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.

The 2026 MRS International Risk Conference, themed "AI in an Uncertain World," was successfully held from July 17 to 19, 2026, at the University of Hawaii at Manoa in Honolulu, Hawaii, USA. Hosted by the Shidler College of Business at the University of Hawaii at Manoa, the conference was co-organized by China Finance Review International (CFRI), Modern Risk Society (MRS), and the Dishui Lake Advanced Institute of Finance at Shanghai University of Finance and Economics. Bringing together over 300 scholars and students from around the world, the conference featured 64 parallel sessions with 199 papers presented. More than 15 sessions focused on AI-related topics and 14 on climate finance and climate risk, reflecting the most pressing concerns in today's international financial research. As one of the co-organizers, CFRI actively participated in the event and awarded the CFRI Research Excellence Award. The organizing team also visited the Shidler College of Business to discuss international academic cooperation and journal development.

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.

Molecular perovskite energetic materials (PEMs), featuring ordered ABX₃ crystal frameworks, have emerged as a promising platform for integrating fuel and oxidizing components at the molecular and crystal levels. A review in Energetic Materials Frontiers establishes molecular perovskites as a distinctive platform for the development of multicomponent energetic crystals. Their modular crystal architecture enables fuel and oxidizer components to be organized within ordered lattices, while site substitution, composite modification and data-driven design provide multiple pathways for tuning energy release, thermal stability and safety.

New research reveals that natural soil minerals critically control the fate of lead locked into chloropyromorphite—a stable mineral formed during phosphorus-based lead remediation. Negatively charged clay minerals enhance its mobility, while positively charged iron minerals promote rapid settling, highlighting the need for site-specific risk assessments.

A new study in Macao reveals that stormwater drains carry significant microplastic pollution into coastal waters. Concentrations reached over 53 particles per litre, with hazardous polymers like PVC posing high ecological risks. The research highlights how land use and rainfall drive this pollution pathway in subtropical coastal cities.

Optical neural networks capable of executing large-scale operations in parallel at the speed of light play an ever more prominent role in energy-efficient artificial intelligence. Towards this goal, scientists in China, inspired by the human brain, invented a backpropagation-free framework (B-ONN) for local learning. The technique will open new avenues for deploying physically robust, large-scale optical computing systems in real-world edge devices and green infrastructures.

A research team from the School of Physics at Renmin University of China has developed PhononBench, a large-scale phonon-based benchmark for evaluating the dynamical stability of AI-generated crystal materials. By combining the MatterSim-v1 universal machine-learning interatomic potential with Phonopy, the study enables high-throughput phonon-spectrum calculations for more than 130,000 AI-generated crystal structures. The benchmark systematically evaluates the dynamical stability of crystals generated by different AI models, identifies 32,995 MatterSim-labeled dynamically stable crystal structures under a strict phonon-frequency criterion, and provides a web-based platform for rapid phonon-spectrum calculation and dynamical-stability assessment.

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.