Traditional catalyst design and process optimization often operate in isolation, leading to local optima that neglect system-level energy efficiency and economic viability. This fragmentation is particularly pronounced in complex catalytic systems like dimethyl oxalate (DMO) hydrogenation to methyl glycolate, where thermodynamic constraints and multi-scale interactions limit performance. To bridge this critical gap, this study pioneers a Bilateral Machine Learning framework for Synergistic Multi-objective Optimization of catalysts and processes (BiML-SyMO). This closed-loop framework seamlessly integrates experimental catalyst data with mechanism-driven process simulations to enable the co-optimization of catalytic performance and overall process sustain-ability. The framework innovatively employs differential evolution-driven hyperparameter optimization and uncertainty-sampling active learning to establish XGBoost as the superior catalyst prediction model (R2 > 0.88). SHAP-based feature importance and partial dependency plots analyses were adopted to identify the features that have a significant impact on the output results and their interaction mechanisms. The results indicate that the properties of the promoter and the reaction conditions are the two most crucial types of input features. Leveraging this insight, the non-dominated sorting genetic algorithm II-based multi-objective optimization identifies Ag-Al/CNT as the optimal catalyst, achieving 88.10 % yield at H2/DMO = 274.87. Crucially, bilateral feedback to process-scale modeling reveals that yield-maximized scenarios incur prohibitive energy-economic penalties (30.28 MW/t exergy destruction, 2186 $/t product cost). System-level co-optimization via BiML-SyMO shifts the Pareto optimum to H2/DMO = 99.83, slashing unit exergy destruction and product cost to 14.01 MW/t and 1142 $/t. This work establishes a transformative paradigm for sustainable chemical manufacturing, where ML-guided catalyst-process synergy replaces compartmentalized design.

Sodium-ion batteries (SIBs) are promising for large-scale energy storage, but their development is hindered by the lack of high-rate, long-life cathode materials. Na4Fe3(PO4)2P2O7 (NFPP) offers structural stability and cost advantages but suffers from low electronic conductivity and sluggish Na+ diffusion. Herein, a tungsten-doped NFPP (NFPP-W x ) is achieved via a scalable sol-gel method in which W substitution at Fe sites suppresses grain growth, increases surface area, and improves electrode-electrolyte contact. Combined density functional theory and systematic experimental analyses reveal that W-doping narrows the band gap from 2.639 to 0.696 eV, enhances electronic conductivity, and boosts the pseudocapacitive contribution. NFPP-W0.03 delivers 105.6 mAh g-1 at 0.1C and demonstrates exceptional cycling stability, retaining 93.7% after 970 cycles at 2C, 89.3% after 4000 cycles at 50C, and 73.8% after 6000 cycles. A full cell paired with hard carbon exhibits 97.8 mAh g-1 at 0.1C and long-term stability. This work demonstrates W-doping as an effective strategy to engineer NFPP for high-rate, durable SIB cathodes.

The development of high-activity Ni-based catalysts for efficient CO2 methanation remains challenging, primarily due to Ni particle aggregation under high loading conditions, which limits their long-term stability at low temperature. To address this issue, this work evaluates the effect of doping with typical metals M (i.e., Co, La, Fe, and Ce) on the properties of Ni-Mg-Al layered double oxides (LDOs). The Ni-Co LDO (Ni/Co = 18:2, wt%), with the lowest activation energy (i.e., 54.5 kJ/mol), achieved 80% CO2 conversion and 99.9% CH4 selectivity at 350 degrees C, while Ni-La LDO only showed 73% CO2 conversion. The catalytic performance of optimized Ni-Co LDO is also quite stable under a 140 h time-on-stream thermal shock evaluation. Comprehensive characterization reveals that Co doping is particularly effective in generating abundant oxygen vacancies, creating numerous interfacial basic sites and stabilizing small Ni particles. In situ DRIFTS analysis further elucidates the resulting differences in reaction intermediates, pathways, and coordination environments, linking the enhanced structural properties to superior catalytic performance.

Green ammonia offers a viable hydrogen carrier due to its high energy density, established infrastructure, and cost-effectiveness. However, the decomposition process for hydrogen production relies on highly efficient catalysts. This study aims to overcome experimental limitations in catalyst design by developing a Bayesian optimization-based interpretable ensemble learning framework for multi-objective optimization of ammonia decomposition catalysts. A dataset covering the intrinsic properties of catalysts, preparation parameters, and reaction conditions for ammonia decomposition is constructed to develop six various ensemble learning models. The hyperparameters of these models are automatically and optimally configured using the Bayesian optimization-based Optuna method. The results demonstrate the effectiveness of this hyperparameter automatic optimization strategy, as the average R2 across the six models improved by 0.086 compared to the pre-optimization values. In particular, the CatBoost model emerges as the optimal model with test R2 > 0.9188. The Shapley additive explanations reveal the regulatory mechanism of performance by reaction conditions (with a contribution of 48.14%, where temperature and space velocity are dominant) and intrinsic characteristics of the catalyst (33.79%). The partial dependence plots analyses are further conducted to expound the effect of these key parameters on the catalytic performance. The optimal model is integrated with the reference vector guided evolutionary algorithm to minimize the total metal content while maximizing the space-time yield of hydrogen in ammonia decomposition catalysts. Three newly optimized catalysts demonstrate superior performance, offering an improved efficiency-cost balance compared to existing catalysts. External validation with unseen data further confirms the model’s robustness, with prediction errors <5.06%.

A key challenge in metal-air batteries (MAB) is the slow kinetics of both the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). Therefore, it is necessary to develop catalysts that can optimize their kinetic performance. Herein, a bifunctional catalytic material, FePc-CoFe2O4/CNT, was fabricated through a scalable and facile method due to the π-π stacking effect. The introduction of FePc promotes electron transfer from the Fe sites in FePc and the Co sites in CoFe2O4 to the lattice oxygen in CoFe2O4. This process not only overcomes the poor intrinsic conductivity of FePc and enhances the catalytic activity of the Fe-N4 sites but also increases the number of Co3+ active sites. Systematic investigations demonstrate that the synergistic interaction between FePc and CoFe2O4/CNT markedly accelerates the reaction kinetics, resulting in a reduced potential gap between the OER potential at 10 mA cm-2 and the ORR half-wave potential (ΔE = 0.82 V). When used as an air electrode in aqueous zinc-air batteries (ZAB), the catalyst achieves a power density of 199.5 mW cm-2 and stable cycling for over 1000 h at a current density of 2 mA cm-2, outperforming the benchmark Pt/C + RuO2. Remarkably, the catalyst also exhibits excellent performance in lithium-air batteries (LAB), maintaining a capacity retention rate of 99.8% after 225 cycles. This study provides valuable insights into the design of catalysts for MAB.

To address the multi-objective collaborative optimization challenges in thermodynamics, economics, and environment during the production of green methanol, this study proposes a quaternity intelligent architecture integrating kinetic-based mechanism modeling, ensemble learning, interpretability analysis, and evolutionary algorithms. An entire process model for carbon dioxide to methanol (CTM) is developed and coupled with a Python interface to the Component Object Model (COM), enabling automated parameter control and data acquisition. A high-fidelity dataset containing 1,715 samples is generated to develop various ensemble learningdriven surrogate models of the CTM process. The Optuna framework is proposed to automatically optimize the hyperparameters of these ensemble learning models. Results indicated that the optimized CatBoost (CAB) model has the best predictive performance with an average R2 value of 0.97 and RMSE of 0.12. Two complementary interpretability methods are employed to investigate the competitive ranking and synergy mechanism of key parameters on the exergy efficiency, total annual cost, and CO2 emissions of the CTM system. It was found that the recycle ratio, reaction temperature and hydrogen flowrate are the most crucial factors affecting the performance of the CTM system. Finally, the preferred CAB model is integrated with various genetic algorithms to conduct multi-objective optimization on the CTM system, aiming to maximize exergy efficiency while minimizing production cost and CO2 emissions. The optimal scheme increases exergy efficiency by 10.47 %, decrease total annual cost by 37.0 %, and reduces net CO2 emissions by 0.47 t/t. This study provides an innovative paradigm of data-driven and mechanism-constrained collaboration for the intelligent optimization of complex chemical processes.

ABSTRACT Na 4 Fe 3 (PO 4 ) 2 P 2 O 7 (NFPP), with its high specific capacity and excellent structural stability, is a promising cathode material for sodium‐ion batteries. This study reveals the structural regulation mechanism of cobalt‐doped Na 4 Fe 3 (PO 4 ) 2 P 2 O 7 cathode materials and demonstrates their optimization effects on the high‐voltage performance of sodium‐ion batteries. By combining multi‐scale characterization with electrochemical testing, we reveal that Co substitution for Fe can induce lattice expansion, stabilize the (210) crystal plane, and enhance Na + diffusion. Na 4 Fe 2.7 Co 0.3 (PO 4 ) 2 P 2 O 7 (NFCPP‐3) exhibits remarkable rate performance (82.3 mAh g −1 at 100C) and long‐term cycling stability (85.6% after 5000 cycles at 10C). DFT calculations show cobalt doping significantly enhances the material's adaptability under high voltage conditions by reducing the formation energy of the (210) crystal plane (E f : 0.158→0.151 eV) and Na + migration energy barriers (0.807 → 0.623 eV). Furthermore, full cell testing (NFCPP‐3//HC) confirms practical application potential, with a capacity retention of 82 mAh g −1 at 10C and 94.1% after 450 cycles at 1C. This study provides a new perspective for developing cathode materials for high‐voltage and high‐rate sodium‐ion batteries.

Thermosetting polymers exhibit outstanding mechanical properties, thermal stability, and chemical resistance due to their permanently cross-linked network structures. However, the irreversible nature of covalent cross-linking renders these materials non-reprocessable and non-recyclable, posing significant environmental challenges. Although healable polymers based on dynamic covalent bonds and supramolecular interactions have emerged as promising alternatives, a broadly applicable strategy utilizing metal-ligand coordination in thermoset systems remains underexplored. In this work, we present a robust and healable thermoset system fabricated via ring-opening metathesis polymerization (ROMP) of commercially available chelating norbornene comonomers. Cross-linking is accomplished through O-donor coordination to Lewis acidic metal centers, yielding polydicyclopentadiene (PDCPD)-based networks that demonstrate high mechanical strength (up to 60.8 MPa) and effective self-healing performance. This methodology offers a simple and scalable approach to developing high-performance, sustainable thermosetting materials.

The CO2 to methanol (CTM) process represents a pivotal strategy for achieving carbon neutrality and advancing sustainable chemical production. However, the thermodynamic stability of CO2 and inefficiencies in traditional catalyst design hinder large-scale implementation. This study develops an Interpretable Stacking Ensemble-learning framework with Particle Swarm Optimization (ISE-PSO) to accelerate the discovery of high-performance and eco-friendly CTM catalysts. The ISE-PSO framework achieves synergistic optimization of base-model combinations while automatically fine-tuning hyperparameters through ISE-PSO, achieving superior prediction accuracy (R2 = 0.9483) over standalone models. The prediction mechanism of the preferred stacking model is decoded through Shapley additive explanations and partial dependence analyses. Reaction temperature, space velocity, and pressure emerge as dominant features influencing CO₂ conversion ratio and methanol selectivity among the 20 key inputs. By integrating the stacked model with the ISE-PSO algorithm, the optimal catalysts in three different temperature ranges are successfully predicted and have greater industrial potential compared to those reported in existing literature. Specifically, after conducting a full-process modeling and simulation of the CTM process based on the optimal catalyst, results indicate that its exergy efficiency is increased by 3.08 %, total annual cost decreased by 4.23 %, and total CO2 emissions reduced by 29.26 % compared to the benchmark case. This proposed data-driven approach significantly reduces experimental overhead, paving a sustainable pathway for industrial-scale CO₂ valorization and fossil fuel displacement.

Monovalent cation exchange membranes (MCEMs) are central to selectrodialysis (SED), which enables precise ion fractionation. In this study, a novel type of MCEMs were designed for the separation of mixed acid/salt systems via SED. The membranes were fabricated by first doping Ti3C2TX nanosheets into a sulfonated polysulfone (SPPSU) matrix, followed by spray-coating quaternized poly(2,6-dimethyl-1,4-phenylene oxide) (QPPO) layers synthesized from brominated polyphenylene oxide and tertiary amines with varying alkyl chain lengths. The incorporation of 0.5 wt % Ti3C2TX into SPPSU (yielding the SPPSU-T membrane) enhanced the ion exchange capacity by 1.10-fold (to 1.53 mmol & centerdot;g-1) and water uptake by 1.01-fold (to 16.10%) relative to the pristine SPPSU membrane, resulting in a reduced area resistance (2.08 vs 2.14 Omega & centerdot;cm-2) and a 1.30-fold increase in H+ flux (7.31 & times; 10-8 mol & centerdot;cm-2 & centerdot;s-1). Although the optimized SPPSU-T-QPPO18-1.02 (coated with 1.02 g & centerdot;m-2 of QPPO quaternized with N,N-dimethyldodecylamine) exhibited a 3.11-fold increase in area resistance compared to the pristine SPPSU-T membrane, it had a 12.32-fold higher H+/Mg2+ selectivity (P Mg H = 87.59). Moreover, the SPPSU-T-QPPO18-1.02 also achieved 14.23-fold and 5.13-fold higher selectivity for H+ over Zn2+ (P Zn H = 29.32) and Fe2+ (P Fe H = 9.149), respectively, compared to SPPSU (P Zn H = 2.06, P Fe H = 1.78). In the three investigated acid/salt systems, the SPPSU-T-QPPO18-1.02 delivered competitive H+ flux (6.97 similar to 8.78 & times; 10-8 mol & centerdot;cm-2 & centerdot;s-1) and selectivity (9.14 similar to 87.59) compared to the commercial CIMS membrane (flux: 6.00 similar to 8.68 & times; 10-8 mol & centerdot;cm-2 & centerdot;s-1; selectivity: 1.98 similar to 4.11). Furthermore, it demonstrated excellent long-term operational stability in SED processing of MgCl2/HCl mixtures. This work demonstrates that incorporating Ti3C2TX nanosheets into the membrane matrix and coating with a tailored QPPO layer can synergistically overcome the trade-off between ion flux and selectivity in MCEMs, providing an effective strategy for advanced membrane design.

Developing highly efficient and stable electrocatalysts for the oxygen reduction and oxygen evolution reactions (ORR/OER) is crucial for advancing metal-air batteries. While cobalt based nitrogen doped carbon (Co@NC) materials show promise as bifunctional catalysts, their performance is restricted by the limited tunability of nitrogen coordination alone and an insufficient number of active sites. While coordination environment engineering and interface engineering are common optimization approaches, a single strategy often fails to fine-tune the electronic structure of Co active centers to the optimal state, and thus cannot fully satisfy the requirements of reversible oxygen electrocatalysis in metal-air batteries. To break through this bottleneck, we employ a molecular preassembly strategy that simultaneously achieves uniform heteroatom doping and the construction of tightly coupled Co2P/Co and CeO2/Co dual heterointerfaces from a pre-designed molecular precursor. This design overcomes the limitation of single-strategy electronic modulation, enabling more comprehensive and precise optimization of the electronic structure of the Co active centers in Co@NC materials, coupled with stepwise regulation of oxygen intermediate adsorption. As a result, the as-prepared CeO2/Co2P/Co@PSNC catalyst exhibits excellent bifunctional activity in both aqueous zinc-air batteries and organic lithium‑oxygen batteries.

During processing and in service, polymers are prone to thermal oxidative degradation, necessitating stabilization with phenolic antioxidants, which are typically incorporated at low loadings (<1 wt%). However, conventional antioxidants are generally introduced into polymers through simple physical blending. This approach limits their long-term effectiveness, as it fails to maintain a sufficient concentration of antioxidants within the polymer matrix over extended periods. To overcome this drawback, this work developed a novel strategy to enhance oxidative stability by covalently anchoring the antioxidant groups on the polymer backbone. A series of novel dicyclopentadiene (DCPD) comonomers, functionalized with antioxidant groups, were synthesized and subsequently copolymerized with DCPD comonomer via ring-opening metathesis polymerization. The resulting thermoset polydicyclopentadiene (PDCPD) materials exhibit outstanding thermal oxidation stability, high compatibility, and long-term durability.

Developing efficient bifunctional cathode catalysts is essential to address the sluggish kinetics of both the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in Li-O2 batteries (LOBs). Notably, optimizing the d-band center (εd) of catalysts represents a highly effective strategy for enhancing electrocatalytic performance. This study successfully designed a ZnFe-NC/CoS2 composite catalyst featuring a heterointerface. The interface coupling of CoS2 not only alleviates the burden of OER process, but also precisely modulates the d-band electronic structure of Fe-NC sites through synergistic interaction with Zn, further enhancing ORR activity and ultimately leading to a significant overall enhancement in bifunctional performance. Density functional theory (DFT) calculations reveal that Zn and CoS2 exert opposing modulation effects on the εd of the Fe-NC sites, with Zn induces a downward shift, whereas CoS2 induces an upward shift. Their synergistic interaction optimizes the εd to an ideal intermediate position, balancing the adsorption-desorption behavior of LiO2 intermediate, thereby lowering the energy barrier for Li2O2 formation and decomposition and effectively reducing the overpotential. As anticipated, the ZnFe-NC/CoS2-based LOB delivers a low overpotential of 1.04 (±0.01) V, a high specific capacity of 25,650 (±180) mAh g-1, and an extended cycling stability up to 325 cycles. This work presents an effective strategy for designing high-performance bifunctional cathode catalysts for LOBs through synergistic modulation of the εd.

Designing NASICON-type cathodes with simultaneously enhanced electronic conductivity, ion transport kinetics, and redox activity remains a critical challenge for high-performance sodium-ion batteries. Herein, a dual-functional Mo-doping strategy is proposed to regulate both the electronic structure and redox chemistry of Na4Fe3(PO4)2P2O7 (NFPP). Mo incorporation is demonstrated to play two synergistic roles: (1) Introducing additional Mo6+/Mo5+ redox-active centers that contribute extra capacity at ∼2.1 V (vs. Na+/Na), and (2) inducing pronounced electronic modulation by lowering the conduction band minimum, thereby significantly narrowing the band gap and facilitating charge transport. Meanwhile, Mo doping effectively suppresses the formation of electrochemically inactive maricite impurities and promotes homogeneous structural evolution during cycling. Benefiting from the coupled regulation of electronic structure, redox activity, and phase stability, the optimized NFMPP-0.12 exhibits a high reversible capacity of 117.6 mAh/g at 0.1 C, outstanding rate capability, and ultralong cycling stability with 92.2 % capacity retention over 6000 cycles at 20 C. In addition, a full cell paired with a hard carbon anode delivers stable and reversible electrochemical performance. This work establishes a dual-function doping paradigm that integrates redox activation with electronic modulation, offering a general strategy for advancing high-performance NASICON-type cathodes.

Na4Fe3(PO4)2P2O7 (NFPP), with its high specific capacity and excellent structural stability, is a promising cathode material for sodium-ion batteries. This study reveals the structural regulation mechanism of cobalt-doped Na4Fe3(PO4)2P2O7 cathode materials and demonstrates their optimization effects on the high-voltage performance of sodium-ion batteries. By combining multi-scale characterization with electrochemical testing, we reveal that Co substitution for Fe can induce lattice expansion, stabilize the (210) crystal plane, and enhance Na+ diffusion. Na4Fe2.7Co0.3(PO4)2P2O7 (NFCPP-3) exhibits remarkable rate performance (82.3 mAh g-1 at 100C) and long-term cycling stability (85.6% after 5000 cycles at 10C). DFT calculations show cobalt doping significantly enhances the material's adaptability under high voltage conditions by reducing the formation energy of the (210) crystal plane (Ef: 0.158 -> 0.151 eV) and Na+ migration energy barriers (0.807 -> 0.623 eV). Furthermore, full cell testing (NFCPP-3//HC) confirms practical application potential, with a capacity retention of 82 mAh g-1 at 10C and 94.1% after 450 cycles at 1C. This study provides a new perspective for developing cathode materials for high-voltage and high-rate sodium-ion batteries.

Silicon-based anodes are considered promising candidates for next-generation lithium-ion batteries because of their ultrahigh theoretical capacity. However, their practical application is still hindered by severe volume variation, low electronic conductivity, and unstable interfacial evolution during repeated lithiation/delithiation. In this work, a Sn-Ni@Si heterostructured silicon-based anode with tunable graphene content was constructed through a facile solution-assisted route followed by thermal treatment. Among the investigated samples, the optimized Sn-Ni@Si-2 composite with 25 wt% graphene exhibited the best overall electrochemical performance, delivering an initial coulombic efficiency of 87.81% at 0.1C and maintaining a reversible capacity of 677.09 mAh g(-1) after 100 cycles at 0.5C. Structural and electrochemical analyses suggest that the improved performance is associated with the synergistic effects of a continuous graphene conductive framework, improved dispersion of active components, and a stabilized interfacial chemical environment involving Si-O-Sn bonding. The optimized composite also shows reduced charge-transfer resistance and enhanced Li+ diffusion kinetics compared with the graphene-free counterpart. In addition, density functional theory calculations based on a simplified interfacial model indicate that interfacial electronic coupling may contribute to the enhanced charge-transfer behavior observed experimentally. This work provides a feasible strategy for improving the lithium storage performance of silicon-based anodes through synergistic interfacial regulation and conductive-network design.

The rational design of high-performance catalysts for CO2-assisted propane dehydrogenation (CO2-PDH) is hindered by the complex interplay among catalyst properties, preparation parameters, and reaction conditions. Herein, this study develops an interpretable machine learning (ML) framework that accelerates the discovery of optimal catalysts and deciphers the underlying structure-performance relationships. A meticulously curated data set of 606 experimental data points was used to train and optimize seven ML models. The optimized XGBoost model demonstrated superior predictive accuracy (test R2 = 0.966) for propane conversion, propylene selectivity, and yield. Shapley Additive exPlanations (SHAP) analysis quantitatively ranked the importance of 17 input features, revealing that catalyst descriptors (45.4%) and reaction conditions (37.2%) dominated the catalytic performance. Specific surface area, the primary support material, and time-on-stream were identified as the most critical descriptors governing the catalytic interface and activity. Partial dependence plots further elucidated the nonlinear influence of these key parameters on the target outputs. The framework was subsequently employed for multiobjective optimization, leading to the identification of Pareto-optimal catalysts (e.g., Cr2O3-Co/MSS-2-La2O3) achieving a remarkable propylene yield of 74.95%. Independent experimental validation on unseen catalysts confirmed the model's exceptional generalizability and accuracy, with prediction errors ≤ 4.34%. This work provides a robust, data-driven strategy for the rational design of high-performance catalytic materials by translating black-box predictions into actionable design principles.

Achieving uniform lithium deposition and stable interfacial chemistry remains a critical challenge for the practical application of lithium metal anodes. Herein, a facile combustion deposition approach is employed to construct lithiophilic metal oxide interfaces on three-dimensional nickel foam current collectors. This open-air, seconds-level process enables the controllable fabrication of oxide layers with tunable compositions and loadings, providing a versatile platform for systematically investigating interfacial chemistry under unified conditions. By comparatively studying Mn2O3, ZnO, and Fe2O3, the role of metal oxide chemistry in regulating lithium nucleation behavior and deposition morphology is elucidated. Among them, Mn2O3 exhibits superior lithiophilicity, leading to reduced nucleation overpotential and more uniform lithium deposition. As a result, the Mn2O3-modified electrode delivers stable cycling approximately 2500 h with low polarization in symmetric cells. When paired with a LiFePO4 cathode, the corresponding full cell retains 82.67% of its initial capacity after 300 cycles at 0.5 C. This work demonstrates that this platform, which is potentially extendable to larger dimensions, can effectively correlate interfacial chemistry with lithium plating/stripping behavior, thereby providing a basis for the rational design of lithium metal anodes.

Rechargeable batteries operated based on lithium-metal anodes represent a major breakthrough in the field of electrochemical energy storage. However, the Li-metal batteries (LMBs) are practically hindered by unstable anode chemistry that invites dendrite formation and parasitic reactions, and accounts for rapid battery failure and safety issues. Here we show that a bismuth-based, inorganic-rich artificial solid electrolyte interphase (ASEI) helps to effectively stabilize the anode-electrolyte interface. The interphase is derived from the in situ reaction between Li and Bi(CF3SO3)(3)-LiNO3 salt mixture, and consists of multiple components including Li3Bi, Bi, LiF, and Li3N. The inorganic-rich ASEI demonstrates high electrolyte wettability, lithiophilicity, and mechanical strength, and a low Li+ diffusion energy barrier, so that it promotes uniform Li plating/stripping while effectively suppressing the dendrite formation and volume variation. By applying ASEI, a Li||Li symmetric battery maintains stable cycling for > 1000 h at an ultra-high current density of 10 mA cm(-2) and an areal capacity of 10 mAh cm(-2). LMBs that pair the ASEI-modified Li anode with various layered oxide cathodes exhibit improved cycling and rate performance, and a 10-Ah Li-metal pouch cell demonstrates favorable cycling performance at a high specific energy of > 460 Wh kg(-1), showing promise for the next-generation electrochemical energy storage.