Journal of Membrane Science Letters最新文献

Compared with traditional membrane separation methods such as distillation and chromatography, nanofiltration (NF) affords decreased waste generation and energy consumption. Despite the multiple advantages of NF and materials available for NF membranes, the industrial applicability of this process requires improvement. To address these challenges, we propose four important pillars for the future of membrane materials and process development. These four pillars are digitalization, structure–property analysis, miniaturization, and automation. We fill gaps in the development of NF membranes and processes by fostering the most promising contemporary technologies, e.g., the integration of process analytical technologies and the development of a parallel artificial nanofiltration permeability assay (PANPA) or large online databases. Moreover, we propose the extensive use of density functional theory-aided structure–property relationship methods to understand solute transport process at a molecular level. Realizing an inverse design would allow researchers and industrial scientists to develop custom membranes for specific applications using optimized properties.

Tuning the pore size distribution of hemodialysis membranes is essential for the membrane’s selectivity and significantly affects the quality of the dialysis treatment. Tailoring the membrane’s molecular weight cut-off appropriately balances the removal of middle-molecular-weight uremic toxins while avoiding albumin loss. This undesirable albumin loss is a potential side effect and concern for clinical use when aiming at increased removal of middle molecular weight molecules (middle molecules). It is hypothesized that control of the position of a narrow pore size distribution allows middle molecule removal while simultaneously counteracting the unwanted albumin loss. This study presents a comprehensive ex-vivo methodology and novel data on the balance of the clearance of middle molecules and albumin loss at different blood and dialysate flow rates using novel dialyzers. The outcomes hold significance for dialysis therapy, while the insights acquired have broader implications for the selectivity of ultrafiltration membranes.

The Theranova dialyzer shows the largest clearance for small-middle molecules. Phylther stands out with higher removal of the middle molecule YKL-40 than the other dialyzers but exhibits a significant albumin loss. Theranova demonstrates the best compromise between low albumin loss and good clearances of middle molecules.

Developing silica membranes that are highly selective for CO₂ has always been a challenge due to the small sizes of the pores and less amount of CO₂ philic sites in a typical silica network structure. Herein, we describe the fabrication of silica (tetraethoxysilane) membranes functionalized with 3-aminopropyltriethoxysilyl (APTES) and trifluoroacetic acid (TFA). An interaction generated among primary (NH₂) amines and TFA was identified, which was then also revealed by the reversible nature of CO₂ adsorption/desorption — an opposite trend from observations when using another catalyst (HCl). The resultant TEOS-APTES (TFA) membranes demonstrated CO₂ permeance of 3.8 × 10⁻⁷ mol m ^− 2 s ^− 1 Pa⁻¹ and CO₂/N₂ selectivity of 35 at 50 ⁰C via the effect of surface diffusion. This is attributed to the increased microporosity and structural variations affected by TFA, which enhanced molecular sieving and controls the CO₂-philic sites (-NHCOCF₃) via interaction with amines. This novel approach would be effective for the energy-efficient fabrication of highly CO₂-permeable membranes.

Pressure dependence of gas permeation flux for dual-phase ionic-conducting membranes is critical to the design and operation of separation or reaction processes using these membranes. However, literature on dual-phase membranes has mainly focused on temperature, rather than pressure dependence of gas permeation flux. This paper presents a theoretical approach for the development of the pressure dependence of gas permeation flux for dual-phase membranes, demonstrated with CO₂ permeation for samarium-doped-ceria (SDC)/molten-carbonate (MC) dual-phase membranes. The paper presents a model showing that gas permeation through dual-phase ionic-conducting membranes is controlled not only by the intrinsic ion (or electronic) conductivity of the materials for each phase, but also by the geometric factor defined as the ratio of the volume to tortuosity of each phase. These geometric factors for both phases are determined by the topological structure of each phase. Dual-phase membranes of the same materials can have very different pressure-dependent flux equations depending on the topological structure dictated by synthesis method and conditions. CO₂ permeation through SDC-MC membranes made of SDC with low porosity is controlled by carbonate conduction in the molten carbonate phase, leading to logarithmic CO₂ pressure-dependent flux equation. CO₂ permeation through SDC-MC membrane of SDC with intermediate porosity is controlled by oxygen ionic conduction in the SDC phase, and the CO₂ permeation flux shows power-law dependence on CO₂ pressures. The validity of the model is confirmed by comparison of the modeling results with experimental CO₂ permeation data for SDC-MC membranes. This work provides a direction for developing pressure-dependent gas permeation flux equations for various dual-phase ionic-conducting membranes.

Transforming a vast array of candidate materials into membranes with suitable morphologies and improved molecular separation performance is an arduous and costly endeavor for membrane scientists. With the advancement made in artificial intelligence and machine-learning in recent years, it is timely to ask: can machine learning methods guide gas separation membranes Fabrication? The answer is “YES”, and this article explains the justifications for this answer by systematically reviewing and analyzing the up-to-date research efforts in the field. This work aimed to explore the potential of ML algorithms as an effective and cost-saving tool in guiding the experimental process of developing the next generation polymeric membranes, and in addressing the critical needs in the field. Findings demonstrate that training Heteropolymers instead of Homopolymers, synthesizing novel polymers by an inverse design approach, and using reliable datasets that are created under the same conditions, are the most crucial factors to achieve the design intent. A path from A to Z for anyone who intends to use ML algorithms in the membranes’ synthesis process is offered. The article concludes with a brief discussion on future development prospects and open issues that are yet to be addressed for ML‐driven polymeric‐based membranes design and optimization.

Developing high-performance membranes for membrane distillation (MD) to treat highly saline industrial wastewater is of great significance. In this work, a superhydrophobic/hydrophobic/hydrophilic triple-layer membrane combining an electrosprayed superhydrophobic top layer, an electrospun hydrophobic nanofibrous intermediate layer and a hydrophilic microporous membrane substrate was fabricated by using electrohydrodynamic techniques. The top superhydrophobic surface possesses a unique surface morphology composing of hydrophobic SiO₂-polymer microbeads with nanoscaled protrusions and interconnected thin nanofibers, which contributed to the enhanced water flux for desalination in direct contact MD. By tuning the concentrations of hydrophobic SiO₂ nanoparticles and polyvinylidene fluoride-co-hexafluoropropylene for electrospraying the top layer, the triple-layer membrane showed both enhanced anti-fouling and anti-wetting properties due to the reduced liquid-solid contact area and stable Cassie-Baxter state. The triple-layer membrane exhibited stable MD performances when using real seawater and industrial flue gas desulfurization wastewater as the feed solutions, while no obvious fouling and wetting being observed even at 60% water recovery. This study provides an effective approach for fabricating a high-performance triple-layer superhydrophobic/hydrophobic/hydrophilic membrane for potential practical MD applications for industrial wastewater treatment.

Membrane-based pervaporation desalination is an effective process for freshwater resource and treatments on waste brines. Both water permeation fluxes and salt rejection are concerned for the membrane-based desalination. In this work an effective approach to increase the water permeation fluxes of the pervaporation desalination membranes has been demonstrated through utilization of matrix-polymer functionalized carbon nanotubes in creation of water permeation pathways in the membranes. With poly(vinyl alcohol) (PVA) as the matrix polymer for membrane fabrication, a small amount of PVA-functionalized CNTs (0.06 wt%) effectively increases the water permeation fluxes of the PVA membranes from 1,630 to 6,140 gm⁻²h⁻¹ (feeding solution: 3.5 wt% NaCl_(aq) at 25 °C) without sacrifice of salt rejection, corresponding to a 3.77-times of increase in water permeation flux. The membrane is also workable on concentrated salt aqueous solution (15 wt% NaCl_(aq)). The approach has the potential to be employed to other polymer membranes for pervaporation separation.

New developments in modeling solute transport in reverse osmosis (RO) membranes are based on the mechanistic description of solution friction and electromigration. In these models, the membrane charge significantly impacts the separation that occurs in the membrane through Donnan partitioning. One implication of membrane charge is that the salt permeability strongly depends on the ion concentration in the feedwater. In this study, we experimentally evaluate the effect of salinity, varied over almost two orders of magnitude (ca. 10–650mM), on four commercially available polyamide seawater RO and brackish water RO membranes. We found no significant effect of feed concentration on observed salt permeability, while the membrane performance closely resembled the specification by the manufacturers. We also demonstrate that a minor leak in the membrane provides a plausible alternative explanation to trend between concentration and salt permeability reported in other studies. The standard solution diffusion model provides a satisfactory description of our data for the membranes and feedwater conditions that we tested.

One of the most well-established frameworks for modeling multicomponent transport in nanofiltration (NF) is the Donnan-Steric Pore Model with Dielectric Exclusion (DSPM-DE). Conventional DSPM-DE characterizes transport across NF membranes through four governing membrane parameters: (1) pore radius; (2) effective membrane thickness; (3) membrane charge density; and (4) the dielectric constant inside the membrane pores. The process for quantifying these parameters is typically sequential. First, neutral solute experiments are performed to determine pore radius and effective membrane thickness. Next, charged species experiments are conducted, and the data is used to regress out the remaining parameters. The resulting regressions are often performed using local search algorithms that can struggle to provide low residuals with robust fits. In addition, this two-step approach tends to: (1) require a substantial number of charged and uncharged solute experiments; and (2) introduce assumed relationships between pore size and water flux, such as the Hagen-Poiseuille equation, which may not be representative of transport through complex pore networks. To address these issues, we propose the use of metaheuristic global optimization techniques supplemented with gradient-free local search and maximum likelihood estimation to simultaneously regress all four membrane parameters directly from charged species experiments. We validate our approach against eight independent datasets across diverse input salinities, compositions, and membranes.

Additive manufacturing based on electrospray printing has been demonstrated to fabricate polyamide membranes with separation properties similar to commercial membranes while also offering exceptional control of membrane thickness and roughness. In this work, we report on the scalability of the electrospray process to produce membrane leaves that are 10 times the area of membranes fabricated in literature through electrospray printing. The large membrane leaves exhibited salt rejection of >90% (at 2000 ppm feed salt concentration) and ∼0.7 LMH/bar flux, which is comparable to smaller printed membranes using the same process.