Hongyan He

Machine-learned force fields (MLFFs) based on Neuroevolution Potential (NEP) models were developed for the ionic liquid 1-ethyl-3-methylimidazolium acetate ([Emim][Ac]) using first-principles reference data generated with four density functionals: PBE-D3, RPBE-D3, optB88-vdW, and SCAN+rVV10. Using a unified training protocol, the resulting NEP models were systematically benchmarked against structural, thermophysical, transport, and hydrogen-bond-network properties, including radial distribution functions (RDFs), coordination numbers, neutron total scattering profiles, density, viscosity, ionic conductivity, and thermal conductivity. Spearman correlation analysis and weighted composite scoring were further used to establish a property-oriented evaluation framework for MLFF assessment and selection. The results reveal a pronounced dependence of NEP performance on the density functional used to generate the reference data. Although all four models reproduce the dominant local structural features of [Emim][Ac], appreciable differences emerge in medium-range structural ordering and transport properties. The PBE-D3-based NEP provides the most accurate overall description of structural properties, whereas the optB88-vdW-based NEP yields the closest agreement with experimental transport properties, particularly viscosity and ionic conductivity. Analysis of the hydrogen-bond network further reveals systematic relationships among hydrogen-bond populations, residence times, free-ion fractions, and ion transport. These results demonstrate that, for [Emim][Ac], the exchange–correlation functional used for reference-data generation influences the physical fidelity and property-specific performance of the resulting NEP models.

Rechargeable batteries (lithium‐ion, sodium‐ion, zinc‐ion) are core technologies for large‐scale renewable energy storage, featuring high energy density, conversion efficiency, and application flexibility. However, their development remains hindered by structural instability, sluggish ion/electron transport, and uncontrolled interfacial reactions during cycling. The built‐in electric field (BIEF) is an intrinsic physical field that forms spontaneously without external bias, which could regulate the local potential distribution and guide the migration behavior of electrons and ions, showing significant potential for improving reaction kinetics and suppressing structural instability. Through the rational design of heterointerfaces, polarized structures, and composition or defect gradients, BIEFs effectively alleviate volume expansion, homogenize reaction fronts, accelerate carrier transport, and stabilize interfacial evolution. In this review, we provide a systematic overview of BIEF formation mechanisms, characterization methods, theoretical calculations, and functional roles in anodes, cathodes, and electrolytes. Recent progress in characterization techniques and theoretical calculations for clarifying BIEF mechanisms is summarized, along with key engineering strategies and design principles for BIEF construction. We hope that this review could provide useful guidance for the predictive design and practical implementation of BIEF in next‐generation batteries with high energy density and long cycle life.

To bypass the enduring limitations intrinsic to traditional size-sieving separation, alternative materials for CO2/H2 separation need to be developed. Here, we program a permeation inversion in CO2/H2 transport by constructing a nanoconfined ionic liquid membrane through the synergistic engineering of graphene oxide nanosheets and ionic liquids. A substrate-assisted size-selection strategy enables the preferential incorporation of large nanosheets, which form ordered laminates, while short-chain CO2-philic ionic liquids are precisely confined within the interlayer galleries. The resulting membrane exhibits inverted transport behavior, achieving an ultrahigh CO2 permeance of 358.2 GPU and a mixed-gas CO2/H2 selectivity of 201.9 under a 10:90 CO2/H2 feed─surpassing the upper bounds of conventional polymeric and mixed-matrix membranes. Scalability is demonstrated using a five-cell module, in which CO2 flux increases linearly without selectivity loss. Molecular dynamics simulations and spectroscopic studies reveal that the nanoconfined ionic liquid phase creates a continuous, high-solubility pathway for CO2, whereas H2 is largely excluded from the graphene oxide interfaces. This study establishes nanosheet–ionic liquid synergy as a programmable design platform to engineer permeation inversion, offering a scalable route to advanced CO2/H2 separation membranes.

Electrocatalytic nitrate reduction reaction (NO ₃ RR) towards ammonia (NH ₃ ) production is a critical process that sustainably converts hazardous nitrate to value‐added chemicals. Amorphous metal‐based nanomaterials are promising NO ₃ RR catalysts owing to the abundant surface active sites, but their performance is still unsatisfactory for practical needs. We develop a robust one‐pot strategy to prepare a family of amorphous PdMP ( a ‐PdMP, M = Cu, Ni, Co, Fe, Mn, and Zn) nanoparticles with high yield, high structural stability, uniform morphology, and tunable composition. Impressively, the as‐prepared a ‐PdCoP nanoparticles show excellent electrocatalytic activity (onset potential measured at +0.3 V vs. reversed hydrogen electrode), high NH ₃ selectivity, and outstanding long‐term stability (Faradaic efficiency (FE) maintained > ∼98% at 200 mA cm ⁻² for 200 h) in NO ₃ RR. Theoretical calculations demonstrate that alloying in PdM can enhance overall electron exchange/transfer, while phosphization helps to stabilize the metal valence. Particularly, the a ‐PdCoP possesses a moderate d‐band center to avoid intermediate overbinding, meanwhile, it has the lowest work function to accelerate the electron transfer during NO ₃ RR. Our work demonstrates that synergistic amorphization and phosphization of Pd alloy nanocatalysts can greatly promote their activity and durability in NO ₃ RR. The universal synthesis approach provides guidance to prepare amorphous multielement nanomaterials as highly efficient electrocatalysts.

Electrochemical CO ₂ reduction (CO ₂ RR) stands as a transformative pathway for the sustainable synthesis of fuels and chemicals, yet its advancement is hindered by an incomplete mechanistic picture of dynamic catalyst evolution at the electrode‐electrolyte interface. This review emphasizes the critical role of in situ electrochemical atomic force microscopy (EC‐AFM) in addressing this knowledge gap. As a non‐invasive technique capable of nanometer‐ to near‐atomic scale resolution under operando conditions, EC‐AFM provides direct, real‐time visualization of catalyst dynamics. The review begins by outlining the fundamental principles and advantages of EC‐AFM for probing liquid‐solid interfaces and then systematically demonstrates its applications across major catalyst systems. These range from revealing potential‐dependent reconstruction and roughening on single‐crystal and polycrystalline copper electrodes, to tracking the stability and synergistic effects in Cu‐based bimetallic catalysts, and to elucidating structure‐activity dynamics in emerging systems such as gas diffusion electrodes. Finally, we discuss current technical challenges of EC‐AFM and propose future interdisciplinary research directions. This review summarizes how in situ EC‐AFM has informed our understanding of CO ₂ RR by revealing dynamic catalyst reconstruction, tracking active‐site evolution, and elucidating interfacial processes. By systematically compiling these advances, it serves as a critical interdisciplinary resource, positioning EC‐AFM as a practical tool that offers visual guidance for the rational design of next‐generation catalysts and electrochemical systems.

Ionic liquids integrate dissolution and electrocatalysis to overcome lignin recalcitrance, enabling lignin valorization.

Confining ionic liquids within zeolite cages activates Br ⁻ anions for in situ Br ₂ generation, achieving synergistic antimicrobial efficacy with superior thermal stability and reduced dosage. Graphical abstract image partly designed using AI.