Chem & Bio Engineering最新文献

Metal-organic framework (MOF) membranes with adjustable angstrom-scale channels and versatile topological structures are promising for molecular separation. However, it remains a challenge to construct MOF membranes with precisely tuned pore sizes and tailored molecular affinity for achieving high-efficiency liquid molecular separation, particularly for organic azeotropic mixtures. Herein, we proposed an in situ macromolecule incorporation strategy to fabricate a Zr-MOF membrane with a tunable pore microenvironment for pervaporation separation of butanol-water and ethanol-ethyl acetate azeotropic mixtures. The membranes were prepared by an in situ interfacial growth process. The amino macromolecules (poly-(ether imide), PEI) were dispersed into a metal cluster solution, followed by coordination with ligands on the substrate surface for membrane synthesis, wherein the macromolecule was in situ incorporated within MOF pore apertures. The incorporated macromolecule narrowed the membrane pore size and provided molecularly accelerated transport sites, thus facilitating the fast and selective transport of water or ethanol molecules. When incorporating PEI (M _W = 10,000 g·mol^-1) at a content of 1 wt %, the resulting MOF membrane displayed excellent separation performances with a total flux of 2.22 kg·m^-2·h^-1 and a separation factor of 140 for the ethanol-ethyl acetate system, and a total flux of 8.52 kg·m^-2·h^-1 and a separation factor of 1620 for the butanol-water system, outperforming the performance of state-of-the-art membranes. The proposed macromolecule-incorporated angstrom-sized channels demonstrate considerable potential for broad application in other fields, e.g., single-atom catalysis, sensing, and energy conversion.

Molecularly imprinted membranes (MIMs) epitomize the convergence of membrane science with molecular imprinted technology. Endowed with intrinsic molecular recognition and memory functionalities, MIMs have the potential to evolve into pivotal tools for the selective separation, identification, and purification of biomolecules. This review summarizes the forefront advancements in the selective recognition and separation of macromolecular biomarkers through MIMs, such as proteins, peptides, and nucleotides. A comprehensive overview of diverse imprinting polymerization methodologies employed in crafting MIMs tailored for specific biomacromolecule targets is presented. Additionally, the evolving research landscape concerning MIMs as a potent platform for the targeted capture of biomacromolecule-associated biomarkers is critically appraised and is discussed.

Biomolecular condensates are dynamic, membraneless compartments that emerge through phase separation of specific proteins and nucleic acids, regulating key biochemical processes within cells. Inspired by these natural systems, we recently developed a modular platform for engineering synthetic ribonucleic acid (RNA) condensates using a multistranded branched RNA motif, termed a nanostar. Here, we employ coarse-grained modeling and molecular dynamics (MD) simulations using the oxRNA2 platform to quantify conformational dynamics of 3-, 4-, and 5-arm nanostars. We define flexibility as the standard deviation of interarm angle distributions and geometry as the mean interarm angle. Across valencies at 37 °C and 0.15 M monovalent salts (NaCl), increasing the arm number reduces the mean angle as expected from geometry, while the dispersion of interarm angles remains comparable, indicating similar flexibility. Salt increases the mean angle in 4- and 5-arm nanostars and increases the temperature dependence of interarm angles. In contrast, the 3-arm nanostar is largely unaffected by salt and temperature over the studied ranges. Lastly, at 1.0 M salt and 37 °C, DNA nanostars adopt larger mean angles compared to RNA nanostars across all valencies, suggesting that backbone chemistry shifts preferred geometry more than it broadens fluctuations. These results illuminate how valency, salt, and temperature differentially control geometry versus flexibility, informing the design of synthetic RNA nanostars and thus condensates with predictable material responses.

We leverage supervised learning to realize high-throughput and objective interrogation of organ-on-a-chip quality from microscopy images. More than 600 images of two lung-related cell types in good and bad quality lung-on-a-chip models are assessed. We demonstrate that trained classifiers can predict different cell types and lung-on-a-chip quality with AUC and accuracy of above 95% and 83%, respectively. Coupled with a dimensionality reduction approach, the predictive capacity of certain classifiers can be enhanced, while the computational cost of resource-intensive algorithms can be significantly reduced. This study is anticipated to further encourage the implementation of machine learning for automating organ-on-a-chip development.

Ethylene (C₂H₄) is a vital industrial chemical with a growing market, similarly produced through the steam cracking of hydrocarbons, such as ethane (C₂H₆). However, separating C₂H₄ from a C₂H₆ and C₂H₄ mixture remains challenging due to their similar molecular sizes and boiling points, necessitating highly energy-intensive processes such as cryogenic distillation. This study introduces two ultramicroporous metal-organic frameworks (MOFs), NKMOF-17-Co and NKMOF-17-Cu, synthesized rapidly under ambient conditions as efficient materials for C₂H₆/C₂H₄ separation via physical adsorption. Gas adsorption experiments reveal that NKMOF-17-Co and -Cu exhibit a preference for adsorbing C₂H₆ over C₂H₄. Determined by these single crystal structures of loading gases into NKMOF-17-Co and -Cu, stronger hydrogen bonding and C-H···π interactions between C₂H₆ and MOFs were confirmed clearly. Verified by breakthrough tests of a C₂H₆ and C₂H₄ mixture for NKMOF-17-Co and -Cu, C₂H₄ was obtained in high purity (>99.96%). NKMOF-17-Co and -Cu were regenerated with low energy requirements and showed stable performance under repeated cycling tests. These findings suggest that NKMOF-17-Co and -Cu hold great promise for energy-efficient separation processes in the industrial separation of C₂H₆ and C₂H₄.

This study uses a new multi-rocking cell system for visual, parallel assessment of methane hydrate (MH) dissociation. The method allows for systematic comparison of various fluidizerscomposed of thermodynamic hydrate inhibitors like urea and additives including surfactants, kinetic hydrate inhibitors, and antiagglomerantsunder controlled, water-rich conditions. Our findings show that surfactants, such as SDS, SO, and DDBSA, accelerate dissociation but cause MH migration and persistent foaming. In contrast, DTMAC and saponin effectively promote dissociation while suppressing foam and MH migration. Combination fluidizers (e.g., SDS + PVP, SDS + Tween 80) further fine-tuned the performance, demonstrating additive-specific effects such as foam suppression or accelerated dissociation. This work provides insights for designing effective fluidizers for hydrate management in subsea production environments.

Hydrogen energy is a promising solution to global energy and environmental challenges. Water electrolysis is a green technology for hydrogen production, but its efficiency is limited by the electrocatalytic performance of the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). Elevating the temperature of the electrolysis system is effective in accelerating the electrocatalytic efficiency. However, conventional heating methods often increase the system complexity and energy consumption. The photothermal effect, where a substance generates heat upon light absorption, enables localized heating, improves energy utilization, and offers a promising strategy to enhance electrolyzer performance. Here, recent advances in photothermally enhanced electrocatalytic water splitting for hydrogen production are comprehensively reviewed. We elucidate the mechanisms of electrocatalytic water splitting and photothermal effects, with particular emphasis on the multiscale mechanisms of photoenhanced electrocatalysis that integrate nanoscale localized heating, hot-carrier generation, interfacial restructuring, and system-level modulation to collectively accelerate the HER and OER. We have systematically introduced recent advances in the design of photothermally enhanced electrocatalysts, specifically in the HER, OER, and various anode alternative reactions. This review also introduces representative designs of photothermally enhanced electrolyzers. By conducting a comparative economic analysis of various electrolyzers, this review demonstrates the significant economic benefits of photothermally enhanced water electrolysis for hydrogen production. We also provide a perspective on the future research direction of photothermally enhanced electrocatalytic hydrogen production. This review will inspire future endeavors toward realizing highly efficient, energy-saving, and cost-effective hydrogen production systems.

Lacto-N-neotetraose (LNnT) is one of the important ingredients of human milk oligosaccharides, with various promising health effects for infants. In this study, the biosynthetic pathway of LNnT was constituted in GRAS (generally recognized as safe) host Saccharomyces cerevisiae by coexpressing β1,3-N-acetylglucosaminyltransferase (LgtA), β1,4-galactosyltransferase (LgtB), lactose permease (Lac12), and UDP galactose 4-epimerase (Gal10). The engineered yeast strain designated LN1, coexpressing all these enzymes, achieved an LNnT production level of 1.54 g/L. To further enhance LNnT yield, the flux through the pathway was improved by employing both protein fusion and modular assembly strategies. The assembly of LgtA and LgtB using short peptide tags resulted in a significant increase in LNnT titers, reaching 2.42 g/L. Finally, the LNnT titer reached 6.25 g/L under fed-batch fermentation conditions in a 500 mL flask, representing the highest reported production level of LNnT in S. cerevisiae to date.