Researchers at Tohoku University have discovered that changing the number of layers in a crystal can systematically tune both the magnetic and topological properties of a material. By synthesizing a brand-new compound, the team showed how adding spacer layers shifts magnetic order from antiferromagnetic to ferrimagnetic - while also producing a highly tilted "type-II" Dirac electron state that can't exist for particles in a vacuum.

This study develops a novel “Panning for Gold” framework that integrates Koopman analysis with clustering algorithms to identify representative water quality dynamic data for wastewater treatment plants (WWTPs). Using a full-scale Anaerobic-Anoxic-Oxic process in southern China as a case study, the research demonstrates that machine learning models can maintain predictive accuracy with 43.59% less training data, establishing a low-data-requirement paradigm for efficient water system management.

Researchers developed a palladium-engineered nickel phosphide catalyst for selective bio-oil upgrading. In anisole tests at 200 ℃, NiPdP achieved near-complete conversion and 97.28% selectivity to cyclohexane; with m-cresol at 250 ℃, selectivity to methylcyclohexane reached 95.72%. Palladium altered catalyst formation, lattice strain and electronic structure, helping suppress unwanted carbon–carbon bond cleavage, sintering and coke formation while supporting reuse over 10 cycles.

Porous liquids combine permanent nanoscale cavities with fluidity, making them promising for applications like carbon dioxide capture, gas separation, and other chemical processes. Their viscosity must be tailored to the intended application: lower-viscosity liquids are easier to pump and circulate while facilitating faster heat and mass transfer, whereas higher-viscosity liquids provide greater structural stability in membrane formation.

Until now, viscosity and pore volume have been notoriously difficult to tune independently. While diluting solvent-containing porous liquids reduces viscosity, it simultaneously lowers the concentration of the porous components. In solvent-free “type I” porous liquids, where the porous molecules themselves form the fluid, changing the length of the attached polymer chains alters the ratio of nonporous material and can inadvertently block access to the pores altogether.

A Kyoto University research team—including Xiangmei Xiang, Dr Zaoming Wang, Professor Kenji Urayama, and Professor Shuhei Furukawa from the Institute for Integrated Cell-Material Sciences (WPI-iCeMS), working with collaborators in Japan and Taiwan—has now developed a molecular strategy that changes the viscosity of a type I porous liquid by more than eight orders of magnitude while retaining its intrinsic porosity. The study was published in the Journal of the American Chemical Society on August 4, 2026.

Researchers from the Dalian Institute of Chemical Physics with collaborators revealed that active motifs can be generated insitu through the reconstruction of NiO surface during POM, identifying the atomic-scale origin of catalytic activity.

Finding better materials to store hydrogen safely and efficiently is key to clean energy, but AI models trained on incomplete data can recommend candidates that don't actually work in practice. An international team led by Tohoku University, drawing on insight from 69 researchers, has proposed a "physics-aware" framework that combines thermodynamic constraints, reproducible data, and real experimental feedback to make AI-guided materials discovery more reliable. The roadmap could help accelerate the search for practical hydrogen storage solutions to support future clean-energy systems.

Published chemical literature has accumulated a wealth of reaction data, but this data comes from diverse sources, is unevenly distributed, and is clearly biased towards highly selective "successful experiments." Can such literature data be used for reliable machine learning modeling and further guide new catalytic reactions? Professor Luo Sanzhong's team and collaborators at Tsinghua University recently reported OrgAIcat, a machine learning framework for asymmetric organocatalysis. The research team manually compiled over 22,000 data points on Aldol and Michael addition reactions from 844 papers to construct the iSynth dataset. Based on this, the team developed R-SPOC, a reaction descriptor that requires no quantum chemical calculations, for predicting enantioselectivity and screening catalysts. When OrgAIcat was combined with Bayesian optimization, a challenging Aldol reaction was improved from 41% ee to 96% ee, with the entire optimization process requiring only 12 experiments. These results demonstrate that after rigorous compilation, proper characterization, and sufficient validation, historical literature data can still be used to guide experiments, despite its heterogeneity and publication bias. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

Excess nitrogen entering rivers, lakes, and groundwater is a major environmental challenge worldwide. Fertilizers, livestock waste, domestic sewage, atmospheric deposition, and other diffuse sources can all contribute to nitrogen pollution, yet determining exactly where the nitrogen comes from and how it moves through a watershed remains difficult.

The few millimeters of soil surrounding plant roots may be small in scale, but they are among the most biologically and chemically active regions on Earth. In a new review published in Biochar, researchers propose that biochar should be viewed not simply as a soil amendment, but as a “rhizosphere interface engineer” capable of coordinating physical, chemical and biological processes around plant roots.