A synthetic biology technology has been developed that converts easily degraded messenger RNA (mRNA) into a ring-shaped form inside microbial cells, increasing protein production.

Using a highly sensitive two-coil mutual-inductance technique, we systematically track the evolution of the phase stiffness in interfacial superconducting LaAlO₃/KTaO₃(111) and its correlation with the superconducting transition temperature as a function of electrostatic gating. A crossover from Bose–Einstein-condensate-like to Bardeen–Cooper–Schrieffer-like behavior is revealed, suggesting that superconductivity at the two extremes is limited by the establishment of phase coherence and the formation of Cooper pairs, respectively. This behavior is consistent with the emergence of superconducting granularity in two dimensions, which is further supported by the observation of a paramagnetic Meissner effect.

Seawater zinc-air batteries (SZAB) could provide safe, high-energy power for marine applications, but Cl⁻ in seawater can poison the catalysts that drive oxygen reduction reaction (ORR). Researchers from Hainan University and collaborators designed an FeSn dual-single-atom catalysts (FeSn-DSACs) in which Sn neighbors tune Fe active sites through p-d orbital hybridization. The catalyst simultaneously accelerates ORR and suppresses Cl⁻ adsorption and corrosion. In SZAB, it delivered a peak power density of 255.6 mW cm⁻² and operated for more than 500 h, offering a design strategy for durable seawater ORR catalysis.

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.