Researchers have developed a bifunctional catalyst system combining ZnZrOₓ (ZZO) with silanol nest‑enriched silicalite‑1 (AS‑1) for CO₂ hydrogenation coupled with toluene alkylation. The moderate acidity of AS‑1 significantly suppresses side reactions, reducing benzene selectivity from 6.04 % (using conventional ZSM‑5) to 0.5 %, while increasing para‑xylene (PX) selectivity from 23.6 % to 33 %. Epitaxial silicalite‑1 shell coating further enhances shape selectivity, boosting PX selectivity to 44.4 %. Optimizing the spatial distribution of ZZO and AS‑1 increases toluene conversion to 16.3 %. The integrated ZZO/AS‑1@S‑1 catalyst achieves a CO₂ conversion of 9.5 %, toluene conversion of 15 %, and PX selectivity of 57.5 % under optimized conditions.

Researchers have developed a multifunctional flame‑retardant strategy for epoxy resin by incorporating polyphosphazene‑decorated hierarchical nickel phyllosilicate (NiPS@PZN) as a reinforcing and wear‑resistant filler, together with 9,10‑dihydro‑9‑oxa‑10‑phosphaphenanthrene‑10‑oxide (DOPO) as a flame‑retardant synergist. At a total loading of 6 % (1:1 mass ratio), the composite achieves a UL‑94 V‑0 rating and a limited oxygen index of 26.6 %, while total heat release, total smoke production, and CO₂ production are reduced by 10.4 %, 23.2 %, and 26.8 %, respectively. The tensile strength increases to 103.5 MPa and the minimum wear rate reaches 1.78 × 10⁻⁵ mm³·N⁻¹·m⁻¹, demonstrating a breakthrough in overcoming the traditional trade‑off between flame retardancy and mechanical/tribological properties.

Existing electrochemical biosensing systems generally rely on external computing units for data analysis. The necessity of analog-to-digital conversion and data transfer leads to high complexity, low working efficiency and concern of privacy. Zhao et al. realize the in-sensor computing in electrochemical biosensing for the first time, which uses a single neuromorphic organic photoelectrochemical transistor to simultaneously perform multi-target biosensing and artificial neural network classification. The strategy permits in-situ training using real-time data, helping solving the issue of device-to device variations. The proof-of-concept application is demonstrated by direct classification of microRNA fingerprints in real biological samples, which provides a new paradigm for simpler, faster and more secure AI-driven electrochemical biosensing.

Researchers have developed BattFailScholar, a knowledge-augmented large language model framework that dramatically improves battery failure diagnosis. By equipping AI with a structured "case library" of over 11,000 battery failure examples, the system boosts diagnostic accuracy by 19.7% over standard LLMs, effectively handles rare failure types, and provides reliable risk assessments with confidence scores. Beyond diagnosis, it can infer missing links in failure chains with over 85% accuracy, offering a new tool for safer battery development.

With artificial intelligence (AI) workloads driving rising demands for computing power, bandwidth, and energy efficiency, integrated photonic neural networks (PNNs) offer a promising route beyond electronic bottlenecks. This review surveys emerging PNN technologies from devices and computing architectures to integration, packaging, and systems, highlighting optoelectronic co-design, key challenges, and their potential as next-generation AI accelerators.

Researchers developed DexAnyTwist, a learning framework that enables dexterous robotic hands to twist many everyday objects, from bottles and valves to screwdrivers and light bulbs. By identifying different modes and assigning expert policies to them, the system improved simulation performance and transferred zero-shot to real-world objects.

Researchers have updated the classic nonsolvent induced phase separation (NIPS) theory. By optimizing a fluid observation cell, the team decoupled the formation of macrovoids and cellular pores and revealed their distinct formation mechanisms. A new thermodynamic parameter was proposed for accurate pore prediction. The developed ultrathin membrane delivered outstanding performance in vanadium flow batteries, outperforming commercial counterparts.

 Researchers have developed a CoOₓ-modified Pt/Al₂O₃ catalyst that achieves outstanding performance in the dehydrogenation of perhydro-dibenzyltoluene (H₁₈-DBT), a promising liquid organic hydrogen carrier. The optimal catalyst with 1 wt% CoOₓ loading delivers a dehydrogenation degree of 93% and a hydrogen production rate of 20.98 × 10³ mol H₂·mol Pt⁻¹·h⁻¹ at 300 °C, outperforming all previously reported catalysts. However, increasing Co loading to 10 wt% causes a sharp decline in dehydrogenation degree to only 7.2%. The study reveals that amorphous CoO clusters enhance activity by increasing Pt electron density and reducing Pt particle size.