The push for electrification in chemical engineering is accelerating the development of efficient technologies for external field intensification, such as microwave. These technologies aim to maximize the utilization of matter and energy. However, the emergence of fluid structure at nano-/microscopic levels, combined with the complex interactions between interfacial effects and microwave, poses significant challenges to existing theoretical frameworks. Traditional thermodynamic models, which rely on macroscopic experimental data within a phenomenological approach, may not accurately capture the precise variations in fluid structures at interfaces with microwave applied. In this perspective, we begin with quantum mechanics and propose the concept of equivalent potential, providing a fundamental principle to unify the impacts of interface and microwave. Meanwhile, the importance of fluid structure regulation within the framework of equivalent potential has been discussed, promoting deeper exploration of both thermal and non-thermal microwave effects. Looking ahead, the ongoing development and application of novel theoretical methods that decouple interfacial effects from external field effects, alongside advancements in in situ spectral characterization technologies, are expected to establish a paradigm based on the microscopic fluid structure regulation that better facilitates the utilization of microwaves in modern chemical engineering.

Structured catalysts hold considerable promise for catalytic distillation due to their enhanced mass transfer, robust mechanical/thermal stability, and facile recyclability. However, conventional synthesis methods suffer from uncontrolled bulk nucleation in the liquid phase, leading to low loading efficiency and limiting practical use. Herein, this study developed a microwave-assisted hydrothermal method for the in situ growth of NaA zeolite coatings on silicon carbide (SiC) foams. The strong microwave absorption of SiC induces localized overheating, which promotes directed crystal growth on the SiC surface while minimizing solution-phase crystallization. A silica sol pretreatment method was employed to address support dissolution and facilitate the rapid construction of a dense zeolite layer, achieving a mass variation of 1.11 after only 5 cycles, which was not attainable with other pretreatment methods under identical conditions. The resulting coating exhibited excellent adhesion, with a minimal mass loss of 0.62% under rigorous ultrasonic and solvent-flushing tests. In aldehyde-ketone condensation reactions, the structured catalyst maintained a high yield (> 90%) over three cycles. The reusability of the NaA@SiC structured catalysts, combined with uniform crystalline coatings, offers a promising approach to decrease raw materials consumption in future manufacture and applications of structured catalysts.

Solid-state hydrogen storage is widely recognized as a promising pathway for safe, high-density, and reversible hydrogen utilization, yet its advancement remains hampered by complex thermodynamic, kinetic, and structural constraints. This review highlights the emerging role of big data and machine learning in reshaping the research landscape. Through analyses enabled by the Digital Hydrogen-S platform, recent material development trends and persistent bottlenecks are systematically identified, revealing widespread misalignments with the US Department of Energy targets in storage capacity, operating temperature, and pressure. Data-driven approaches are shown to accelerate property prediction, high-throughput screening, and inverse design, while the integration with high-throughput computation and experimental validation is forming an intelligent closed-loop paradigm. Meanwhile, neural network potentials offer near-first-principles accuracy for probing hydrogen adsorption, dissociation, and diffusion, though challenges in long-range interactions and transferability remain. Looking ahead, establishing open-access multimodal databases (combining numbers, text, spectra, and images), developing multimodal large language models, implementing inverse design strategies, and constructing generalized neural network potentials capable of describing complete absorption-desorption cycles represent critical steps toward intelligent and practical material discovery. This review provides a structured framework to guide future research and accelerate the deployment of solid-state hydrogen storage technologies.

As a cost-effective oil-water separation technology, fiber coalescers rely on a thorough understanding of the droplet coalescence mechanism. However, current research has primarily focused on the single process of sessile-sessile droplet coalescence. Using high-speed imaging and the mask region-based convolutional neural network, this study provided the first quantitative characterization of the complete dynamics of asymmetric pendant-sessile droplet coalescence, a phenomenon more prevalent in industrial settings. It was discovered that this process comprises three stages. In the liquid bridge formation stage (Stage I), the lateral expansion of the liquid bridge was dominated by the capillary pressure difference, and the influence of sessile-to-pendant droplet radius ratios on this process was negligible. The oscillation decay stage (Stage II) exhibited the uniqueness of the asymmetric system, where fiber adhesion accelerated energy dissipation, leading to rapid oscillation decay, while the amplitude of the capillary wave on the pendant droplet side was significantly enhanced with an increasing the size ratio. Ultimately, in the stable morphology formation stage (Stage III), increasing the size ratio to 1.5 could significantly reduce the size of the secondary droplets. These findings provided direct strategies for reducing polydisperse secondary droplets in industrial coalescers and enhancing separation efficiency.