This discovery advances the decade-long debate on the physics of disorder and opens the way to new applications, from electronics to pharmaceuticals. The research work was carried out by the Department of Physics in collaboration with other European research institutions and published in Physical Review X

In APL Bioengineering, researchers designed a force-sensing miniature robot, called a mobile microgripper, to handle cell spheroids with care. The MMG resembles the gripping part of the claw toy, made of two arms connected by a hinge for a controlled — and gentle — gripping. It’s controlled by magnets, which are biocompatible with spheroids, decreasing the risk of collateral damage, and the gripping force is monitored and adjusted in real time, allowing researchers to adapt to the delicate nature of the cells.

While hearing loss is preventable with earplugs, they can be uncomfortable, and users often remove them despite the risks. In the Journal of the Acoustical Society of America, researchers advance the state of “meta-earplugs” by using Helmholtz resonators to tune sound waves in the ear canal. The earplugs have multiple resonators, each tuned to a different frequency, which helps target a range of low-frequency sounds and relieves acoustic pressure without relying on electronics.

MIT researchers developed a method for making an iron-containing gel that can form soft, magnetically activated structures. The gel could be the basis for microscopic robots and materials that can be controlled by an external magnet, for example to release drugs or grab biopsies within the body.

28 April 2026 / Kiel. How much of the essential trace element iron remains available for marine life in the ocean depends critically on the diversity of organic molecules in seawater. This is shown by new research published in Nature Communications by an international team led by Dr Martha Gledhill from the GEOMAR Helmholtz Centre for Ocean Research Kiel. The study demonstrates for the first time that the formation of iron minerals and the distribution of dissolved and particulate iron in the South Pacific can be realistically predicted when the chemical complexity of organic matter is taken into account. These findings provide an important basis for understanding how marine life may respond to a warmer and more acidic future ocean.

The stress concentration and damage evolution of ultra-high performance concrete (UHPC) under long-term dynamic loading are difficult to monitor in real time, as conventional sensors suffer from poor durability, high cost, and incompatibility with matrix deformation. Recently, a team from the University of Shanghai for Science and Technology developed a machine learning framework that significantly improves dynamic compressive stress prediction in high-sensitivity ultra-high performance concrete (HS-UHPC) by incorporating electrical resistivity as a key input parameter. Using three machine learning algorithms—double-layer neural network, boosting tree, and squared exponential Gaussian process regression (SE-GPR)—the team demonstrated that adding resistivity measurements alongside traditional displacement data enhances predictive accuracy, with the SE-GPR model achieving an R² of 0.85 and reducing mean absolute error by 41.1% compared to displacement-only models. The core innovation is using electrical resistivity to directly capture load‑induced microstructural changes, overcoming the damage‑detection limitations of traditional strain or displacement measurements. This provides a new theoretical basis for intelligent monitoring of self‑sensing concrete.