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.

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.

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.

When a material heats up, its electrical resistance often rises. The harder question is what, exactly, is getting in the way.

The electrons carrying the current may be scattered by vibrations of the material’s atomic lattice, known as phonons. They may also collide with one another. Frustratingly, a conventional temperature test warms the electrons and the lattice together, so the effects arrive tangled in the same resistance measurement.

Researchers from the Institute for Functional Intelligent Materials (I-FIM) at the National University of Singapore (NUS) [WN1] have now pulled those effects apart in twisted bilayer graphene, a material made by stacking two sheets of graphene at a slight angle. In a paper published in Nature Communications on 13 August 2026, the researchers used terahertz radiation to warm the electrons while keeping the surrounding lattice almost unchanged. The material’s resistance climbed by several kilo-ohms in devices twisted close to the so-called magic angle, revealing a strong electronic contribution even in regimes often associated with phonons.

 [WN1]linked

When a material heats up, its electrical resistance often rises. The harder question is what, exactly, is getting in the way.

The electrons carrying the current may be scattered by vibrations of the material’s atomic lattice, known as phonons. They may also collide with one another. Frustratingly, a conventional temperature test warms the electrons and the lattice together, so the effects arrive tangled in the same resistance measurement.

Researchers from the Institute for Functional Intelligent Materials (I-FIM) at the National University of Singapore (NUS) [WN1] have now pulled those effects apart in twisted bilayer graphene, a material made by stacking two sheets of graphene at a slight angle. In a paper published in Nature Communications on 13 August 2026, the researchers used terahertz radiation to warm the electrons while keeping the surrounding lattice almost unchanged. The material’s resistance climbed by several kilo-ohms in devices twisted close to the so-called magic angle, revealing a strong electronic contribution even in regimes often associated with phonons.

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.

A team led by researchers from the University of Osaka has revealed the first structure of an umami taste receptor. They found that the pufferfish receptor can sense many more amino acids than the human receptor, including some linked to flavors humans detect as sweet. This helps the fish to detect more types of savory chemicals in their food. The research also shows how the receptor’s structure makes this flexibility possible. The findings could help develop new umami flavors.