Three-dimensional shape sensing is critical for soft robotics, medical navigation, and intelligent perception systems, yet existing high-performance approaches typically rely on multicore fibers or multiple sensing channels. Researchers from Northwest University have developed a deformation-encoding strategy based on femtosecond-laser-inscribed misplaced orthogonal eccentric scatterers, enabling a standard single-mode fiber to reconstruct complex 3D shapes using only a single interrogation channel. The work establishes a new pathway toward miniaturized, high-density, and highly integrated optical fiber sensing systems.

Infrared adaptive optical systems that correct aberrations via deformable mirrors are critical for astronomy, defense, and industry fields. To this end, scientists in China invented a chain‑ring dual‑network chalcogenide glass that combine broadband infrared-transparent with polymer-like flexibility, which overcomes the long-standing material trade-off. Based on this novel material, they fabricated infrared deformable lenses with real‑time aberration correction, opening new pathways for infrared adaptive optical technology.

Tomomichi Itoh and his colleagues at Ehime University have shown how tiny light-induced molecular shape changes can be amplified into visible deformation of polymer microparticles. The team developed light-responsive particles containing azobenzene molecules and revealed how structural changes triggered by light spread through the material, ultimately deforming particles several to tens of micrometers in size. These findings provide new insights for designing light-driven microscale materials and devices.

To build faster and more energy-efficient computers, researchers need to understand nanoscale magnetic materials. But looking inside these tiny magnets is surprisingly difficult: modern microscopes only measure the magnetic field outside the material, not the magnetic pattern inside. Because many different internal patterns can produce the exact same field, working backwards has long been a challenge. Physicists at the University of Vienna, together with colleagues in Dresden, Singapore, and Ulsan, have now solved this problem by building the laws of physics directly into modern machine learning methods. Their method reveals the realistic magnetic structure and even determines the precise distance between the sensor and the sample. The study is published in Physical Review Letters, and the software is freely available as open source.

- DGIST Professor Seong Kyun Kim’s team develops a novel material that retains airborne moisture and evaporates it when heated to provide cooling - Adheres firmly to metal and semiconductor surfaces without inducing corrosion; demonstrates cooling for over 12 hours and temperature reductions of up to 18.2°C - Published in the internationally renowned journal Small Structures

Reducing addiction risk from opioid painkillers

Much of the opioid crisis lies in the central nervous system, as the same brain pathways that mediate profound pain relief also drive tolerance and dependence, which increases the risk of addiction. Penn chemist and pharmacologist Dirk Trauner and collaborators have harnessed the pain-relieving power of opioids by developing a platform that allows activity to be turned “on” or “off” with the flick of a light switch. Their findings pave the way for bypassing opioid addiction and other adverse side effects.

Chemical predictions can help prevent unwanted medication reactions or improve efficacy of industrial processes, among other applications, but predicting how chemicals interact can be costly and time-consuming — especially as how chemicals may react in one environment may behave differently in another. A novel experimental modeling approach may help simplify the process by not only increasing the number of chemical predictions, but also the transferability of such foresight.