AIChE Journal最新文献

Metal–organic framework (MOF) membranes with tunable pore structure and chemical functionality are promising for molecular separation. Nevertheless, it remains a grand challenge to achieve precise molecular sieving and high flux simultaneously for liquid separation, especially for water desalination. Herein, for the first time, we report the design and engineering of hydrophobic Zr-MOF, UiO-66 membranes for membrane distillation. Thermal activation was proposed to remove the hydroxyl groups in the MOF framework to create a hydrophobic membrane surface, which ensures the membrane's anti-wetting ability. Meanwhile, ligand concentration was regulated to introduce lattice defects to deliberately enlarge pore size, which facilitates the fast transport of water vapor. The relation between the hydroxyl groups and water transport was revealed by density functional theory calculations. The resulting hydrophobic MOF membrane showed excellent water desalination performance with a water flux of ~12.8 L·m⁻²·h⁻¹ and NaCl rejection of over 99.9%. Our work extends the potential of MOF membrane for water desalination and also provides a facile approach to tuning the pore properties of molecular sieving membranes.

Carbon molecular sieve (CMS) membranes hold significant potential for advanced gas separation and purification, yet further improvements in molecular sieving properties remain a critical challenge. In this study, carboxylated-PIM-1 (PIM-COOH) polymers were synthesized by hydrolyzing PIM-1 polymers, serving as efficient precursors for CMS membrane fabrication. The pyrolysis process was optimized, with the temperature tuned to 800°C for 2 h, resulting in high-performance CMS membranes. These membranes demonstrated exceptional gas separation capabilities, achieving H₂ and O₂ permeabilities of 831.8 and 79.7 Barrer, respectively, with H₂/CH₄ and O₂/N₂ selectivities of 453.6 and 12.8, surpassing the latest 2015 upper bound for H₂/CH₄ and O₂/N₂ separation. Mixed gas tests further validated the single-gas results, revealing H₂/CH₄ and O₂/N₂ selectivities as high as 1402.1 and 16.8, respectively. This innovative strategy, leveraging PIM-COOH-derived CMS membranes, provides a promising pathway for next-generation hydrogen purification and air separation technologies.

Efficient conversion of CO₂ and CO is pivotal for CO₂ hydrogenation to methanol given the synthesis loop recycle generally employed in practice. However, competitive adsorption of CO and CO₂ at active sites reduces methanol production. In this study, multi-functional active sites were constructed over Ga₂O₃-modified Cu/ZnO/Al₂O₃ (CuZnAlGa) for synergistic hydrogenation of CO₂ and CO to methanol. Incorporating the Ga₂O₃ promoter effectively dispersed Cu particles, formulated extra Cu-Ga sites active for CO₂ adsorption and Ga sites for H₂ dissociation (Ga-H), enhancing H₂ and CO₂ activation as well as promoting non-competitive CO₂/CO adsorption. Under 240°C, 5 MPa, and 6000 mL g_cat⁻¹ h⁻¹, the CuZnAlGa catalyst achieved a methanol space–time yield of 517 g kg_cat⁻¹ h⁻¹ with excellent stability, outperforming the commercial methanol synthesis catalysts. The formate pathway, involving formate and methoxy as critical intermediates, is the predominant route for methanol formation. The Ga₂O₃ promoter accelerates formate formation and further hydrogenation to methoxy.

The chlorine evolution reaction (CER) is essential to the chlor-alkali industry and water treatment, while most studies focus on high-chloride acidic systems, with limited research on its performance and mechanisms in neutral tap water (low Cl⁻ with Ca²⁺/Mg²⁺). This study developed a Ru single-atom supported on NiSbSnO₂ (Ru/NATO) electrocatalyst and systematically evaluated the CER performance under both acidic and simulated tap water conditions. The Ru/NATO-1 exhibited a Faradaic efficiency of 97.3% for CER under acidic conditions, a 1.9-fold improvement compared with NATO. The Ru/NATO-1 also maintained high activity, with chlorine selectivity increasing significantly from 56% to 90.1% in simulated neutral tap water. Combined experimental and theoretical analyses demonstrated that the d–p orbital hybridization between Ru and Sn atoms in Ru/NATO promoted interfacial electron transfer while modulating the adsorption sites of chlorine-containing intermediates. These results highlight an effective approach for electrochemical chlorine generation in tap water.

Machine learning (ML) was used to measure the solubility of CO₂ in each new ILs due to the numerous the combination of forms of anions and cations. The ML model with 8869 data points and four algorithms (Transformer, DNN, RF, SVM), which were contained at different temperatures and different pressures, was employed to establish the relationship between structure and properties, encoding anions and cations based on a simplified molecular linear input specification. Moreover, a decoding method, which was referenced first for predicting CO₂ solubility in ILs with ML, was used to improve the data processing results. In this paper, four error indicators (r, R², RMSE, MAE) were used, and the Transformer model had the most accurate predictions with them of 0.993, 0.986, and 0.0021, respectively. Ultimately, SHapley Additive exPlanations analyses was used to understand the black box operation.

The well-established concept of dividing wall columns can be advanced into multiple dividing wall columns. These columns can theoretically achieve any number of pure fractions and offer major potential investment and energy savings compared to conventional sequences. At Ulm University, the first pilot-scale multiple dividing wall column was designed, constructed, and commissioned. This paper describes a step-by-step procedure that was applied to find a suitable operating point for the pilot plant for a given separation task, that facilitates products with preferably high purities. The procedure combines separation efficiency measurements for the used packing, followed by a simulative evaluation of the energy demand of the separation and screening simulations, where both liquid split ratios are varied independently to define an operating window for the process. The determined operating point is experimentally validated, offering product purities exceeding 95 wt%. A high level of agreement was observed between simulation results and experimental data.

Deep eutectic solvents (DESs) are promising green solvents in lignocellulose pretreatment, as they combine low toxicity, biodegradability, and efficient ability to remove hemicellulose and lignin. However, under- or over-removal of hemicellulose and lignin can hinder cellulose exposure or produce inhibitors (e.g., pseudo-lignin or condensed lignin), reducing enzymatic hydrolysis efficiency. Here, a mass-transfer kinetic model incorporating the pH and viscosity characteristics of DESs was developed to precisely regulate hemicellulose and lignin dissolution. The model revealed that the activation energy required for the solubilization of hemicellulose and lignin into DESs is 16.41 and 21.40 kJ/mol, respectively, which is significantly higher than that required for diffusion into the boundary layer (4.58 and 4.61 kJ/mol), indicating that solubilization is the rate-determining step. Theoretical calculations revealed that, compared with lignin, hemicellulose exhibits greater advantages in terms of interaction energy, diffusion coefficient, and number of hydrogen bonds with DESs, making it inherently more soluble and diffusible.

Understanding the motion of porous particles is essential for regulating multiphase flows and particle-fluid interactions. To investigate path instability during settling, experiments using particle tracking velocimetry (PTV) and particle image velocimetry (PIV) were conducted. Based on two-dimensional and three-dimensional trajectory characteristics, four typical settling modes were identified: Oblique, S-shaped, Zigzag, and Spiral. Under random release conditions of a single particle, the occurrence probabilities of these modes were approximately 13%, 51%, 18%, and 18%, respectively. Path instability decreased with increasing permeability, and at higher permeability, particles exhibited more stable settling trajectories. Porous particles showed higher terminal velocities than impermeable ones under the same driving force. Particle path instability correlates with wake asymmetry and is mainly controlled by permeability rather than Reynolds number, with higher permeability stabilizing the settling trajectory. These findings provide new insights into the motion behavior of porous particles such as catalysts and offer guidance for optimizing particle-fluid interactions and enhancing mass and heat transfer efficiency in multiphase systems.

The characterization of flow regime transitions in gas–liquid stirred tanks is important for process optimization. Conventional studies, however, have primarily characterized steady-state flow regimes, overlooking the evolution of gas-phase polydispersity that significantly influences the transition dynamics. This work addresses this gap by introducing a deep-learning-based polydisperse particle detection system for efficient size-resolved bubble characterization. The results reveal that regime transitions are mechanistically controlled by a hydrodynamic decoupling between bubble classes, with large-bubble dynamics playing a key role. The flooding-to-loading transition is characterized by the radial dispersion of large bubbles, marked by a sharp surge in their local occurrence frequency, with its growth rate increasing more than fivefold. The transition to full recirculation coincides with axial entrainment of large bubbles below the impeller, causing a more than threefold increase in local gas holdup. This study establishes a mechanistically grounded framework linking microscopic bubble phenomena to macroscopic flow pattern transitions.