A data-knowledge dual-driven machine-intelligence framework enables experiment-efficient, full-chain synthesis of 2D dendrites. It achieves rapid process optimization within one week, constructs high-fidelity recipe-morphology mappings, and deciphers multifactorial growth mechanisms, requiring <70 experiments, which covers <1.5% of the total parameter combinations.

Researchers have developed a strategy to improve the fire safety of polymer materials by using ZIF-67, a porous metal-organic framework, together with different flame-retardant components. The study explains how ZIF-67 can enhance fire resistance, reduce smoke release and provide a versatile platform for designing safer high-performance polymer composites.

- Professor Sarah Yunmi Lee’s research team in KAIST’s Department of Chemistry developed a strategy to balance radical generation and catalyst regeneration.

- Uses even substances that previously did not react easily, synthesizing target compounds under mild conditions such as room temperature

- Expected to be applied to developing new catalysts for building complex molecules used in pharmaceuticals, bioactive substances, etc.

Researchers at the Institute of Computing Technology, Chinese Academy of Sciences, have developed AtomFlow, a flow-matching model that predicts three-dimensional reactant structures from a product structure. It preserves the number and types of atoms throughout generation. Tested on small organic molecules with no more than seven heavy atoms in the Transition1x benchmark, its MatRIS version achieved 96.2% Top-10 accuracy, meaning that a matching reactant was found among the ten highest-ranked candidates in 96.2% of evaluated cases.

Working backwards from a target molecule to possible precursors, known as retrosynthesis, helps chemists plan syntheses for drug discovery and materials research. Many AI approaches represent molecules as SMILES strings and learn to translate products into reactants. These representations do not explicitly encode three-dimensional atomic coordinates, and sequence generation alone does not guarantee conservation of each element’s atom count. AtomFlow addresses this constraint by keeping the atoms fixed in identity while learning how their coordinates change.

Researchers from the Institute of Coal Chemistry, Chinese Academy of Sciences, have identified the nickel dimer (Ni₂) play vital role inCO₂ methanation. Encapsulated in MFI zeolite with MnOₓ promotion at only ~0.86% Ni loading, the catalyst achieves ~76% CO₂ conversion, ~98% CH₄ selectivity and CH₄ space time yield of ~450 mol·mol Ni^-1·h^-1 at 400°C.

China is the world’s largest tea producer, and the tea industry is an important specialty industry for increasing agricultural efficiency and farmers’ income. Frequent tea diseases severely affect yield and quality. Traditional manual diagnosis is inefficient and error-prone, while conventional plant protection operations rely mainly on large-scale pesticide application, resulting in high costs and serious pollution. Current deep learning-based disease detection technologies are easily affected by complex environments in real tea plantations, such as lighting, occlusion, and rainy weather, and their accuracy and stability are insufficient to meet real-time field detection needs, becoming a key bottleneck in the development of smart tea plantations.

Tokyo, Japan – Researchers from Tokyo Metropolitan University have used an evolutionary algorithm to identify optimal shapes for ultra-thin, bio-inspired corrugated airfoils, the two-dimensional cut-throughs of wings. Lightweight membrane-like wings are a strong contender for use in unmanned aerial vehicles (UAVs) in thin atmospheres, like on Mars, and in UAV miniaturization. By using a “zero-thickness” model, they were able to focus on the effect of the airfoil corrugation pattern without the influence of a wing’s leading-edge thickness and shape. They discovered designs which were optimized for minimal drag or maximal lift.

A group of researchers in the Paul M. Rady Department of Mechanical Engineering at CU Boulder have developed a low-cost electronic sensor capable of continuously monitoring soil pH, offering a new window into the hidden world below our feet.