Harvard researchers have developed a 3D printing method that places “active” liquid crystal elastomers and “passive” elastomers side‑by‑side in filaments, effectively pre‑programming the filaments to bend, twist, expand, or contract in specific ways when heated or cooled.

The mysterious origin of an impressive cloud disturbance on Venus has now been revealed by a team including the University of Tokyo. Researchers used numerical models to show that an enormous 6,000-kilometer-wide atmospheric wave front, which circumnavigates the planet for days at a time, is caused by a large “hydraulic jump.” This is when a fluid abruptly slows down, changing from shallow and fast to deep and slow. On Venus, a sudden change in airflow in the lower cloud region is coupled with the creation of a strong updraft, forcing sulfuric acid vapor higher into the atmosphere where it condenses into a massive line of cloud. Future planetary studies can consider the potential impacts of this process, and what it might mean for any exploratory missions.   

 Researchers have developed an integrated strategy combining cathode catalytic H₂-O₂ reaction heating, machine learning, and multi-objective optimization to significantly improve the cold-start performance of proton exchange membrane fuel cells. At -20 °C, the approach achieves a temperature rise exceeding 30 °C without external load, suppresses peak ice volume fraction in the cathode catalyst layer to 3.28 vol%, and ensures post-start stability. Machine learning models accurately predict key cold-start indicators, while SHAP analysis reveals complex nonlinear interactions among operating parameters.

Seoul National University College of Engineering announced that a research team led by Professor Yong-Lae Park from the Department of Mechanical Engineering has developed an “intelligent artificial muscle” capable of simultaneously performing sensing and actuation functions, inspired by biological muscle–tendon complexes.

This artificial muscle, which embeds liquid metal channels within a liquid crystal elastomer (LCE), contracts in response to electrical stimulation while also being able to measure internal force and length in real time. As a result, it enables the simultaneous processing of motor signals (somatic nervous system) and sensory signals (sensory nervous system), similar to biological muscles. The technology is regarded as a breakthrough with strong potential for application in next-generation humanoid robots.

The research findings were published in the prestigious international journal Advanced Materials and were also selected as a cover article.

AI TO EYE: Between Code and Conscience offers a concise and vivid portrait of how artificial intelligence is reshaping contemporary society. In addition to insights drawn from literature and the wider cultural record, 14 short essays and a rich mosaic of voices from more than 40 interviews unite perspectives on this topic from science, industry, journalism, film, music, and the arts. Rather than explaining AI in technical terms, this volume presents a human-centered view of how people across disciplines experience the ongoing debate and apply this powerful technology. From education and media to healthcare and the creative industries, the contributors illuminate both the emerging opportunities and the challenges of an AI-driven world. AI TO EYE blends contemporary conversations with enduring ideas from culture and intellectual history to deliver a polyphonic and highly quotable cultural document that meets AI “eye to eye.”

This paper systematically reviews the research progress and application of machine learning in adsorption processes. By virtue of outstanding nonlinear modeling and data mining capabilities, machine learning has been successfully applied to adsorbent design and high-throughput screening, adsorption parameter prediction and process optimization, reactor design and digital twin simulation, as well as interpretable model development. The reviewed studies demonstrate that machine learning enables highly accurate prediction of adsorption performance, greatly accelerates material discovery and process optimization, and significantly reduces experimental and computational costs. This work also summarizes mainstream machine learning models, public adsorption databases, common simulation methods, and current challenges including insufficient model interpretability, data scarcity, and insufficient coupling with physical mechanisms. Finally, future directions such as deep integration of mechanistic models and AI, intelligent adsorption 2.0, and AI-assisted 3D printing for reactor manufacturing are prospected. This review is expected to provide a comprehensive reference for promoting the intelligent, efficient, and green development of adsorption separation technology.