Thanks to a recent study by researchers at IOCB Prague, it is now possible to monitor processes in living cells more effectively than before, including responses to drugs and changes in cellular structures. The researchers have developed a new type of fluorescent labels guaranteeing higher-contrast and clearer images via fluorescence microscopy. The discovery is important for medicine and the pharmaceutical industry, among other fields.

A recent study published in Physical Review Letters and carried out by researchers from EHU, the Materials Physics Center, nanoGUNE, and DIPC introduces a groundbreaking approach to solar energy conversion and spintronics. The work tackles a long-standing limitation in the bulk photovoltaic effect—the need for non-centrosymmetric crystals—by demonstrating that even perfectly symmetric materials can generate significant photocurrents through engineered surface electronic states. This discovery opens new pathways for designing efficient light-to-electricity conversion systems and ultrafast spintronic devices.

Osaka Metropolitan University researchers have developed a polymerization technology that enables the synthesis of degradable polymer capsules in aqueous solvents without any initiators or catalysts by irradiating light-reactive monomers derived from natural products.

With the rapid advancement of multi-dimensional detection technologies including hyperspectral imaging and polarization imaging, the concealment efficacy of traditional camouflage has been threatened. To address this challenge, Professor Qiang Li’s team from Zhejiang University has developed a composite hierarchical structure device, which achieves low emissivity, low degree of polarization and simulation of vegetation spectrum for camouflage in all dimensions. This technique will open up ways to counter multimodal imaging detection, enhancing the survivability of equipment.

This study presents a transfer learning–based method for predicting train-induced environmental vibration. The method applies data fusion to combine physics-based numerical simulations and limited measurement data within a neural network. It reduces the heavy reliance of conventional machine learning–based models on scarce and costly field measurements while achieving improved prediction accuracy.

Topological phases are at the heart of many advances in photonics and materials science. In a recent eLight paper, researchers introduced the concept of multi-topological phases, a previously unknown class of topological phases that goes beyond conventional topological theory. Such multi-topological phases can host multiple sets of boundary states, associated with multiple topological invariants, arising from constrained inter-cell coupling in lattice systems. This discovery offers a novel design strategy for future topological materials.

Using advanced first-principles simulations, a team led by Prof. Maryam Ghazisaeidi at The Ohio State University and Prof. Giulia Galli at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) and Chemistry Department demonstrated that nitrogen-vacancy (NV) centers in diamond, a leading solid-state qubit platform, can be attracted to dislocations and retain — and in some cases improve — their quantum properties when positioned near these line defects.