Researchers have developed a zirconium‑modified LiMn₂O₄ film electrode for selective lithium extraction from brine via electrochemically switched ion exchange. Zr modification simultaneously enhanced electronic conductivity and structural stability by tuning the electronic structure and strengthening Mn–O hybridization. In real brine from Qarhan Salt Lake, the optimized LZMO‑0.03 electrode achieved a lithium intercalation capacity of 22.16 mg·g⁻¹ and a Li/Mg separation factor of 685.43, with energy consumption as low as 3.61 Wh·mol⁻¹ under constant current‑constant voltage mode. After 20 cycles, Mn dissolution was reduced to approximately 51.8 % of that of the pristine LMO electrode.

Researchers have developed a nickel‑cobalt layered double hydroxide nanosheet catalyst supported on nickel foam for the selective electrooxidation of isobutanol to isobutyric acid under mild alkaline conditions. The optimized catalyst exhibits a three‑dimensional hierarchical nanosheet architecture with an enlarged electrochemically accessible surface area and favorable charge‑transfer behavior. It achieved a Faradaic efficiency of 91.3 % and selectivity of 93.2 % toward isobutyric acid at 1.52 V vs. RHE, significantly outperforming the corresponding monometallic hydroxide catalysts. Mechanistic studies supported a sequential oxidation pathway via adsorbed isobutyraldehyde intermediates.

As brain-computer interfaces become increasingly precise in reading human emotions, a practical question arises: Will a model trained today still be able to recognize the emotions of the same person tomorrow, in a few weeks, or even months later? Recently, the team led by Shuang Liu from the Medical School of Tianjin University/Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration published a research paper titled "Toward Sustainable Cross-Time Affective Brain–Computer Interfaces: Explicit Modeling and Structural Calibration of Temporal Variability" in the National Science Review (NSR). This study offers a cross-time "EEG ruler" for affective brain-computer interfaces (aBCIs). To address temporal variability in cross-time emotion recognition, the researchers propose a Multi-level Dynamic Integrated Perception Network (MDIN), which explicitly models and structurally calibrates temporal variability on three levels: emotional neural representation, dynamic functional connectivity, and cross-time discriminative structures, offering a new technical pathway for long-term stable affective brain-computer interfaces. This work may further accelerate the transition of aBCIs from short-term experimental validation to long-term, adaptive use in real-world mental health monitoring and human-machine interaction.

Researchers have developed a high‑accuracy Al‑O‑H machine learning interatomic potential combined with global optimization to explore the structural landscape of γ‑Al₂O₃ and its influence on propane dehydrogenation over single‑atom Pt catalysts. Two energetically favorable structures, γ‑NAV and γ‑AV, were identified with energies lower than the conventional model by up to 34.44 meV·(f.u.)⁻¹. These optimized structures expose abundant penta‑coordinated Al³⁺ sites on the (100) surface, serving as preferred anchoring sites for Pt atoms. Catalytic calculations demonstrate that γ‑NAV and γ‑AV significantly reduce propane dehydrogenation activation barriers through enhanced metal‑support interactions.

High‑throughput workflows have revolutionized experimental heterogeneous catalysis by enabling rapid, parallel synthesis and screening of catalyst libraries. This review traces the evolution of high‑throughput technology from manual experiments in the early 1900s to today‘s autonomous, artificial intelligence‑driven platforms that integrate robotics, machine learning, and self‑driving laboratories. The state‑of‑the‑art mode encompasses five key steps: experiment design, catalyst synthesis, catalyst testing and screening, catalyst characterizations, and data handling and analysis. Despite its transformative potential, high‑throughput workflows face critical pitfalls—including gaps between microscale reactor results and industrial performance, pressure limitations, and chemical information disparity in funnel‑shaped screening—that must be addressed to fully realize its promise for rational catalyst discovery.

Structures critically control hydrothermal gold mineralization and orebodies distribution. Using borehole data, deep geophysical surveys, and 3D geological models from typical Jiaodong gold deposits, this study reveals the ore-controlling role of fault-dip variations. Gold orebodies preferentially occur in gently dipping fault segments and at steep-to-gentle transitions, forming a step-like mineralization pattern. Comparison with gold deposits across China identifies three step-control styles: (1) gently dipping segments and steep-to-gentle bends of a single fault; (2) subsidiary gently dipping faults linked to a steep master fault; (3) gently dipping interlayer structures bounded by steep faults. Mechanical analysis shows that gently dipping faults concentrate stress, provide good sealing, and have low wall-rock shear strength, which together promote fluid focusing and gold precipitation. These findings refine the theory of hydrothermal gold mineralization and offer valuable guidance for deep gold exploration.

Researchers have developed a high‑precision state of health prediction model that combines physical information constraints and multi‑dimensional features for lithium‑ion batteries under random variable load conditions. A neural network‑transformer framework based on physical information was constructed, converting the consistency laws of irreversible battery aging into regularization loss terms in the training process to ensure physical rationality. Multidimensional coupled features including voltage, current, and cycle count were extracted, and Bayesian optimization was applied to adaptively adjust key hyperparameters. Experimental results show that the model outperforms comparison models in terms of MAE, RMSE, and R² metrics, effectively suppressing non‑physical fluctuations and balancing high prediction accuracy with physical rationality.

 Researchers have developed an integrated analytical framework combining multi‑physics mechanism simulation and active learning‑based intelligent optimization to address the challenge of synergistic optimization of gas diffusion layer physical properties in anion exchange membrane water electrolyzers. The active learning‑based weighted ensemble surrogate model achieved a coefficient of determination of 0.9994 on the test set with a root mean square error of 5.0240 and a mean absolute error of 4.0694. The optimal parameter combination identified by the framework was a permeability of 1.18 × 10⁻¹⁰ m², an electrical conductivity of 2208.86 S·m⁻¹, and a porosity of 0.605, which effectively suppressed local heat accumulation and eliminated mass transport bottlenecks.

Researchers have developed an integrated analytical framework combining multi‑physics mechanism simulation and active learning‑based intelligent optimization to address the challenge of synergistic optimization of gas diffusion layer physical properties in anion exchange membrane water electrolyzers. The active learning‑based weighted ensemble surrogate model achieved a coefficient of determination of 0.9994 on the test set with a root mean square error of 5.0240 and a mean absolute error of 4.0694. The optimal parameter combination identified by the framework was a permeability of 1.18 × 10⁻¹⁰ m², an electrical conductivity of 2208.86 S·m⁻¹, and a porosity of 0.605, which effectively suppressed local heat accumulation and eliminated mass transport bottlenecks.

Now, researchers from Forschungszentrum Jülich in Germany, RWTH Aachen University and Stanford University have published (DOI: 10.1016/j.esci.2026.100589) a comprehensive review in the journal eScience that synthesizes the current understanding of dendrite formation at the solid electrolyte/lithium metal anode interface. Available online since 7 May 2026, the review examines inorganic, polymer and hybrid solid electrolytes, establishing critical correlations between key parameters and the processes that govern dendrite growth.