This comprehensive review establishes the existing methods and effectiveness of lithium ion battery recycling while putting forth a proposal for the future of lithium ion battery recycling. This proposed mechanism will include artificial intelligence powered diagnoses of the cathode materials to determine the failure features of the cathode and the optimal way to recycle the materials within, as well as the facility to sort the recyclable materials within one facility to establish a closed-loop system.

Researchers at Lund University have demonstrated a new strategy for controlling where light is generated in semiconductor nanowire LEDs. Instead of relying on a material that emits light throughout the device, they used an indirect-bandgap core semiconductor nanowire as a pathway to transport electrical charge into nanoscale branches specifically designed for light emission. The work introduces separating charge transport from light generation, opening opportunities for more efficient nanowire-based optoelectronic devices.

Black phosphorus-based micro-supercapacitors (MSCs) are a highly promising micro-scale energy storage technology beyond traditional lithium-ion batteries. They offer significant advantages in flexible wearable electronics, yet suffer from intrinsic instability and sluggish electron kinetics. The two-dimensional phosphorene/bismuthene (BP/Bi) heterojunction, featuring a highly conductive network and anti-stacking architecture constructed via interfacial P-O-Bi covalent coupling, provides a new solution for the development of high-performance, low-temperature-tolerant MSCs and integrated sensing systems.

Nanoplastics can cross biological barriers and may pose risks to the central nervous system, but how deeply they penetrate brain-like tissues and where they induce oxidative stress have remained unclear. Researchers from Wuhan University and Nanjing Medical University developed a flexible electrochemical sensor based on platinum nanoparticle-modified carbon nanotube fiber to monitor hydrogen peroxide, a key oxidative stress marker, within human cortical organoids. The sensor revealed that nanoplastic-induced oxidative stress increased over time but remained mainly localized within the approximately 100-μm-deep region where most particles accumulated.

A Pediatric Investigation study finds that early autism identification can fail after screening, with gaps in referral, diagnosis, service capacity, and support. Reviewing evidence across children aged 0–5 years, researchers show that screening can accelerate diagnosis for some children but cannot replace repeated surveillance or clinical concern. The findings support faster, more coordinated and equitable pathways that help families reach assessment and needs-based support sooner, even before diagnostic certainty is complete.

Segmentation tools help doctors identify and delineate tumors, lesions, and other abnormalities in medical images. However, noise, redundant information, and blurred boundaries can make automated segmentation difficult. Researchers from Tongji University in China and the University of Toyama in Japan developed V-UNet, a new model designed to capture global context and fine local details. Tests on four public datasets demonstrated competitive segmentation performance with relatively low computational requirements.

Traditional MIC-based antibiotic susceptibility testing often misses sub-resistant bacteria, including persistent, tolerant, and heteroresistant populations, which may contribute to treatment failure and resistance evolution. Researchers at Huazhong University of Science and Technology have developed DP-AST, a deep learning-based microfluidic rapid phenotypic AST platform inspired by the Dujiangyan irrigation system. DP-AST measures MIC, bacterial growth activity, and concentration-effect curves to identify hidden sub-resistant bacteria. The study also identified gadE as a key regulator of amikacin sub-resistance in E. coli, offering new insights for resistance mechanism studies and future clinical diagnostics.Traditional MIC-based antibiotic susceptibility testing often misses sub-resistant bacteria, including persistent, tolerant, and heteroresistant populations, which may contribute to treatment failure and resistance evolution. Researchers at Huazhong University of Science and Technology have developed DP-AST, a deep learning-based microfluidic rapid phenotypic AST platform inspired by the Dujiangyan irrigation system. DP-AST measures MIC, bacterial growth activity, and concentration-effect curves to identify hidden sub-resistant bacteria. The study also identified gadE as a key regulator of amikacin sub-resistance in E. coli, offering new insights for resistance mechanism studies and future clinical diagnostics.Traditional MIC-based antibiotic susceptibility testing often misses sub-resistant bacteria, including persistent, tolerant, and heteroresistant populations, which may contribute to treatment failure and resistance evolution. Researchers at Huazhong University of Science and Technology have developed DP-AST, a deep learning-based microfluidic rapid phenotypic AST platform inspired by the Dujiangyan irrigation system. DP-AST measures MIC, bacterial growth activity, and concentration-effect curves to identify hidden sub-resistant bacteria. The study also identified gadE as a key regulator of amikacin sub-resistance in E. coli, offering new insights for resistance mechanism studies and future clinical diagnostics.