A team from the College of Chemistry and Chemical Engineering of Shaanxi University of Science and Technology and Functional Inorganic Materials Energy Conversion Laboratory started from “yarn” as a continuously processable and weave-integrable material unit. They designed a dynamic electrostatic cladding-spinning strategy that integrates a high-strength mechanical skeleton, a photothermal conversion shell, and thermoelectric waste-heat recovery into a single yarn platform.

From drug synthesis planning and production scheduling to facility location problem, combinatorial optimization (CO) is a backbone of efficient operation in science, engineering, and society. Yet building effective solvers still often requires expert modeling and extensive manual tuning. A new review surveys rapid progress in using large language models (LLMs) across three major paradigms — LLM-assisted modeling and solver collaboration, LLM-assisted heuristics, and LLM-based end-to-end solvers — and analyze their workflows, strengths, and limitations. The paper also summarizes application areas such as planning and scheduling, wireless networks and engineering design, and outlines research directions for making LLM-assisted CO more reliable.

Current clinical anti-obesity regimens mainly utilize GLP-1 or PPAR-γ agonists, which are restricted by post-withdrawal weight rebound and inflamed adipose microenvironments respectively. This study fabricates adipocyte-targeted nanocomposite microneedles (LRSGSG@MN) co-delivering rosiglitazone and semaglutide for immuno-metabolic therapy. Lipid carriers deliver rosiglitazone to activate PPAR-γ and mitochondrial biogenesis, while sustained semaglutide polarizes macrophages toward anti-inflammatory M2 to alleviate adipose inflammation, disinhibiting thermogenesis and reinforcing white fat browning. In high-fat-diet obese mice, the patch alleviates adiposity and glucolipid disorders and maintains weight steadily after treatment withdrawal. This work reveals peripheral immunomodulatory effects of semaglutide and establishes a long-acting adipose-targeted microneedle platform for durable obesity intervention.

Researchers in Lin Quan's research group at Jilin University have developed an injectable MXene-based hydrogel specifically for treating periodontitis. This hydrogel, named GQM, consists of oxidized gellan gum, quaternized chitosan, and magnesium-tannic acid-modified MXene nanosheets. It can be injected into periodontal pockets, overcoming the problem of traditional drugs' inability to remain within these pockets. Under near-infrared light irradiation, it can gently disrupt bacterial biofilms through photothermal activity, while the electrostatic effect of the QCS (quaternary cytosolic crystals) helps kill periodontal pathogens. Furthermore, the nanosheets promote electron transport, restore mitochondrial NAD+/NADH balance, and induce macrophages to transform into the M2 type. In a rat model of periodontitis, GQM combined with near-infrared light therapy reduced bacterial count, inflammatory response, and alveolar bone resorption. The MXene-based hydrogels treat periodontitis by disrupting bacterial biofilms and reprogramming immune metabolism, offering a novel strategy for its management.

To overcome the fragility of soft sensing materials under long-term cyclic loading, researchers have developed a highly durable "eutectogel" utilizing a novel nanoscale cavitation mechanism. By integrating this fatigue-resistant material into a smart glove with deep learning algorithms, the team achieved a 99.9% accuracy rate in real-time sign language recognition. This innovation paves the way for seamless communication and advances the durability of next-generation human-machine interfaces.