A single-component electrocatalyst with weak and unidirectional catalytic activity cannot realize high-performance Li-S batteries with rapid sulfur redox kinetics and restricted shuttling. In this work, a heterostructured mixcrystal electrocatalyst of MoC-Mo2C nanodot-embedded carbon sheet-assembled hollow architecture (MoC-Mo2C/CSHA) was constructed to modulate the reaction behaviors of sulfur species. In situ X-ray diffraction and in situ electrochemical impedance spectroscopy unveiled an enhanced catalytic activity of MoC-Mo2C/CSHA for the bidirectional conversions of sulfur species, and the ensuing theoretical study confirmed the synergistic catalytic effect of the MoC-Mo2C structure originating from the integration of two different components. Further postcycling characterization suggested the prevention of anodic side reactions and the suppression of the polysulfide shuttling phenomenon. Consequently, the assembled Li-S batteries demonstrated a high rate performance of 706 mAh g-1 at 3.0 C and good long-life cyclic stability lasting for 500 cycles at 1.0 and 2.0 C. Moreover, both coin cells under high sulfur loadings and pouch cells exhibited steady cyclic performance, with a maximum areal capacity of 5.09 mAh cm-2. This study showcases the extraordinary advantages of heterostructures serving as electrocatalysts and providing an efficient route to advance Li-S batteries into practical utilizations.

Sodium-ion batteries are promising for large-scale energy storage due to resource abundance and low cost. Layered NaxTMO2 cathodes offer high specific capacity and tunable compositions but face challenges in structure-property correlations, sluggish kinetics, and phase instability. First-principles calculations elucidate sodium-storage mechanisms and thermodynamic stability yet struggle with high-dimensional design, finite-temperature effects, and long-timescale dynamics. Artificial intelligence (AI) now enables efficient property prediction, composition screening, and mechanism analysis. Integrating physics-based models with AI establishes an intelligent computing framework-combining mechanistic interpretability and data-driven efficiency-that shifts research from isolated computations to high-throughput, multiscale, closed-loop optimization. This review summarizes advances in theoretical calculations, AI applications, and their convergence toward this paradigm. It highlights how the "physics-constrained modeling-data-driven prediction-experimental feedback" loop addresses high-voltage stability, phase-transition control, and kinetic limitations, enabling targeted cathode design. Future directions include active learning, generative inverse design, and automated experimentation. Overall, intelligent computing is steering layered cathode development from empirical trial-and-error toward demand-oriented rational design.

Hard carbon (HC) anodes exhibit low initial Coulombic efficiency (ICE) and reversible capacity, originating from irreversible Na storage sites. We systematically probe Na-ion interactions with various atomicscale structures of HC, in which edge defects and C=O groups exhibit high adsorption energy towards Na ions, leading to irreversible Na-ion storage. Herein, we propose a reconstruction strategy of the defect structures through phosphorus (P) vapor treatment, in which the defect structures can be bonded with a P atom, thereby generating the functional groups exposed to P, such as C-P and O-P. The Preconstructed sites exhibit moderate Na-adsorption energies, effectively converting irreversible Na storage into reversible behavior. Moreover, these sites further induce the formation of an inorganic-rich solid electrolyte interphase (SEI) that exhibits high Na ion conductivity. Therefore, the synthesized Preconstructed HC (P-HC) anode demonstrates markedly improved Na-storage capabilities, achieving a high initial reversible capacity of 454 mAh g-1 with an ICE of 90.4% and a fast-charging performance of 270 mAh g-1 after 675 cycles at 3.75 C, significantly surpassing the pristine HC anode (333 mAh g-1 and 80.9% ICE; 234 mAh g-1 after cycling). (c) 2026 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

MnMoO4 holds great promise as a cathode material for lithium-oxygen batteries (LOBs), but its poor conductivity and weak interaction with oxygenated intermediates substantially impede its electrocatalytic properties. Herein, electron-deficient P atoms were incorporated with MnMoO4 hollow nanospheres (P-doped MnMoO4) to realize internal orbital interactions between Mo 4d and P 3p, activating external orbital hybridization between catalysts and LiO2 during cycling. This relay orbital hybridization not only promoted charge transfer but also optimized the adsorption and desorption abilities of catalysts toward LiO2, thereby reducing the reaction energy barriers. Consequently, LOBs with P-doped MnMoO4 cathode catalysts sustained steady operation for 380 cycles under 1000 mA g-1 , which is even better than some of their noble metal counterparts and points to their commercial promise for use in future large-scale applications. This work provides general guidance for constructing relay orbital hybridization through P doping on catalysts for LOBs and other electrocatalytic systems.

ABSTRACT Lithium manganese iron phosphate (LiMn 0.4 Fe 0.6 PO 4 , LMFP) offers a significant improvement in operating voltage and energy density compared to Li (lithium) iron phosphate (LiFePO 4 , LFP), garnering considerable research attention in recent years. However, LMFP suffers from low electronic conductivity and sluggish ion diffusion kinetics, resulting in poor performance under high current densities. Furthermore, the Jahn–Teller effect associated with Mn 3+ in LMFP leads to Mn (manganese) dissolution during electrochemical reactions, which compromises structural stability and leads to suboptimal long‐term cycling stability. In this study, a simple ball milling‐sintering method was employed to successfully incorporate Hf 4+ (Hafnium) into the transition metal sites of LMFP. The higher bond energy of Hf–O compared to Mn–O enables the construction of a stable Mn–O framework through Hf doping, thereby stabilizing the lattice structure, reducing Mn dissolution, and significantly enhancing the long‐term cycling performance of the material. Furthermore, Hf 4+ doping improves the redox reaction kinetics of the material, increasing both the lithium‐ion diffusion rate and electronic conductivity. Among the tested materials, LMFP‐3%Hf exhibited the most outstanding cycling stability (with a capacity retention rate of 89.7% after 400 cycles at 1C) and rate capability (delivering a discharge specific capacity of 70 mAh g −1 at 10C).

Metallographic image segmentation underpins automated metallographic analysis, yet pixel-level annotation is costly. Metallographic microstructures exhibit dramatic size differences, including cross-scale structures such as large-scale matrix phases, grain boundary cementite networks, and acicular Widmanstätten structures. Existing feature extraction modules cannot balance the semantic integrity of large regions and the fine details of microstructures, causing missed small microstructures and blurred segmentation boundaries. Current semi-supervised methods only impose consistency constraints on perturbed input images at the final prediction output, ignoring multi-scale semantic features from decoder upsampling stages. This leads to noisy supervision signals, low-quality pseudo-labels, and poor generalization. To address these issues, this paper proposes a semi-supervised metallographic image segmentation model, EISC, integrating MT cross-network scale feature extraction and decoder multi-scale consistency constraints. It adaptively fuses multi-receptive-field features and introduces a regularization term to maintain the semantic consistency of multi-scale decoder outputs, improving pseudo-label quality. Comparative and ablation experiments verify that EISC effectively enhances the segmentation accuracy and robustness of metallographic images.

Abstract Room-temperature sodium–sulfur batteries are practically restricted by the polysulfide shuttle effect, which requires cathode hosts that combine effective physical confinement and chemical catalytic activity. Herein, we develop a Ce-doped hierarchical nanoporous carbon (Ce-MPC) host that integrates physical confinement with tailored interfacial chemistry. The highly dispersed Ce species serve dual functions: strong chemisorption of polysulfides and accelerated catalytic conversion of soluble long-chain sodium polysulfides into insoluble short-chain Na2S2/Na2S. These nanoscale design elements work synergistically to suppress the polysulfide shuttle. As a result, the Ce-MPC@S cathode delivers a specific capacity of 811 mA h g–1 at 0.1 A g–1 after 100 cycles, and an ultralong-life of 1000 cycles with a capacity retention of 367 mA h g–1 at 2.0 A g–1. This work highlights the crucial surface chemistry kinetics coupling for high-performance RT Na–S batteries.

Noble metal-containing MAX phases and their MXene derivatives marry the ordered, conductive M–X scaffold with noble metal catalytic and electronic functionalities. This paper reviews topotactic strategies that retain the M–X framework while modifying the A site: (1) “chemical scissor” through Lewis acid salts etching the A site and creating vacancies for noble metal incorporation; (2) thermally induced exchange via noble metals film diffusion, effective yet interface limited, with criteria on chemical potentials, inertness, and temperature; and (3) powder metallurgical insertion of low-melting noble metal alloys. For MXenes, we outline direct noble metal bonding pathways to X-site via vacancy coordination, to M-site through strong metal-support interactions, and to terminations/dopants. These bonds accelerate charge transfer, stabilize single atoms/clusters, and drive superior electrocatalysis, gas/biosensing, and biomedical activity. We also highlight isolating monolayer noble metals from noble metal-MAX and chart scalable noble metal insertion into M/X sites for next-generation catalysts and devices.

Energetic multi-principal-element alloys (EMPEAs) have garnered considerable attention for their reactive characteristics and compositional flexibility in pyrotechnic applications. However, the complex fragmentation behavior under impact loading presents challenges for optimal design. This study established a machine learning framework to predict the mean particle size (MPS) of EMPEAs under ballistic impact conditions and achieve end-to-end material design. Using ballistic gun experiment data from 110 EMPEA samples, multi-stage feature dimensionality reduction through Pearson correlation analysis and genetic algorithm identified five key predictors from 28 initial material descriptors. Among six evaluated algorithms, the AdaBoost model demonstrated optimal performance (R2 = 0.851). Model interpretability analysis revealed that atomic radius mismatch (ΔR), mixing entropy (ΔSmix), thermodynamic parameter (ΔTE), bulk modulus mismatch (ΔK), and impact velocity (v), all exhibit negative correlations with MPS, with ΔR identified as the most critical factor. Symbolic regression confirmed ΔR's exponential negative relationship with MPS. Ti15Zr50Ta35 EMPEA was designed and experimentally validated, achieving a quasi-static overpressure of 0.22 MPa at 1300 m/s impact velocity with size-dependent oxidation behavior in fragments, outperforming most existing energetic structural alloys. This work demonstrates the effectiveness of machine learning in understanding impact fragmentation mechanisms and facilitating the design of high-performance EMPEAs with enhanced energy release characteristics.

Hydrogen isotope (H/D/T) permeation in structural materials challenges the safety of nuclear fusion systems and hydrogen energy technologies. In this study, first-principles calculations are combined with bond- and chargeanalysis to investigate how M-site chemistry and H/D/T behavior jointly modulate radiation tolerance of M2AlC (M = Ti, V, Cr). It is found that hydrogen is thermodynamically stable within Ti-Al interlayers of Ti2AlC but unstable in V2AlC and Cr2AlC. Climbing-image nudged elastic band (CINEB) calculations show sequential reduction of hydrogen diffusion barriers with increasing M-site atomic number (Ti -> V -> Cr). These phenomena correlate with M-Al interlayer spacing and local bonding environments. Bonding analysis reveals that the stronger M-H and Al-H bonds in Ti2AlCH1/8 endow hydrogen with greater thermodynamic stability and higher diffusion energy barriers in Ti2AlC compared to V2AlC and Cr2AlC. Crucially, hydrogen insertion lowers MA-AM cation anti-site defect formation energies, most notably in Cr2AlC (1.70 -> 0.86 eV), enhancing radiation-induced defect recovery. The differential radiation resistance induced by hydrogen across the three MAX phases originates from the disparity in electron-donating capacities of the M-site elements. These findings establish M-site electron donation as a design lever to tailor hydrogen-mediated radiation resistance in MAX-phase materials.

Silicon suboxide (SiOx) is a promising anode material for next-generation high-energy-density lithium-ion batteries due to its high theoretical capacity. However, pronounced volume expansion during lithiation leads to structural failure and interfacial instability, severely limiting its practical application. Conventional approaches, such as carbon coating or nanostructuring, mainly provide passive buffering and fail to fundamentally mitigate mechanical degradation. Herein, a synergistic modification strategy integrating mechanical reinforcement and interfacial catalysis is proposed for silicon-based anodes. High-performance ceramic silicon nitride (Si3N4) is incorporated into SiOx via high-energy ball milling (HEBM), forming a SiOx-Si3N4 composite anode (denoted as SiOx@Si3N4-HEBM). The introduced Si3N4 establishes a rigid supporting structure that suppresses volume expansion and particle agglomeration during lithiation, thereby alleviating mechanical stress. In addition, Si3N4 catalyzes the in situ formation of a Li3N-rich solid electrolyte interphase (SEI), enhancing interfacial ion-transport kinetics. At a reversible capacity of 1350 mAh g-1, the capacity retention after 100 cycles at 0.5 C is improved from 38.89% to 64.36%, accompanied by enhanced rate capability and significantly reduced interfacial impedance. This work offers an effective strategy for improving the cycling stability of silicon-based anodes through coupled mechanical and interfacial regulation, highlighting its potential for practical applications.

With the development of new energy technologies, electric vehicles are becoming increasingly popular. Early battery fault detection is crucial for ensuring personal safety and minimizing property damage. However, traditional fault diagnosis methods often have difficulty detecting early-stage faults. To address this challenge, in this study, we propose a novel electric-vehicle battery fault diagnosis method that integrates information granularity and the segmented-slope feature. First, the original voltage data are denoised using the locally weighted scatterplot regression algorithm. Second, segmented-slope features are extracted from the voltage signal to enhance the distinction between healthy and faulty cells. Next, information granularity theory is applied to select the reference cell. The quality of each individual cell’s feature curve was evaluated based on its granularity value, and the mean granularity of the selected cells was then used to construct the reference feature cell. Finally, a dynamic threshold model, constructed based on the Manhattan distance and three-sigma criterion, provides an adaptive threshold adjustment mechanism for early warning and fault location. In the current tests involving four labeled vehicles (one normal and three with internal short circuit faults), the accuracy of this method was very high.

A P2-type layered oxide delivers exceptional long-term sodium-storage performance through highly reversible Fe 2+ /Fe 3+ redox chemistry, achieving 93.04% capacity retention after 80 000 cycles over a wide temperature range.

Silicon is a well-known anode material for lithium-ion batteries that has attracted a lot of interests because of its high theoretical specific capacity (4200 mAh g-1). However, its severe volume expansion during cycling leads to structural degradation and rapid capacity fading. The design of porous silicon architectures has emerged as a fundamental and effective strategy to mitigate these issues by accommodating mechanical stress and preserving electrode integrity. Concurrently, the development of advanced in situ/operando characterization techniques has shifted the research paradigm, enabling direct observation of dynamic structural and interfacial evolution under operating conditions. This review systematically summarizes recent progress in the rational design of porous Si-based anodes and critically examines how state-of-the-art in situ methods provide direct mechanistic validation of these designs. The work highlights the synergistic interplay between targeted material engineering and in situ/operando characterization, offering a roadmap for the development of high-performance porous silicon anodes.

High energy-density lithium–sulfur (Li–S) batteries with rapid intermediate conversion and forbidden shuttle effect require superior electrocatalysts with tunable catalytic activity. In this protocol, binary FeNi3 alloy nanoparticles homogeneously embedded within carbon nanosheets (FeNi3/CNS) are synthesized to regulate the conversions of sulfur species. Time of flight-secondary mass ion spectroscopy reveals a significantly improved catalytic effect of binary FeNi3 alloy compared to bare Ni, which is confirmed by a larger Li2S amount generated during in situ X-ray diffraction measurement. Further anode characterization validates efficient shuttling suppression and good lithium metal protection. In Li–S batteries, electrochemical tests demonstrate a remarkable rate capability of 852 mAh g−1 at 3.0 C, and outstanding long-term cycle at 1.0 C (639 mAh g−1 after 500 cycles). Even under a wide operation temperature range (−15–60 °C), Li–S batteries exhibit stable cycling with high specific capacities under high current rates. Moreover, Li–S batteries using FeNi3/CNS attain a maximum areal capacity of 5.60 mAh cm−2 under ∼4.0 mg cm−2 sulfur. This study highlights the advantages of adopting binary or multi-component metal alloys as electrocatalysts and points out the research directions to advance Li–S batteries into practical applications.

This study proposes an in-situ interfacial design strategy for short carbon fiber-reinforced SiC composites (C-sf/SiC) as a simplified alternative to complex CVD coating by utilizing controlled reactions between sintering aids and carbon fibers. Three additive systems-Al2O3-Y2O3, Al4C3-B4C-C, and Al-B-C-were employed to fabricate composites via spark plasma sintering at 1700 degrees C, 1750 degrees C, and 1800 degrees C. 1750 degrees C was identified as the optimum temperature, achieving high densification (>92% relative density) while avoiding excessive fiber erosion. Comprehensive characterizations revealed distinct interfacial reaction mechanisms and their effects on composite performance. The Al2O3-Y2O3 system triggered severe carbothermal reduction, forming a porous yttrium aluminum garnet (YAG)-rich reaction layer, resulting in an interfacial strength of 162 MPa, moderate flexural strength (232 +/- 35 MPa) and fracture toughness (4.5 +/- 0.2 MPa m(1/2)), yet high porosity (6.48%) and poor ablation resistance (mass ablation rate R-m = 0.30 mg/s under oxyacetylene flame at 2.1 MW/m(2) for 60 s). The Al4C3-B4C-C system preserved a clean interface with strong bonding (575 MPa), leading to brittle fracture, the lowest mechanical properties (flexural strength: 199 +/- 38 MPa; fracture toughness: 3.9 +/- 0.2 MPa m(1/2)), and limited ablation improvement (R-m = 0.16 mg/s). In contrast, the Al-B-C system promoted a uniform similar to 1 mu m Al2O3-rich interphase, achieving an optimally weakened interface (119 MPa) due to thermal expansion mismatch and partial fiber graphitization. This delivered the best mechanical performance (flexural strength: 353 +/- 9 MPa; fracture toughness: 4.8 +/- 0.2 MPa m(1/2)) and superior ablation resistance (R-m = 0.12 mg/s), as the dense, uniform interphase effectively shielded carbon fibers from oxidation. The results demonstrate that in-situ tailored interphases, enabled by strategic additive selection, can concurrently enhance the mechanical and ablation performance of C-sf/SiC composites.

Lithium metal batteries (LMBs) integrating high-voltage nickel-rich cathodes are promising candidates for energy densities exceeding 500 Wh kg-1. However, their practical development is hindered by severe interfacial reactivity at both electrodes, which imposes stringent requirements on electrolyte design. A fundamental challenge lies in the insufficient understanding of interactions among electrolyte components and their potential synergistic effects, which restricts precise regulation of solvation structures and simultaneous stabilization of dual interfaces. Herein, we propose a molecular orchestration strategy based on complementary molecular charge engineering to induce mutual reinforcement among electrolyte constituents. Functional molecular pairs with complementary charge demands are rationally integrated through cooperative intermolecular association, serving as synergistic precursors. The resulting intermolecular charge transfer modulates Li+ coordination affinity and enhances the reductive and oxidative reactivity of each additive, enabling synchronous dual-interface optimization. Consequently, the electrolyte supports ultrahigh voltage operation (4.6 V), fast charging (10C), a wide temperature (-25 to 50 degrees C) and long-term stability over 3200 h. Notably, a high energy density exceeding 513 Wh kg-1 (based on the total cell weight) is attained in 5.4 Ah Li & Vert;NCM811 pouch cells under harsh practical conditions. Departing from conventional reliance on high-concentration electrolytes or increased additive dosages, this work establishes a practical electrolyte-engineering paradigm centered on the systematic organization and activation of latent functionalities, providing an energetic perspective for high-performance LMBs.

Taking lithium manganese iron phosphate (LMFP) as the research object, surface modification was used to carry out the modification research on it, and the effects of the Ni coating method, different precipitating agents and different coating amounts on the electrochemical performance of LMF cathode materials were investigated. The effects of different precipitants (nickel oxalate and nickel hydroxide) and nickel coating amounts on the electrochemical performance of LMFP materials were investigated, and the optimal coating conditions were determined. The optimum cladding conditions are: nickel sulfate and sodium hydroxide as precipitant cladding 1% nickel. The discharge capacity at 0.2C magnification was 150.9mAhg(-1), and the retention of discharge specific capacity after 100 cycles was 95% (0.5C), 92.6% (1C) and 86.9% (2C), respectively.