The electrocatalytic CO₂ reduction reaction (CO₂RR) serves as an effective approach to convert CO₂ into high-value chemicals and facilitate carbon cycling. Among various products, ethylene (C₂H₄), a crucial industrial feedstock, demonstrates substantial market demand and economic significance. Copper (Cu)-based catalysts exhibit remarkable advantages in CO₂RR to C₂H₄ conversion due to their unique electronic structure and optimal *CO adsorption capacity.

Concurrently, the membrane electrode assembly (MEA) design featuring an electrolyte-free cathode effectively addresses mass transfer limitations, minimizes ohmic losses, and enhances interfacial efficiency, thereby significantly boosting current density and product selectivity. The integration of Cu-based catalysts with MEA technology thus emerges as a highly promising solution for industrial-scale CO₂RR to C₂H₄ production.

In a recent pioneering study, researchers at Insilico Medicine ("Insilico"), a generative artificial intelligence (AI)-driven clinical-stage biotechnology company, harnessed its AI-driven generative chemistry platform, Chemistry42, to design a novel, first-in-class PROTAC targeting PKMYT1, D16-M1P2, which employs a dual mechanism of action—inducing PKMYT1 degradation while directly inhibiting its kinase activity. This innovative strategy holds promise for overcoming limitations of existing inhibitors, such as poor selectivity and acquired resistance, and to effectively target both the catalytic and non-catalytic functions of PKMYT1. As a result, the PROTAC demonstrates the potential for a more selective, potent, and durable therapeutic effect.

An international research team led by RMIT University have created tiny particles, known as nanodots, made from a metallic compound that can kill cancer cells while leaving healthy cells largely unharmed.

While this work is still at the cell-culture stage – it hasn’t been tested in animals or people – it points to a new strategy for designing cancer treatments that exploit cancer’s own weaknesses.

How far is it really from Hamburg to Rome – by ship, train, or truck? A research team including KLU Professor Arne Heinold has found a way to answer that question within milliseconds using open data. Their work was awarded the 2025 Prize for Open Data from the renowned American research institute, the Massachusetts Institute of Technology (MIT).

Quantum computers will be able to assume highly complex tasks in the future. With superconducting quantum processors, however, it has thus far been difficult to read out experimental results because measurements can cause interfering quantum state transitions. Researchers at Karlsruhe Institute of Technology (KIT) and Université de Sherbrooke in Québec have performed experiments that improve our understanding of these processes and have shown that calibrating the charge at the qubits contributes to fault avoidance. Their findings have been published in Physical Review Letters. (DOI: 10.1103/yljv-b4kj[SW1)

Using catalytic chemistry, researchers at Institute of Science Tokyo have achieved dynamic control of artificial membranes, enabling life-like membrane behavior. By employing an artificial metalloenzyme that performs a ring-closing metathesis reaction, the team induced the disappearance of phase-separated domains as well as membrane division in artificial membranes, imitating the dynamic behavior of natural biological membranes. This transformative research marks a milestone in synthetic cell technologies, paving the way for innovative therapeutic breakthroughs.

AI and human-movement research intersect in a study that enables precise estimation of hand muscle activity from standard video recordings. Using a deep-learning framework trained on a large, comprehensive multimodal dataset from professional pianists, the researchers introduce a system that accurately reconstructs muscle activation patterns without sensors. This advancement provides a low-cost, non-invasive method for analyzing fine motor control, optimizing rehabilitation strategies, enhancing performance training, and informing future developments in human-machine interaction.