Membranes最新文献

Membrane technologies have played an instrumental role in bioseparation and downstream processing [...].

Conversion of carbon dioxide (CO₂) to methanol in a traditional reactor (TR) with catalytic packed bed faces the challenge of lower reactant conversion due to thermodynamic limitations. On the contrary, membrane reactors selectively remove reaction products, enhancing the conversion, but it is still limited, and existing designs face challenges of structural integrity and scale-up complications. Therefore, for the first time, a ceramic membrane microchannel reactor (CMMR) system was developed with 500 µm deep microchannels, incorporated with catalytic membrane for CO₂ conversion to methanol. Computational fluid dynamic (CFD) simulations confirmed the uniform flow distribution among the microchannels. A catalytic LTA zeolite membrane was synthesized with thin layer (~45 µm) of Cu-ZnO-Al₂O₃ catalyst coating and tested at a temperature of 220 °C and 3.0 MPa pressure. The results showed a significantly higher CO₂ conversion of 82%, which is approximately 10 times higher than TR and 3 times higher than equilibrium conversion while 1.5 times higher than conventional tubular membrane reactor. Additionally, methanol selectivity and yield were achieved as 51.6% and 42.3%, respectively. The research outputs showed potential of replacing the current industrial process of methanol synthesis, addressing the Sustainable Development Goals of SDG-7, 9, and 13 for clean energy, industry innovation, and climate action, respectively.

Membrane proteins remain the most challenging targets for structural characterization, yet their elucidation provides valuable insights into protein function, disease mechanisms, and drug specificity. Structural biology platforms have advanced rapidly in recent years, notably through the development and implementation of nanodiscs-discoidal lipid-protein complexes that encapsulate and solubilize membrane proteins within a controlled, native-like environment. While nanodiscs have become powerful tools for studying membrane proteins, faithfully reconstituting the compositional asymmetry intrinsic to nearly all biological membranes has not yet been achieved. Proper membrane leaflet lipid distribution is critical for accurate protein folding, stability, and insertion. Here, we share a protocol for reconstituting tailored compositional asymmetry within nanodiscs through membrane extraction from giant unilamellar vesicles (GUVs) treated with a leaflet-specific methyl-β-cyclodextrin (mβCD) lipid exchange. Nanodisc asymmetry is verified through a geometric approach: biotin-DPPE-preloaded mβCD engages in lipid exchange with the outer leaflet of POPC GUVs solubilized by the lipid-free membrane scaffold protein (MSP) Δ49ApoA-I to form nanodisc structures. Once isolated, nanodiscs are introduced to the biotin-binding bacterial protein streptavidin. High-speed atomic force microscopy imaging depicts nanodisc-dimer complexes, indicating that biotin-DPPE was successfully reconstituted into a single leaflet of the nanodiscs. This finding outlines the first step toward engineering tailored nanodisc asymmetry and mimicking the native environment of integral proteins-a potentially powerful tool for accurately reconstituting and structurally analyzing integral membrane proteins whose functions are modulated by lipid asymmetry.

This study introduces a novel hybrid model for an electromembrane stack, unifying an equivalent electrical circuit model incorporating specific resistance (RM,Rs) and capacitance (Cgs,Cdl) parameters with an empirical fouling model in a single framework. The model simplifies the traditional approach by serially connecting N (N=10) ion exchange membranes (anionic PC-SA and cationic PC-SK) and is validated using NaCl and Na2SO4 solutions in comparison with laboratory tests using various voltage signals, including direct current and electrically pulsed reversal operations at frequencies of 2000 and 4000 Hz. The model specifically accounts for the chemical stratification of the cell unit into bulk solution, diffusion, and Stern layers. We also included a calibration method using correction factors (αi) to fine-tune the electrical current signals induced by voltage stimulation. The empirical component of the model uses experimental data to simulate membrane fouling, ensuring consistency with laboratory-scale desalination processes performed under pulsed reversal operations and achieving a prediction error of less than 10%. In addition, a comparative analysis was used to assess the increase in electrical resistance due to fouling. By integrating electronic and empirical electrochemical data, this hybrid model opens the way to the construction of simple, practical, and reliable models that complement theoretical approaches, signifying an advance for a variety of electromembrane-based technologies.

High regeneration energy demand remains a critical barrier to the large-scale deployment of ethanolamine-based (MEA-based) CO₂ capture. This study adopts an Al₂O₃ ceramic-membrane heat exchanger (CMHE) to recover both sensible and latent heat from the stripped gas. Experiments confirm that heat and mass transfer within the CMHE follow a coupled mechanism in which capillary condensation governs trans-membrane water transport, while heat conduction through the ceramic membrane dominates heat transfer, which accounts for more than 80%. Guided by this mechanism, systematic structural optimization was conducted. Alumina was identified as the optimal heat exchanger material due to its combined porosity, thermal conductivity, and corrosion resistance. Among the tested pore sizes, CMHE-4 produces the strongest capillary-condensation enhancement, yielding a heat recovery flux (q value) of up to 38.8 MJ/(m² h), which is 4.3% and 304% higher than those of the stainless steel heat exchanger and plastic heat exchanger, respectively. In addition, Length-dependent analyses reveal an inherent trade-off: shorter modules achieved higher q (e.g., 14-42% greater for 200-mm vs. 300-mm CMHE-4), whereas longer modules provide greater total recovered heat (Q). Scale-up experiments demonstrated pronounced non-linear performance amplification, with a 4 times area increase boosting q by only 1.26 times under constant pressure. The techno-economic assessment indicates a simple payback period of ~2.5 months and a significant reduction in net capture cost. Overall, this work establishes key design parameters, validates the governing transport mechanism, and provides a practical, economically grounded framework for implementing high-efficiency CMHEs in MEA-based CO₂ capture.

To enhance desalination efficiency and reduce experimental costs, the development of advanced mathematical models for EMS is essential. In this study, we propose a novel hybrid approach that integrates neural networks with high-accuracy numerical simulations of electroconvection. Based on dimensionless similarity criteria (Reynolds, Péclet numbers, etc.), we establish functional relationships between critical parameters, such as the dimensionless electroconvective vortex diameter and the plateau length of current-voltage curves. Training datasets were generated through extensive numerical experiments using our in-house developed mathematical model, while multilayer feedforward neural networks with backpropagation optimization were employed for regression tasks. The resulting AI (artificial intelligence)-driven hybrid models enable rapid prediction and optimization of EMS design and operating parameters, reducing computational and experimental costs. This research is situated at the intersection of membrane science, artificial intelligence, and computational modeling, forming part of a broader foresight agenda aimed at developing next-generation intelligent membranes and adaptive control strategies for sustainable water treatment. The methodology provides a scalable framework for integrating physically based modeling and machine learning into the design of high-performance electromembrane systems.

The present study examined the safety of 86 veterinary antiparasitic drugs in mammals based on their mobility in the soil-water compartment, bioconcentration factor in fish, and blood-brain barrier permeability. An in silico analysis was performed based on biomembrane permeability descriptors, using novel multiple linear regression, boosted tree, and artificial neural network models. Additionally, intestinal absorption in humans was predicted quantitatively using pkCSM software and qualitatively using SwissADME. It was established that the majority of studied drugs are at least slightly mobile in soil, are unlikely to bioaccumulate in fish, and may be absorbed from the human gastro-intestinal tract; in addition, some of them have high potential to enter the mammalian brain.

This study presents the techno-economic optimization of a hybrid distillation-membrane process for the complete fractionation of liquefied petroleum gas (LPG), targeting high-purity propane, n-butane, and isobutane recovery. The process employs an initial distillation column to separate propane (99% purity) from a propane-enriched stream, which is subsequently fed to a two-stage membrane system using an MFI zeolite hollow-fiber membrane for n-butane/isobutane separation. Through systematic simulation and sensitivity analysis, different membrane configurations were evaluated. The two-stage process with a partial residue-side reflux configuration demonstrated superior economic performance, achieving a total operating cost of 31.58 USD/h. Key membrane parameters-area, permeance, and separation factor-were optimized to balance separation efficiency with energy consumption and cost. The analysis identified an optimal configuration: a membrane area of 800 m², an n-butane permeance of 0.9 kg·m^-2·h^-1, and a separation factor of 40. This setup ensured high n-alkane recovery while effectively minimizing energy use and capital investment. The study concludes that the optimized distillation-membrane hybrid process offers a highly efficient and economically viable strategy for the full utilization of LPG components.

The aim of this study was to investigate the effects of three lipopolysaccharides (LPS), obtained from Hafnia alvei PCM 1200, Proteus penneri 12, and Proteus vulgaris 9/57, on the fluidity of liposomal lipid membranes. The experiments were performed on liposomes composed of egg yolk lecithin (EYL) in the liquid-crystalline phase and synthetic lecithin (DPPC) in the gel phase. The experimental results were compared with data obtained from a computational model of the membrane surface layer. Membrane fluidity was assessed using EPR spectroscopy with the spin probes TEMPO (surface layer; changes in the F parameter) and 16-DOXYL (hydrophobic core; changes in the τ parameter). In EYL liposomes, all LPS samples induced a reduction in surface-layer fluidity (decrease in the F/F₀ ratio). In contrast, effects on the hydrophobic core (τ/τ₀) were observed only at low dopant concentrations (<0.2%), above which membrane fluidity plateaued. In DPPC membranes, the response was more complex: local minima in F/F₀ and maxima in τ/τ₀ were detected, indicating transient alterations in membrane stiffening and plasticization that depended on the specific LPS applied. Computational simulations of the membrane surface further confirmed the greater susceptibility of low-mobility systems (corresponding to the gel phase) to dopant-induced perturbations. In the model, the best agreement with the EPR data was obtained when an effective dopant charge of q = 3 was assumed.

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