Inspired by the vectorial lattice Boltzmann method for linear elastodynamics (Boolakee et al., 2025), we construct a total-Lagrangian vectorial lattice Boltzmann formulation for twodimensional finite-strain hyperelastic dynamics. The governing equations are first written as a conservative first-order system for the material velocity and the full deformation gradient. This representation separates the kinematic part of the dynamics from the constitutive closure: the first Piola-Kirchhoff stress is evaluated locally from the current deformation gradient and enters the lattice only through nonlinear flux moments. A D2Q4 stencil with six-component vector populations is then used to match the state and the two material-coordinate fluxes. The formulation includes a second-order population initialization, trapezoidally centered body forcing, displacement reconstruction by velocity quadrature, and half-way reconstructions for velocity Dirichlet and Neumann traction boundaries on grid-aligned domains. The resulting method preserves the local collide-stream structure of standard lattice Boltzmann schemes while adapting the vectorial first-order strategy from linear elastodynamics to hyperelastic finite-strain dynamics.
Large language models (LLMs) can turn grid-analysis requests into executable programs for power-system simulation, but utilities and research laboratories often require on-premise deployment. In this setting, first-pass failures frequently arise at an API-knowledge boundary, through hallucinated functions, misused parameters, and mishandled result tables.We present PowerCodeBench, a parameterised benchmark generator released as a frozen 2000-task suite for pandapower, and a deployment-time workflow that requires no weight updates. Documentation-driven L0–L3 probes produce per-model API profiles for diagnosis, model comparison, documentation allocation, and backend calibration. A query-side demand estimator selects layered API evidence before generation, while execution feedback routes targeted repair.Across ten open-weight LLMs (1.5B–480B) and four mid-tier APIs, the validation-enabled workflow raises scalar-match accuracy by 32–56 percentage points after up to three repair rounds relative to an unassisted first pass, for every model of at least 7B and every API. Open-weight models in the 70B–120B range reach the four-vendor mid-tier accuracy range under matched no-tool conditions. Selective injection approaches the full-layer reference using 41% of its prompt tokens. Among model–item pairs passing numerical checks under both workflows, engineering review confirms the requested analysis in 88% of full-workflow outputs versus 66% under plain repair. Round-0 pilots on OpenDSS and PyPSA motivate staged onboarding from broad retrieval at cold start to calibrated selective injection. Measured throughput, latency, energy, and allocated GPU memory establish a practical on-premise serving envelope.
China's power sector is undergoing rapid decarbonisation while simultaneously expanding generation. Although the power sector's greenhouse gas (GHG) emission trajectories under various decarbonisation pathways have been extensively researched, other non-GHG environmental implications remain poorly investigated, particularly due to limited spatially resolved data to trace where the actual impact occurs globally. Therefore, this study employs a spatially explicit life cycle assessment method to evaluate the environmental synergies and trade-offs of China's power sector under two future scenarios in 2050. A comprehensive portfolio of provincial-level, technology-specific life cycle inventories for China's current and future power systems are compiled, incorporating country-specific supply chain processes for key materials. The results show substantial decreases in GHG Emissions, from 5.5 Gt CO2eq in 2020 to 3.2 and − 1.02 Gt CO2eq by 2050 under the two scenarios. Synergies are observed between GHG Emissions and Particulate Matter Formation, with the latter declining by 31–70% compared to 2020. However, these synergies are accompanied by significant environmental trade-offs, as Land Transformation, Freshwater Eutrophication and Mineral Resource Use increase significantly. Spatially, land and water related impacts are gradually concentrating in China, while mineral related impacts are increasingly spreading to other countries and regions. Provincial contributions to national total impacts are also significantly reshaped under future scenarios because different power system configurations redistribute the ultimate cause of environmental impacts. This study highlights the need for climate policies to balance climate targets with broader environmental considerations for sustainable decarbonisation and to coordinate tailored mitigation across technologies, provinces, and global supply chains to support a more equitable and just transition.
This study uses density functional theory (DFT) to investigate the selective hydrogenation of acetylene (C2H2) on palladium-based catalysts with core@shell (M13@Pd42) and Crown-Jewel (CJ-M43Pd12) structures. The influence of Cu, Ag, and Au dopants are evaluated to guide the design of highly efficient Pd-based catalysts for converting acetylene to ethylene (C2H4). The calculation reveals that the Crown-Jewel structured Cu43Pd12 (CJ-Cu43Pd12) catalyst exhibits superior activity and selectivity for ethylene production compared to the core@shell structured Cu13@Pd42 and the pure Pd55 catalysts. Electronic structure analysis reveals that the Cu-Pd interaction and electron transfer from Cu to Pd enhance performance in the CJ-Cu43Pd12 catalyst. The findings highlight the significance of geometrical and electronic considerations in the design of catalytic systems for acetylene hydrogenation. These results establish an atomistic theoretical framework for Pd-efficient acetylene semihydrogenation catalysts, demonstrating that the Crown-Jewel geometry can integrate Pd-site isolation and Cu to Pd electronic modulation to balance C2H2 activation, C2H4 desorption, and Pd atom utilization efficiency.
Decoupling the intrinsic interdependence between thermoelectric transport parameters remains a central challenge in the development of high-performance thermoelectric materials. We reveal that interfacial polarization-driven ionic thermoelectric transport in the lead-free halide perovskite Cu2AgBiI6 (CABI) enables an alternative pathway for thermovoltage generation that partially alleviates this constraint. Solution-processed CABI films deposited on fluorine-doped tin oxide glass develop a hierarchical flower-vase morphology that promotes strong interfacial polarization and facilitates coupled ionic-electronic transport under thermal gradients. The resulting system exhibits a large Seebeck coefficient (similar to 4.7 mV K-1), resulting in high thermovoltage. Time-dependent thermovoltage, capacitance measurements, and impedance spectroscopy reveal thermally activated polarization associated with restricted ionic migration within the CABI lattice, which amplifies the thermovoltage and further relaxes the intrinsic coupling between thermoelectric transport parameters. This polarization-mediated ionic thermoelectric behaviour establishes halide perovskites as a new platform for hybrid ionic-electronic thermoelectricity and enables proof-of-concept semi-transparent thermoelectric glazing and wearable heat-harvesting devices. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
