A new study  by Prof. XUE Tian from the University of Science and Technology of China (USTC), Prof. YAO Yonggang of the Kunming Institute of Zoology of the Chinese Academy of Sciences (CAS), and Prof. ZHAO Huan of Hefei University has revealed that chronic exposure to artificial light at night (LAN) can trigger depression-like behaviors by activating a specific neural pathway in the brain.

Anode-free all-solid-state batteries (AFASSBs) are potential candidates for next-generation electric mobility devices that offer superior energy density and stability by eliminating Li from the anode. However, despite its potential to stabilize the interface between sulfide solid electrolytes (SEs) and anode-free current collectors (CCs) efficiently, a controllable approach to incorporating MoS₂ into AFASSBs has not yet been found. Herein, we propose a strategy for stabilizing the interface of Li-free all-solid-state batteries using controllable MoS₂ sacrificial thin films. MoS₂ was controllably grown on CCs by metal–organic chemical vapor deposition, and the MoS₂ sacrificial layer in contact with the SEs formed an interlayer composed of Mo metal and Li₂S through a conversion reaction. In the AFASSBs with MoS₂, Mo significantly reduces the nucleation overpotential of Li, which results in uniform Li plating. In addition, MoS₂-based Li₂S facilitates the formation of a uniform and robust SE interface, thereby enhancing the stability of AFASSBs. Based on these advantages, cells fabricated with MoS₂ exhibited better performance as both asymmetrical and full cells with LiNi_0.6Co_0.2Mn_0.2O₂ cathodes than did cells without MoS₂. Moreover, the cell performance was affected by the MoS₂ size, and full cells having an optimal MoS₂ thickness demonstrated a 1.18-fold increase in the initial discharge capacity and a sevenfold improvement in capacity retention relative to SUS CCs. This study offers a promising path for exploiting the full potential of MoS₂ for interface stabilization and efficient AFASSB applications.

What if people could detect cancer and other diseases with the same speed and ease of a pregnancy test or blood glucose meter? Researchers at the Carl R. Woese Institute for Genomic Biology are a step closer to realizing this goal by integrating machine learning-based analysis into point-of-care biosensing technologies. 

The new method, dubbed LOCA-PRAM, was reported in the journal Biosensors and Bioelectronics and improves the accessibility of biomarker detection by eliminating the need for technical experts to perform the image analysis.

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary tool for determining the best options for patients - both in the treatment of cancer and in the prescription of medicines. As recently published in Nature Communications, Jinze Liu, Ph.D., and Kevin Byrd, D.D.S., Ph.D., created Threshold-based Assignment of Cell Types from Multiplexed Imaging Data (TACIT), which assigns cell identities based on cell-marker expression profiles. TACIT cuts down cell identification time from over a month to just minutes—saving researchers valuable time and resources.

UC Riverside engineers highlight wafer-scale accelerators—massive computer chips the size of frisbees that span entire silicon wafers—as a breakthrough in AI computing. Published in Device, their review finds these chips outperform traditional GPUs in speed and energy efficiency, critical as AI models grow. Led by Professor Mihri Ozkan, the team cites Cerebras’ WSE-3 as using one-sixth the power of GPU systems. While costly and best suited for large-scale applications, wafer-scale chips reduce energy use by keeping data local. The paper also stresses sustainable manufacturing, noting most carbon emissions come from production. Efficiency, Ozkan argues, “starts at the factory” to ensure greener AI technologies.