High-capacity lithium-ion anodes often undergo an “activation” process in which capacity initially decreases and then gradually recovers during cycling, delaying full battery performance. Researchers have now shown that this behavior is strongly dependent on particle size. Using a Sn-based model system, the team found that large particles exhibit pronounced activation, whereas small particles show little to no activation. Their results reveal that the difference arises from a stress-induced kinetic retardation during lithiation, providing new guidance for the design of advanced battery materials.

In a newly published research article in Space: Science & Technology, an international team of researchers from the Aerospace Information Research Institute, Chinese Academy of Sciences; the Italian National Institute for Nuclear Physics (INFN); the China Academy of Space Technology; the National Astronomical Observatories, Chinese Academy of Sciences; the Yunnan Observatories, Chinese Academy of Sciences; and the China University of Geosciences jointly presents a detailed account of the successful deployment of the INRRI laser retroreflector on the lunar farside by the Chang’e-6 mission.

The escalating advancements in wireless communication and electronic devices have intensified electromagnetic pollution, driving an urgent demand for high-performance microwave absorption materials (MAMs). The core challenge in designing high-performance MAMs is the simultaneous achievement of excellent impedance matching and a strong attenuation capability. Early research primarily focused on conventional materials such as carbon-based structures and ferrites. Although carbon materials such as graphene and carbon nanotubes offer strong dielectric loss, their high conductivity often leads to poor impedance matching and surface reflection. Furthermore, although ferrites provide magnetic loss, their low Snoek's limit restricts their application at high frequencies.

A research team led by Prof. Jin Ge reports a strategy to markedly enhance iontronic mechanical sensors by tuning two previously underexplored electrical parameters—ionic conductivity and excitation frequency. Through a droplet-based model system combined with impedance spectroscopy and equivalent circuit analysis, the study establishes a quantitative framework linking these parameters to device impedance and capacitance variation. The results reveal that reducing ionic conductivity while increasing excitation frequency significantly boosts sensitivity, achieving up to 8772% improvement without altering device structure. The approach is further validated in hydrogel-based solid-state systems, demonstrating broad applicability. Prototype devices enable real-time pressure sensing, material differentiation in robotic systems, and 2D pressure mapping, offering a new pathway for the rational design and optimization of iontronic sensors.

The integration of liquid metals into conductive hydrogels has long been hindered by particle instability and coalescence. Researchers from Fuzhou University have developed a novel stabilization strategy using gelatin to create a highly uniform, conductive, and stretchable hydrogel. This "electronic skin" not only monitors human motion with high sensitivity but also utilizes deep learning for real-time handwriting recognition and harvests solar energy via photothermal conversion.

Lignin is a sustainable "gold mine" for green chemicals, yet its complex structure remains difficult to crack. Researchers have now developed an ultra-efficient Ru nanocluster catalyst supported on CeO₂ to convert lignin-derived compounds into high-value 1,4-cyclohexanediol in pure water. By leveraging the Strong Metal-Support Interaction (SMSI) to create active interfaces, this green process achieves a record 96.7% yield, paving the way for sustainable plastic and resin production.

Conventional vision systems suffer from severe power and latency bottlenecks due to the physical separation of sensing and processing, which are challenges that in sensor computing seeks to resolve. However, existing neuromorphic hardware remains hindered by high programming energy and unidirectional photocurrent polarity. This work presents an energy efficient reconfigurable photodiode based on ferroelectric Hf_xZr_1-xO₂ (HZO) and ambipolar semiconductor WSe₂, achieving energy-efficient in-sensor computing.

Researchers from Xinjiang University and East China University of Science and Technology have developed a novel iron-based single-atom catalyst (ZnS@Fe-NSC) that overcomes the long-standing trade-off between high activity and long-term stability in electrochemical CO₂ reduction. By integrating sulfur doping and zinc sulfide (ZnS) nanoparticles within the catalyst architecture, the team achieved exceptional CO production efficiency with over 98% selectivity and maintained stable operation for 30 hours, paving the way for more durable and efficient energy conversion technologies.