AIChE Journal最新文献

Machine learning can effectively accelerate materials development in real-world sorbent applications including clean-up of polluted impoundment sites. Zeolites synthesized from coal fly ash can adsorb contaminants, such as boric acid, from water. Machine learning models were trained on molecular simulation data to predict boric acid uptake based on zeolite structure, aluminum content, extra-framework cation species, and boron concentration in solution. Overall, eXtreme Gradient Boosting models yielded the highest speed and accuracy. The models were used with a genetic algorithm to enable concentration-specific zeolite optimization for coal ash impoundment sites. Results indicate small pore zeolite frameworks such as PHI, CHA, AVE, ERI with low Si/Al ratios and a mix of Na and Ca metal cations are most effective for boric acid removal. Our use of machine learning models with a genetic algorithm has broad implications for machine learning-aided materials design.

Salt-laden waste alkali streams pose significant environmental and economic challenges and demand efficient recovery. As an energy-efficient route for alkali recovery, diffusion dialysis (DD) is limited by the poor hydroxide permeability ($��U_{{\mathrm{OH}}^{-}}�$) of conventional dense cation exchange membranes (CEMs). Herein, porous CEMs were fabricated via non-solvent induced phase separation of a blend of chloromethylated polyethersulfone and sulfonated polyethersulfone, followed by crosslinking with polyethylenimine and post-sulfonation with 1,3-propane sultone. Through optimization of polymer composition, crosslinking degree, and sulfonation level, we engineered membranes with tailored microstructure and transport properties. The optimized membrane demonstrates a record-high $��U_{{\mathrm{OH}}^{-}}�$ of 23.8 × 10⁻³ m/h in NaOH/Na₂WO₄ systems, while maintaining OH⁻/WO₄²⁻ selectivity ($��S_{�{\mathrm{OH}}^{-}�/�{\mathrm{WO}}_4^{�2�-�}�}�$) of 21.2; the permeability exceeds commercial benchmarks and reported values. This breakthrough establishes a practical and scalable approach for sustainable alkali recovery from high-salinity industrial waste streams.

Rational design of electrochemical energy conversion and storage devices hinges on a fundamental understanding of the electrical double layer (EDL) at the electrode–electrolyte interface. The classical Gouy–Chapman–Stern model of EDL predicts a unity Parsons–Zobel (PZ) slope, which is derived from the relation between inverse measured capacitance and inverse Gouy–Chapman capacitance. However, recent experiments on metals such as Au and Pt have revealed ultrahigh capacitances and ultralow PZ slopes, markedly deviating from the classical model. In this study, we employ density-potential functional theoretic models to systematically compare three mechanisms: surface roughness, nonspecific ion attraction, and ion chemisorption with partial charge transfer. Our analysis reveals that only ion chemisorption fully accounts for the ultrahigh double layer capacitance and ultralow PZ slopes. The gleaned insights into the EDL on transition metals are informative for understanding local reaction environment in electrochemical energy conversion and storage devices.

Here, we introduce a calibration-less bonded-sphere model to describe three-dimensional, linear elastic, highly deformable particles. Voronoi tessellation is used to partition a particle into multiple sub-spheres, generating a virtual bond network that mimics the mechanical properties of the original particle. Inter-particle collisions are resolved by considering contacts between the contacting sub-spheres. The model is validated through six test cases: (i) bending of a beam, (ii) stretching of a rod, (iii) contact of a deformable sphere with a flat wall, (iv) collision between two deformable spheres, (v) motion of a deformable sphere along an inclined plane, and (vi) packing of deformable spheres. The results confirm that the desired mechanical properties of the deformable particle (i.e., Young's modulus and coefficient of friction) are obtained when assigning the desired values to the virtual bonds and the sub-spheres comprising the bond network, thereby omitting a tedious calibration process typically required by conventional bonded-sphere models.

The effect of surfactants on gas-liquid systems in an internal-loop airlift reactor was investigated using a CFD-PBM coupled model. This model integrates three mechanisms: the inhibition of bubble coalescence, the increase in drag force, both induced by the Marangoni effect, and the enhancement of bubble breakup due to decreased surface tension. This comprehensive approach established a multiscale simulation framework for gas-liquid systems containing surfactants. Using an ethanol-water system as an example (x_ethanol = 0–0.05, corresponding to 0–14 vol%), the simulation results for gas holdup are in good agreement with experimental data from our previous work, showing a rapid increase followed by a gradual decrease in gas holdup as ethanol concentration increases. Further numerical simulations were performed to evaluate the relative contributions of the three surfactant-induced mechanisms to gas holdup. The results revealed that the inhibition of bubble coalescence is the dominant mechanism by which surfactants affect the hydrodynamics.

Optimizing convective heat transfer in packed beds is critical for energy-efficient process design, yet the interplay between particle configuration and pore-scale transport remains poorly understood. We propose an experimentally validated dual network model (DNM) to explore a wide range of packing configurations and flow conditions to guide packing optimization. Using stratified beds for demonstration, we show that placing a small-particle layer adjacent to the heating surface enhances heat transfer, with an optimal layer thickness (~7.1% of bed width representing a single-layer), beyond which a rising pressure drop reduces overall efficiency. Cross-flow analysis attributes these improvements to increased conduction, suppressed channeling, and intensified local mixing. Increasing particle size differences further boosts heat transfer but increases hydraulic resistance, shifting the optimal thickness toward smaller values. This experimental–modeling integrated framework provides pore-scale insights into the trade-off between heat transfer and pressure drop, offering practical guidelines for scalable thermal management and process intensification.

Electrocatalytic oxidation of 5-hydroxymethylfurfural (HMFOR) offers an energy-efficient route to high value-added chemicals. There is an urgent demand for high-performance non-precious metal catalysts. Herein, the oxygen-deficient FeO/NiO/Ni was synthesized, which has high performance for HMFOR with a conversion rate of 100%, a selectivity of 98.6% for 2,5-furandicarboxylic acid (FDCA), and a Faradaic efficiency of 94.1% at 1.5 V (vs. RHE), superior to most reported non-precious metal catalysts. The efficient catalytic activity of FeO/NiO/Ni is attributed to the high specific surface area and enhanced adsorption and activation of HMF by oxygen vacancies, thereby reducing the rate-limiting step energy barrier of 5-hydroxymethyl-2-furancarboxylic acid to 5-formyl-furancarboxylic acid (*HMFCA→*FFCA). Ni³⁺ acts as the active site for direct HMF oxidation catalysis, while Fe doping induces oxygen vacancies that facilitate the formation and stabilization of Ni³⁺ species, enhancing their formation and catalytic functionality.

The practical application of aqueous zinc (Zn) batteries is largely limited by the detrimental hydrogen evolution reaction (HER) on the Zn metal surface. Herein, an effective ion/solvent separation strategy is proposed to construct a uniform and seamless MOF membrane on a 3D Zn foam surface, by which the selective Zn²⁺ transport and accurate H₂O molecules separation from the MOF channels can be realized, thereby obtaining a HER-free Zn anode. Specifically, by screening 10 Zn-MOF candidates through theoretical calculations, a pore-size-customized ZIF-8 was selected for the accurate Zn²⁺/H₂O separation because of its superior adsorption characteristics and nominal aperture pore size. Then, a series of in situ characterizations and experiments demonstrate that negligible amounts of H₂ gas are observed from the MOF@Zn foam anode. Consequently, the ion/solvent separation effect is verified by HER-free MOF@Zn foam-based coin full cells delivering 78.1% capacity after 500 cycles and large-scale pouch cells with expected performance.

The automation and control of protonic membrane reformers can facilitate the commercialization of this emerging hydrogen-producing technology. To this end, a decentralized and offset-free model predictive control (MPC) approach is developed to explore potential automation pathways for a protonic membrane reforming system that achieves 84% hydrogen recovery at 0.69 A$��·�$cm$��{\hspace{0.17em}}^{�-�2�}�$ (15.2 cm$��{\hspace{0.17em}}^{�2�}�$ active surface area). Three physics-based and data-driven models estimate thermal hydrogen generation on the anode, electrochemical hydrogen recovery on the cathode, and steam generation. During setpoint tracking, the experimental results of the MPC architecture show faster hydrogen purification rate settling times and implementable control action profiles between sampling times, protecting the actuators and the thermo-electrochemical performance of the protonic membrane. Ultimately, this work points to control strategies for protonic membrane systems that incorporate a combination of classical and predictive controllers with disturbance-observer-based error quantification for optimal hydrogen production.