental measurements across all studied datasets.