离子膜分离领域数据静态、表征定义规范、数据驱动研究、适合机器学习、不依赖图片表达结论且数据可从表格文本提取的细分方向

The artificial intelligence – aided analysis and prediction the performance of alkaline anion exchange membranes for fuel cells are reported.

… attribute of ion exchange membranes, namely the activity coefficient. This approach effectively and accurately predicted activity coefficients across various ion exchange membranes. To …

Abstract Possessing high ionic conductivity is required to polymer-based membrane electrolytes. However, it is a challenge to evaluate the conductivity based on the structure of the polymer membrane without any measurements. We present a deep learning protocol to predict the hydroxide ion (OH-) conductivity from chemical structure information of poly (2,6-dimethyl phenylene oxide)-based anion exchange membranes (AEMs) grafting with one kind of functional cationic group. The modeling process includes data collection and feature processing, functional cationic group identification, OH- conductivity prediction and scientific law extraction. The established model achieves 99.7% of accuracy for classifying various functional cationic groups. The prediction error in OH- conductivity is ± 0.016 S/cm for quaternary ammonium based AEMs, ± 0.014 S/cm for saturated heterocyclic ammonium based ones, and ± 0.07 S/cm for those possessing imidazolium cations. The proposed protocol is powerful to assist researchers in designing the AEMs with predictable OH- conductivity, and provides a new research paradigm of the AEMs preparation.

… ionic diffusivities. In this work, we have elaborated a molecular dynamics (MD) protocol to reliably predict … tetramethylammonium (TMA) anion-exchange membrane (AEM) compensated …

… We can predict improvements in IEM performance based on reasonable inputs for C A max , (α̅ g/c ) D , and ξ. C A max is the parameter most readily controlled via the synthetic …

… In this study, multiple predictive machine learning models … parameters affecting AEMWE performance. Recursive feature … demonstrated the highest predictive performance of current …

Anion Exchange Membrane (AEM) water electrolysis is a promising solution for hydrogen production that consists of low-cost metals as the electrocatalyst and low-concentration …

… ion crossover and maximizing energy storage efficiency. This study investigates the electrochemical performance … poly (ether ether ketone) membranes through experimental data …

… theory (EMT), predictions of MMM separation performance indicated that significant improvements … The results pointed out that the membrane performance predicted by the unmodified …

… (DL) to design a performance prediction method based on the … CNN is used to construct the performance prediction model … The effectiveness of the CNN-based prediction model is …

… for ion concentrations within membranes applicable to … ion behavior within the membrane based on Donnan equilibrium theory and the condition of electroneutrality, using membrane-…

Summary Fuel cells (FCs) have attracted widespread attention as a highly efficient, clean, and renewable energy conversion technology. Proton exchange membrane (PEM), as one of the core components of FCs, plays a crucial role, and a comprehensive summary of its development is essential for promoting rapid progress in the field of sustainable energy. This article provides a comprehensive review of the development status and research trends of PEMs over the past twenty-eight years, based on statistical analysis and data mining techniques. Price, sustainability, stability, and compatibility issues are the main challenges faced by current PEMs used in FCs research. The current research focuses mainly on the characterization, performance optimization, enhancement mechanisms, and applications of PEMs in FCs. This review provides a systematic summary of PEM materials, serving as a valuable reference for the development, application, and promotion of new PEM materials in FCs.

… A total of 20 physical and chemical properties of base polymers collected by literature, experiments, quantum chemical calculation, and descriptor generators were used as explanatory …

… Proton conductivity of Nafion membranes, the key feature for their application in proton exchange membrane … We apply an exhaustive search and retrieve 3539 records from 310 original …

… -property relationships of composite membranes, a MOFs doped PEM (MOF@PEM) database was established based on the collected proton conductivity of composite membranes from …

Two-dimensional (2D) materials emerge as promising alternatives to conventional polymer-based proton exchange membranes (PEMs) due to their high proton conductivity, mechanical robustness, and surface tunability. Here we present an integrated framework combining ab initio molecular dynamics (AIMD) simulations and machine learning (ML) to accelerate the discovery of proton- and hydrogen-transport properties over 866 nonmetallic 2D materials. Three ML models were trained using AIMD-derived permeation barriers from 488 materials, with Random Forest achieving the highest accuracy and revealing structure–property relationships that govern proton transport. Critical descriptors, including proton−atom distance, pore size, interlayer spacing, and electron affinity, emerged as key predictors of permeation behavior. H+/H2 selectivity through additional AIMD simulations allowed identifying 18 promising candidates, including the experimentally studied graphene and hexagonal boron nitride, thus supporting the robustness of our approach. Experimentally synthesized but barely explored materials, including 2D Si, Ge, TeC, TeCl, GeSe and CSe, emerged as strong candidates for proton conducting membranes. The framework further highlights theoretically stable compounds as unexplored opportunities for PEMs. By integrating atomic-scale simulations with data-driven models, this work provides both fundamental insights into proton permeation mechanisms and practical guidance for designing selective, high-performance nanomaterials for hydrogen energy technologies.

… performance, unveils the intricate quantitative structure–property relationships of SPAEs, and provides valuable guidance for rational design of novel proton exchange membranes. …

Ion-exchange membranes (IEMs) have received … In this background, modeling the ion-exchange membrane … on the basis of conductivity measurements together with the data of …

Abstract Accurate measurements of membrane ionic conductivity are important for advancing the fundamental understanding of electric field-driven ion transport in ion-exchange membranes. There is no standardized technique for performing such measurements on membranes equilibrated with salt solutions, despite a longstanding interest in this topic. As a result, discrepancies between reported ionic conductivity values for common commercial membranes are often found in the open literature. This study examines ionic conductivity measurements based on the experimental technique in which a membrane directly contacts the electrodes. In the through-plane configuration of this technique, external resistances due to the interfacial region between the membrane and electrodes contribute significantly to the total measured resistance, particularly for thin membranes equilibrated with dilute salt solutions. A non-invasive approach to account for such external resistances based on performing the measurements with membranes having different thicknesses is presented. The total resistance of the electrochemical cell containing membranes with different thicknesses was measured via electrochemical impedance spectroscopy, and the results were extrapolated to zero thickness to determine the magnitude of the external resistances. The membrane ionic conductivity results obtained from this approach were compared to those obtained from measurements utilizing the in-plane configuration, in which external resistances were negligible, as well as measurements in which external resistances were eliminated by replacing the solution on the membrane surface with a highly conducting electrolyte solution. The salt concentration dependence of the ionic conductivity of homogeneous ion-exchange membranes is discussed within the Nernst-Einstein framework.

Alkaline anion exchange membrane (AEM)‐based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large‐scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial‐and‐error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid‐catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation‐type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron‐withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low‐cost monomers given by machine learning, the large‐scale synthesis of anilinium‐based AEMs confirms the potential for commercial applications.

… copolymers for anion exchange membranes (AEMs). The … the OH − conductivity, the conductivity – dimensional stability … construction of robust regression and classification predictive …

… Area resistance was determined by linear regression of the potential difference versus … to the resistance calculated based on the conductivity of 0.5 mol/L NaCl (63.99 mS/cm). (49) …

… regression for gross power density which provides a better understanding on the dominant performance-determining membrane … towards tailoring ion exchange membranes for RED …

This article describes the development of a sub-30 μm thick LDPE-based radiation-grafted anion-exchange membrane (RG-AEM) with high performance characteristics when fully hydrated. This RG-AEM had a OH− anion conductivity of 200 mS cm−1 (80 °C in 100% relative humidity (RH) environments), which led to a H2/O2 anion-exchange membrane fuel cell (AEMFC) performance of 2.0 W cm−2 (80 °C, RH = 92% environments, a PtRu/C anode, and a Pt/C cathode) and a H2/air (CO2-free) AEMFC peak power density of 850 mW cm−2 with a (non-platinum-group) Ag/C cathode electrocatalyst. When hydrated in a RH = 100% N2 (CO2-free) atmosphere, the OH− form of this RG-AEM shows <7% degradation after 500 h at 80 °C, with the extent of degradation being highly similar to that when measured using three different techniques (decrease in conductivity, decrease in ammonium content as measured using Raman spectroscopy, and decrease in ion-exchange capacity).

Abstract Redundancy analysis (RDA) was used to study the effect of eight physicochemical properties of six filtration membranes (PES, PVDF, CF55, S11, S11+ and S11−) on the performance of electrodialysis with filtration membranes (EDFM) in terms of selective peptides migration. The whey hydrolysate anionic and cationic peptides migrations were quantified and identified. PVDF and CF55 were not selective to the contrary of PES, S11, S11+ and S11−. It appeared that at least two properties among zeta potential, pores/surface hydrophilicity, porosity, and roughness were significantly related to the migration of ALPMHIR, IDALNENK, LIVTQTMK, TKIPAVFK, TPEVDDEALEK, TPEVDDEALEKFDK or SLAMAASDISLLDAQSAPLR. These results suggested that the peptides migrations were mainly influenced by the membrane’s pore size, and the types of interactions (electrostatic and hydrophilic/hydrophobic) occurring between peptides and membrane. Besides, statistical models for each peptide were also established to predict their specific migration through variety of membranes having different physicochemical properties. To the best of our knowledge, it is the first time that such a method was applied on peptide migration during electrodialysis with filtration membranes.

A 2-dimensional multicomponent ion transport model based on Nernst-Planck (NP) equation and electroneutrality assumption is developed for an electrodialysis (ED) cell operated in the ohmic regime. The flow in channels are assumed incompressible, isothermal, and laminar. Donnan equilibrium and flux continuity are considered at ion-exchange membrane (IEM)-solution interfaces. To account for tortuosity effects inside membranes, effective ionic diffusion coefficients are calculated using membranes water volume fractions. The developed multicomponent model is used to elucidate the effects of feed solution properties, cell properties, system hydrodynamics, operational conditions, and membrane properties on selective divalent ion removal in the cell. The results indicate that the selective removal of divalent ions improves with decreasing the cell length, imposed potential, and ionic strength of feed water. Enhanced mixing in spacer-filled cell also promotes selective divalent ion removal. Higher concentrations of fixed charges on the membranes results in greater selectivity toward divalent ions at short cell length and low imposed potentials. With equal concentrations of fixed charges, membranes with high water content are less favorable for selective divalent ion removal. The developed framework enables the optimum selection of cell design, IEMs, spacer design, and operational conditions to selectively remove ions from multicomponent solutions.

Ion selective separations are becoming increasingly important in many water and energy applications. Ion exchange membranes could address these separation challenges, but advanced understanding of the structure-property relationships that connect polymer chemistry and membrane morphology to selectivity properties is needed. Membrane-based ion separations can be driven by concentration or electric field driving forces, and selectivity is generally defined as counter-ion to counter-ion selectivity or counter-ion to co-ion selectivity. Characterization techniques depend on the definition of selectivity of interest. This review discusses those definitions of selectivity and the methods used to characterize ion selectivity in membranes. Additionally, need for additional data, opportunities for high throughput analysis, and opportunities for connecting molecular simulations with experimental data are discussed.

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering. Ion separation membranes are of importance for a range of applications, including water treatment, raw material recovery, gas separation, and fuel cells, but traditional research and development methods can be expensive and time-consuming. Here, the authors review the capabilities and limitations of artificial intelligence in the design of high performing ion-selective membranes.

… The present study aimed to explore the application of machine learning-based tools to study … membranes (FM) on peptide migration during electrodialysis with filtration membranes (…

… Bipolar membrane electrodialysis (BMED) is a promising technology for Li recovery from … In this study, we developed a machine-learning-based modeling framework using an artificial …

… computational-aided membrane development that will … a membrane screening tool. Additionally, a multifunctional optimization approach using machine learning or artificial intelligence …

Nanofiltration technology holds significant promise for the selective separation of monovalent and multivalent ions, such as lithium (Li) and magnesium (Mg), during Li extraction from salt lakes. Nevertheless, optimizing polyamide nanofiltration membranes for selective ion separation remains inherently challenging due to the complex interactions between ions and membrane structural and experimental parameters, making the ion transport mechanism ambiguous. This work employed a machine learning (ML) approach to identify and comprehensively understand the features that influence the membrane permeability and selectivity using a comprehensive dataset, which encompassed fabrication parameters, experimental conditions, membrane properties, and single salt rejection performance. Initially, ML algorithms accurately predicted intrinsic membrane properties using only fabrication parameters but struggled to predict permeation and selectivity when combining fabrication parameters or membrane properties with experimental conditions. To address this limitation, salt rejection performance was incorporated, and various combinations of input variables were systematically compared to identify optimal input configurations for robust ML algorithms capable of accurately predicting membrane permeability and selectivity. Using the Shapley additive explanation (SHAP) method, we found that membrane permeability was mainly determined by fabrication parameters such as substrate type and heat curing temperature. While these parameters also influenced molecular weight cut-off (MWCO) and zeta potential, they did not fully reflect the physicochemical factors governing ion separation. In contrast, MgCl2 rejection served as a more integrative and informative descriptor, capturing both pore structure and surface electrostatic effects in predicting Li/Mg selectivity. These findings underscore the necessity of a multifaceted approach in modeling membrane performance, integrating both intrinsic properties and external factors to achieve optimal ion selectivity.

… Here, we present a data-driven, interpretable machine learning … sparsity and enhance nonlinear learning robustness under data… resource recovery and membrane separation processes …

… introduces an efficient electrodialysis (ED) system with anion exchange membranes (AEMs) … characteristics was quantified leveraging machine learning techniques. It demonstrated that …

… Bipolar membrane electrodialysis (BMED) is an effective method for directly recovering acids and bases from wastewater containing Na 2 SO 4 . This study developed a Temporal …

Membrane electrode assembly (MEA) electrolyzers hold promise for industrial‐scale CO2 reduction applications. Almost all related studies are based on experiments; however, the multidimensionality and complexity of data present significant challenges to its optimization design. To promote the development of MEA electrolyzers from the perspective of data science, this work constructs an MEA electrolyzer device data dataset, which is composed of 501 devices from 204 relevant literature. By analyzing the data using statistical methods, the potential reasons for the device performance differences are identified. Subsequently, three indicators, including the 1st product, total current density, and the 1st product Faradaic efficiency, are selected and predicted by various machine learning (ML) algorithms. Random forest, gradient boosting, and support vector machine models are found to be optimal for each indicator, respectively. Guided by insights from interpretable ML analysis, an effective MEA electrolyzer featuring an Ag/C‐based catalyst was developed, which achieved a CO Faradaic efficiency of 100% at a current density of 200 mA cm−2 and stably operated at a cell voltage of 2.7 V over 100 h. Besides providing rich information and underlying interactions of the complicated parameters in MEA design, this work aims to motivate MEA optimization and its data management.

… regression tree modeling as a part of machine learning were performed on a database constructed on PEM (polymer electrolyte membrane) electrolysis with 789 data points from 30 …

Membrane technologies play a vital role in sustainable development due to their efficiency in separation, purification, and chemical processing applications. However, the discovery and optimization of new membrane materials remain largely reliant on trial-and-error experimentation, limiting the pace of innovation. Artificial intelligence (AI) and machine learning (ML) are increasingly being applied to overcome these limitations by enabling data-driven insights, predictive modeling, and rapid material design. These computational approaches have shown significant promise in accelerating membrane fabrication, improving process simulation, detecting and mitigating fouling, and enhancing membrane characterization. This review provides a comprehensive overview of the recent advancements in the integration of AI and ML within membrane and material science. Fundamental AI and ML concepts relevant to membrane science are discussed, together with their applications in membrane fabrication, performance prediction, process modeling, fouling control, and membrane design. Challenges related to data quality, model interpretability, and the integration of domain-specific knowledge are also highlighted, along with potential future research directions. Compared with conventional empirical approaches, the advantages of AI and ML in handling complex, multivariate datasets and accelerating innovation are demonstrated. Overall, this review underscores the transformative potential of AI and ML in developing next-generation membranes with improved efficiency, selectivity, and sustainability across various industrial applications. Although several reviews have explored ML applications in membrane processes, comprehensive integration across material design, fabrication, fouling control, optimization, and process modeling remains limited.

… membrane-based lithium extraction is essential from unconventional water sources to supply the high value materials … by complex parameters of membrane properties and operational …

… , yet experimental optimization of materials and operating conditions is prohibitively … machine learning (ML) framework integrating predictive modeling, explainable artificial intelligence (…

… and determine Young's modulus of the membrane at a wide range of temperatures (−20–85… with experimental data we propose that ion-rich water domains in PFSA membrane are …

… the structural parameters f and α and transport parameters κ iso and G, characterizing the ions transport in the phase I of the membrane … are basic to membrane characterization. The …

… membrane by heating in water at different temperatures could have significant impacts on its structure/property relationship, … efforts on developing composite ion-conductive membranes. …

… the experimental data. A correlation of the estimated pore sizes with membrane permeability data … than K or Na salts possessing the same anion as the Li salt, as the Li ion requires the …

… to elucidate the structure–property relationship, especially with … reveal structure–property relationships in a relevant system. … All rights reserved, including rights for text and data mining …

… properties, thermal stability and ion exchange property. In this study, S-PEEKs based membranes were evaluated for ion … The microstructures of S-PEEKs membranes were studied by …

Abstract Reverse electrodialysis (RED) is an electro-membrane process to harvest renewable energy from salinity gradients. RED process models have been developed in the past, but they mostly assume that only NaCl is present in the feedwaters, which results in unrealistically high predictions. In the present work, an existing simple model is extended to accommodate the presence of magnesium ions and sulfate in the feedwaters, and potentially even more complex mixtures. All power loss mechanisms deriving from the presence of multivalent ions are included in the new model: increased membrane electrical resistance, uphill transport of multivalent ions from the river to the seawater compartment, and membrane permselectivity loss. This new model is validated with experimental and literature data of membrane electrical resistance (at 10 mol. % MgCl2 for the CEMs and 25 mol. % Na2SO4 for the AEMs), RED stack performance (up to 50 mol. % MgCl2 or Na2SO4 in the feedwaters), and ion transport (at 10 mol. % MgCl2 or Na2SO4 in the feedwaters) showing very good agreement between model predictions and experimental data. Finally, we showed that the developed model not only describes experimental data but can also predict RED performances under a variety of conditions and cross-flow configurations (single-stage with and without electrode segmentation, multi-stage in co-current and counter-current mode) and feedwater compositions (only NaCl, with Na2SO4, with MgCl2, and with MgSO4). It thus provides a very valuable tool to design and evaluate RED process systems.

… of the geometrically modified devices for membrane active area, electrolyte thickness, and flow mode of the electrolyte was predicted and also verified for membrane active area. This …

Reverse electrodialysis (RED) is a technology to generate electricity using the entropy of the mixing of sea and river water. A model is made of the RED process and validated …

Abstract The prospects and potential of Reverse Electrodialysis (RED) for energy harvesting from natural streams with salinity gradient demand more in-depth studies to understand and overcome the limitations posed by divalent ions. Power performance is greatly influenced by the ionic resistance displayed by the alternating cation and anion exchange membranes (CEMs and AEMs, respectively) housed in RED stacks, which in turn is determined by the type and concentration of ions and counter-ions in the water streams. The effects of divalent ions on power output have been experimentally approached in several works by using real or synthetic water. However, the development of comprehensive models including the effect of divalent ions on membrane resistance and power performance under different scenarios is still very scarce. Thus, this work investigates experimentally the effect of ion species on membrane resistance, providing for the first time mathematical correlations useful to predict power performance in RED stacks under a wide range of compositions of salinity gradient solutions. To this end, electrochemical impedance spectroscopy (EIS) measurements have been performed for CEM and AEM commercial membranes in contact with different concentration of NaCl solutions and including different mixtures of divalent ions (Ca2+, Mg2+, SO42−). These correlations have been implemented in a previously developed model to determine power outputs as function of ion mixture compositions. Scenarios of general interest for RED practical implementation have been addressed; specifically, solutions with a composition representative of seawater or high salinity brines have been studied as high concentration solutions (HCS) and, on the other hand, typical concentrations of wastewater treatment plant effluents, river water or brackish water from desalination plants were used as low concentration solutions (LCS).

The theoretical energy density extractable from acidic and alkaline solutions is higher than 20 kWh m-3 of single solution when mixing 1 M concentrated streams. Therefore, acidic and alkaline industrial wastewater have a huge potential for the recovery of energy. To this purpose, bipolar membrane reverse electrodialysis (BMRED) is an interesting, yet poorly studied technology for the conversion of the mixing entropy of solutions at different pH into electricity. Although it shows promising performance, only few works have been presented in the literature so far, and no comprehensive models have been developed yet. This work presents a mathematical multi-scale model based on a semi-empirical approach. The model was validated against experimental data and was applied over a variety of operating conditions, showing that it may represent an effective tool for the prediction of the BMRED performance. A sensitivity analysis was performed in two different scenarios, i.e. (i) a reference case and (ii) an improved case with high-performance membrane properties. A Net Power Density of ~15 W m-2 was predicted in the reference scenario with 1 M HCl and NaOH solutions, but it increased significantly by simulating high-performance membranes. A simulated scheme for an industrial application yielded an energy density of ~50 kWh m-3 (of acid solution) with an energy efficiency of ~80-90% in the improved scenario.

Salinity gradient energy is a sustainable, renewable, and clean energy source. When waters with different salinities are mixed, the change in Gibbs free energy can be harvested as energy and only brackish water remains. Reverse electrodialysis is one of the technologies that can harvest this sustainable energy source. High power densities have been obtained in small lab scale systems, but translation to large industrial scale stacks is essential for commercialization of the technology. Moreover, power density is an important parameter, and efficiency, i.e., the amount of energy harvested compared to the amount of energy available in the feed waters, is critical for commercial processes. In this work, we systematically investigate the influence of stack size and membrane type on power density, thermodynamic efficiency, and energy efficiency. Results show that the residence time is an excellent parameter for comparing differently sized stacks and translating lab scale experimental results to larger pilot stacks. Also, the influence of undesired water permeability and co-ion diffusion (as reflected in permselectivity) is clearly visible when measuring the thermodynamic efficiency. An averaged thermodynamic efficiency of 44.9% is measured using Fujifilm Type 10 anion exchange and cation exchange membranes that have low water permeability and high permselectivity. This value comes close to the thermodynamic maximum of 50%.

Abstract The co-generation of electricity and electrodialysis of seawater in a hybrid system is a promising approach to overcome water scarcity. Reverse electrodialysis harvests energy from the salinity gradient, where seawater is used as a high salinity stream while secondary treated wastewater can be used as a sustainable low salinity stream. Treated wastewater contains organic micropollutants, which can be transported to the seawater stream. The current research establishes a connection between adsorption and transport of organic micropollutants in ion exchange membranes, using a cross-flow stack in adsorption and zero-current experiments. To mimic the composition of treated wastewater, a mixture of nineteen organic micropollutants of varied physicochemical characteristics (e.g. size, charge, polarity, hydrogen donor/acceptor count, hydrophobicity) at environmentally relevant concentrations was used. Depending on the charge, micropollutants develop different types of mechanisms responsible for short-distance interactions with ion-exchange membranes, which has a direct influence in their transport behavior. This study provides a rational basis for the optimization/design of next-generation ion-exchange membranes, in which the permeability toward organic micropollutants should be also included. This investigation highly contributes to understanding the potential hazard posed by organic micropollutants in reverse electrodialysis in seawater desalination systems, where treated wastewater is used as a low salinity stream.

… while the permeate salinity is fixed at 200 ppm. The results showed that the developed regression models predict the specific energy consumption and the required membrane area with …

Mixed matrix membranes (MMMs) are renowned for their exceptional gas separation capabilities. In this work, high‐throughput computing screening and machine learning are employed to evaluate the CO2 separation performance of 54117 MMMs composed of 9 polymers and 6013 metal–organic frameworks (MOFs). The structure‐property relationships of MMMs are analyzed for 4 binary mixtures (CO2/X, X = CH4, N2, H2, O2), and the best‐performing combinations of MOFs and polymers are found, with which the MMM performance exceeded the Robeson's upper limit. Then, a stacked ensemble regression model with high accuracy (average R2 = 0.96) is trained, demonstrating excellent extrapolation capability (R2 = 0.95) for new MMMs containing 6FDA‐DAM. Furthermore, by utilizing Shapley Additive Explanations and data segmentation, it is identified that the pore limit diameter and largest cavity diameter in MOF features and the fractional free volume and density in polymer features are of paramount importance. Two extrapolation methods are compared and found that transfer learning is better for predicting CO2 separation performance in MMMs and designing new materials with large datasets. Finally, an interactive desktop software is developed to assist researchers in rapidly and accurately calculating the CO2 separation performance of MMMs. This work presents a novel approach for the rapid evaluation of high‐quality MMMs and the efficient calculation of gas permeation rates within membranes.

… four machine learning models to evaluate the membrane separation process. The used models … contactor as well as processing conditions are listed in Table 1 which are used for CFD …

… , and mixed matrix membrane). Subsequently, the data ecology in gas separation membranes … the development of gas separation membranes through machine learning are proposed. …

… to advancing data-driven modeling in membrane-based solar … approaches can improve membrane operations to create more … models are implemented for membrane separation and the …

… (see Table S3). This is likely due to the unique ordered well-defined pore structure of the COF-LZU1 … All rights reserved, including rights for text and data mining and training of artificial …

… of the membrane (point o) and subscript ℓ represents the fluid at the permeate surface of the membrane (point ℓ). Frequently, membrane separation factors are called membrane …

… The solubility selectivity, on the other hand, is determined by the difference of the … tabular summary of such data is presented as an Appendix. The mixed-gas permeability data …

… Table 1 also shows the variation in solution viscosity with PVP-… for gas separation or reverse osmosis polymer membranes. … , including those for text and data mining, AI training, and …

… 2 by the number of journal articles found in the scientific database Scopus. The article search was … This work shows the potential for improving membrane properties by changing the …

… exchange membranes show greater self-diffusion coefficients for water and lower water binding compared to sulfonated proton exchange membranes, but their transport properties in …

… properties as a proton exchange membrane. … search for more in depth understanding of the link between the chemical composition of the polymer and its resulting membrane properties …

With the rapid growth and development of proton-exchange membrane fuel cell (PEMFC) technology, there has been increasing demand for clean and sustainable global energy …

The maritime industry is witnessing a transformative shift towards sustainable and energy-efficient propulsion systems to address environmental concerns and operational efficiency. …

Abstract Ion selective membrane (ISM) electrodes are widely used for selective ion sensing applications. Recently, new modalities have emerged whose operation involves active electrical polarization of the media, and current theoretical models are unsuitable to predict their behavior beyond near-equilibrium conditions. Here, we apply numerical modeling of physicochemical transport in such systems to study mechanisms of interfacial ion transfer and their role in limiting processes. Importantly, our analysis suggest that membrane-phase complexation (MC) has strong merits as a replacement for interfacial complexation (IC) in theoretical treatments. For our purpose, we have developed a highly detailed model based on Nernst–Planck–Poisson (NPP) with kinetic reactions of first-order. The solutions were derived in terms of logarithmically transformed concentration variables, allowing us to input experimentally determined stability coefficients. A unique process, referred to here as the reaction boundary layer (RBL), is a characteristic of MC, and we found it could have a significant impact on current–voltage (I–V) characteristics and transfer selectivity. Together with other well-known processes such as electrically driven diffusion, the RBL dictates the limits of ISM electrical polarization. Using our model, we demonstrate that operating outside these limits results in ingress of interfering ion species and concurrent loss of transfer selectivity.

… membrane fixed charge density on ion flux and selectivity … membrane properties on the trade-off between ion selectivity … using this innovative simulation and modeling strategy. This is …

… -based MCSM model grounded in the physical nature of porous media, providing clear insights into ion transport behavior and the underlying mechanism of ion selectivity. The …

… A dual-sorption-site model in which both miscible and phase-separated water droplets are present in ion-selective, poly(vinyl chloride) (PVC)-based membranes is applied to data …

… membrane models as we know them today: carrier membranes of constant composition. The … are the equations used to describe the response time of ion-exchange type membranes: …

The theoretical models for ISEs almost exclusively assume thermodynamic equilibrium at the membrane/solution phase boundary. In this report, we present a new, congruent model which combines first order reaction kinetics of ion-exchange at the phase boundary and diffusional mass transport in the adjoining phases in the continuity equation. The influence of the rate-constant in the new kinetic model has significant impact on the predicted transients corresponding to instantaneous change in the sample solution composition. The simulated transients generated with the new model coincide with the transients recorded in common potentiometric experiments, e.g., with transients recorded upon step change in the primary or interfering ion concentrations. The simulated transients also align well with previously published transients representing special cases of potentiometry (e.g., super-Nernstian response, non-Nernstian responses in the presence of highly interfering ions). The implementation of the kinetic model for simulating the transients in the water layer test also resulted in a better agreement with the experiments compared to the previous models.

… Unlike those empirical models reported in the literature, this work reports … ion selective adsorption model with the Donnan effect considered for movable ions distributed in the membrane …

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Membrane transport is generally thought to occur via an alternating access mechanism in which the transporter adopts at least two states, accessible from two different sides of the membrane to exchange substrates from the extracellular environment and the cytoplasm or from the cytoplasm and the intracellular matrix of the organelles (only in eukaryotes). In recent years, a number of high resolution structures have supported this general framework for a wide class of transport molecules, although additional states along the transport pathway are emerging as critically important. Given that substrate binding is often weak in order to enhance overall transport rates, there exists the distinct possibility that transporters may transport the incorrect substrate. This is certainly the case for many pharmaceutical compounds that are absorbed in the gut or cross the blood brain barrier through endogenous transporters. Docking studies on the bacterial sugar transporter vSGLT reveal that many highly toxic compounds are compatible with binding to the orthosteric site, further motivating the selective pressure for additional modes of selectivity. Motivated by recent work in which we observed failed substrate delivery in a molecular dynamics simulation where the energized ion still goes down its concentration gradient, we hypothesize that some transporters evolved to harness this ‘slip’ mechanism to increase substrate selectivity and reduce the uptake of toxic molecules. Here, we test this idea by constructing and exploring a kinetic transport model that includes a slip pathway. While slip reduces the overall productive flux, when coupled with a second toxic molecule that is more prone to slippage, the overall substrate selectivity dramatically increases, suppressing the accumulation of the incorrect compound. We show that the mathematical framework for increased substrate selectivity in our model is analogous to the classic proofreading mechanism originally proposed for tRNA synthase; however, because the transport cycle is reversible we identified conditions in which the selectivity is essentially infinite and incorrect substrates are exported from the cell in a ‘detoxification’ mode. The cellular consequences of proofreading and membrane slippage are discussed as well as the impact on future drug development.

… transfer characteristics and performance index under different profiled membrane microstructures. Data-driven deep learning models are constructed for microstructure shape generation…

… assuming that ion permeation across all membranes, whether … particular membrane are direct consequences of the type of ion-… The continuous curves are computed from the data of Fig. …

Tremendous progress in two-dimensional (2D) nanomaterial chemistry affords abundant opportunities for the sustainable development of membranes and membrane processes. In this review, we propose the concept of mixed dimensional membranes (MDMs), which are fabricated through the integration of 2D materials with nanomaterials of different dimensionality and chemistry. Complementing mixed matrix membranes or hybrid membranes, MDMs stimulate different conceptual thinking about designing advanced membranes from the angle of the dimensions of the building blocks as well as the final structures, including the nanochannels and the bulk structures. In this review, we survey MDMs (denoted nD/2D, where n is 0, 1 or 3) in terms of the dimensions of membrane-forming nanomaterials, as well as their fabrication methods. Subsequently, we highlight three kinds of nanochannels, which are 1D nanochannels within 1D/2D membranes, 2D nanochannels within 0D/2D membranes, and 3D nanochannels within 3D/2D membranes. Strategies to tune the physical and chemical microenvironments of the nanochannels as well as the bulk structures based on the size, type, structure and chemical character of nanomaterials are discussed. Some representative applications of MDMs are illustrated for gas molecular separations, liquid molecular separations, ionic separations and oil/water separation. Finally, current challenges and a future perspective on MDMs are presented.

… at the effect that membrane hydration and temperature have upon the nanostructure of the membrane as well as the mobility of water and hydronium ions within the membrane. Their …

… Following this reasoning, triazolium-based ionic liquids … toxicity data that we know of for triazolium-based ionic liquids. In … are the major structural components of membranes. Narcosis …

… copolymer membranes with a range of ion exchange capacity … imaging of the membranes, reveals that membranes with a … The scattering data were collected with a Bruker Hi-Star two-…

… Using the physical and chemical microenvironments of ion nanochannels as the central pivots, the structure–property relationships are elucidated based on the core physical and …

… reverse electrodialysis (RED) is economically limited by the relatively high ion-exchange membranes … Additionally, the shadow effect of non-conductive spacers reduces the membrane …

Abstract The impact of fouling on the performance of Reverse Electrodialysis operated with highly concentrated brine is a poorly investigated area. In this work, the fouling propensity and stability of Ion Exchange Membranes (IEMs), developed by Fujifilm Manufacturing Europe BV (The Netherlands), is investigated under the condition of seawater and brine. The fouling propensity of the IEMs was depicted by the determination of the Gibbs energy barrier of based-on the Classical Nucleation along with the Theoretical modeling of heterogeneous nucleation as a function of electrochemical (contact angle, permittivity, charge density) and morphological (roughness) membrane properties validated by CaCO3 precipitation. Results indicate that Cation Exchange Membranes (CEM) are more susceptible to the scaling due to the reduced energy barrier of heterogeneous nucleation. FTIR-ATR analysis on six months-aged membranes samples indicated a partial modification in the chemical structure of Anion Exchange Membranes (AEM) induced by the organic fouling associated with humic substances. The tensile tests demonstrated substantial mechanical stability of IEMs. Lab-scale RED tests operated with artificial brine over 30 days showed a significant increase in pressure drop through feed channels due to significant colloidal fouling along with a 23% reduction of maximum gross power density with consequent decrease of net power density.

… membrane technique for the treatment of dyehouse effluents for reuse. Some data is reported in Table 5. … All rights are reserved, including those for text and data mining, AI training, and …

… Here we show that data mining of a computational screening … mol −1 , respectively (see Extended Data Table 1). The parallel … MOFs for various gas separation and storage applications …

… This paper explores RTILs as membrane materials for acid gas separations, specifically … Table 2 gives the CO 2 gas solubilities for the tested RTILs of this study. All the data in Table 2 …

… into membranes and characterized to relate its structural properties to membrane separation … Comparing with typical characteristics of MF and UF membranes in Table 2, it can be seen …

… The performance of a gas separation membrane is commonly … the selectivity towards one component of a gas pair (α A/B … Pure gas permeation results are given in Table 1 for a PIM-1 …

… gas separation membranes. This paper attempts to develop a method for correlating this data … The solid lines were calculated from the values of A n and B n in Table 2. The improvement …

… membrane, a series ions or molecules with different sizes have been separated (Figure 4 b; Supporting Information, Table S1… All rights reserved, including rights for text and data mining …