Batteries are part of everyday life, powering everything from phones and laptops to electric cars. Most rechargeable batteries use a liquid to help lithium ions move during charging and discharging. But this liquid can create safety issues and limit how far battery performance can improve.

Solid-state batteries replace the liquid with a solid material. They could lead to safer, more compact and more powerful batteries. But making them work reliably remains a challenge. Unlike in a liquid, lithium ions must move through solid materials and if this movement is uneven, some parts of the battery may charge faster than others, reducing performance.

For the first time, a team at the Institut Laue-Langevin (ILL) and its partners have watched the lithium movement in real time using neutron diffraction, the only technique sensitive enough to track lithium deep inside a thick, working battery.

Their results revealed unexpected structural complexity in the electrode, showing that lithium movement inside the battery was not as uniform as expected. Understanding this hidden behaviour could point the way toward smarter battery designs.

A research team from South China University of Technology has discovered that water molecules within sub-nanometer porous organic nanocages (POCs) act not merely as fillers, but as a critical "switch" regulating material properties. This study reveals how water drives the transition of POCs from a brittle glassy state to a flexible rubbery state, demonstrating immense potential for applications in biomimetic anti-freeze materials and high-performance solid-state lithium-ion battery electrolytes.

A cutting-edge mini-review published in the inaugural issue of Targetome elaborates on the value of proteome-wide dose-response measurements as a powerful strategy to unravel drug mechanisms of action (MoA). This work was jointly completed by a research team led by Prof. Bernhard Kuster from the Technical University of Munich and collaborating scholars from multiple German institutions. The paper constructs a systematic framework for system-level dose-response profiling, addresses key analytical challenges in large-scale omics research, sorts out mainstream experimental workflows, and demonstrates how dose-resolved proteomics can bridge the gap between cellular phenotypes and underlying molecular events. This work advocates replacing conventional single-concentration assays with full dose-response experiments to deliver more actionable insights for modern drug discovery and pharmacological research..

This review systematically examines the pharmacological activity, mechanism of action, and clinical progress of opaganib, offering a novel therapeutic framework for major diseases such as cancer, viral infections, inflammation, and radiation-induced injury.

Researchers have proposed a new rehabilitation design that uses brain signals not to control movement directly, but to decide when and how strongly simple haptic cues should be delivered. Combined with wearable devices based on McKibben artificial muscles, this approach may provide intuitive, low-burden guidance that supports motor learning while reducing overreliance on visual and verbal instructions.

Researchers from Donghua University (Shanghai, China) have developed a novel heterogeneous catalyst, diimine-Ni@PCN-701, by immobilizing diimine-nickel complexes into a zirconium-based metal-organic framework (MOF). This catalyst exhibits exceptional activity for propylene dimerization, achieving a turnover frequency (TOF) of up to 56900 h⁻¹ under mild conditions. It selectively converts propylene into C6 dimers, predominantly 4-methyl-2-pentene (4M2P), with 100% selectivity toward dimers. The enhanced performance arises from the electron-withdrawing effect of the Zr₆O₄(OH)₄ clusters and the steric confinement of the MOF pores, offering a general strategy for designing high-performance heterogeneous olefin oligomerization catalysts.

Researchers from Donghua University (Shanghai, China) have made a theoretical development in relational database normalization by investigating the computational complexity of prime attributes and keys specifically for relation schemes in second normal form (2NF). The study demonstrates that even when a relation scheme is known to be in 2NF, the prime attribute problem remains NP-complete, and the number of all keys can grow exponentially with the number of attributes and functional dependencies. The team also proposes a novel algorithm to find all keys in a 2NF relation scheme, deepening the theoretical understanding of 2NF normalization and providing practical references for database schema design.