Angew. Chem. Int. Ed.:Carbon nanotubes not only shine brighter in the presence of dopamine, but also for longer. The time period of shining serves as a new parameter to detect biological messenger substances.

Science: RESOLV member Prof. E. Narevicius' work presents the detection of Feshbach resonances in a benchmark system for strongly interacting and highly anisotropic collisions.

Chem. Sci: Using a new technique, researchers were able to observe live what happens in the first picosecond when a proton detaches from a dye after exposure to light.

Mater. Horiz.: RESOLV Prof. Kai S. Exner from the Department of Theoretical Inorganic Chemistry at the UDE has now been able to show that previous modeling was based on mechanisms that were too simple and therefore did not provide reliable results. Producing hydrogen from water and solar energy alone - researchers around the world are working on this sustainable path. Theoreticians support this…

Nuc. Aci. Res.: Revealing the critical aspect of defined motional features for affinity and specificity in the DNA/protein interaction, providing unexpected prospects for further design progress in this novel area of DNA recognition.

Phys. Rev. Lett.: An unexpected "inside-out" solvation pattern was observed by studying Cs+ hydration on a Cu(111) surface. Using a combination of experimental and theoretical methods they found Cs+ ions favoring the edges of water clusters on the metal surface and the formation of water-Cs complexes with multiple Cs+ ions.

PNAS: A new experiment has made it possible to observe the effects of an electron in solution on the surrounding liquid.

J. Am. Chem. Soc. : Protein dysfunctions contribute to diseases by undergoing two key processes: forming liquid droplets (LLPS) and creating solid clumps called amyloid fibrils.

J. Phys. Chem. Lett.: RESOLV main spokeperson, Prof. Havenith showed that terahertz spectroscopy maps spontaneous formation of protein-rich droplets that may lead to neurodegenerative diseases.

Fluid Phase Equilibr.: RESOLV PI Prof. G. Sadowski's work introduces a Machine learning (ML) approach using extended-connectivity fingerprints (ECFPs) that can accurately predict PC-SAFT pure-component parameters