The challenge of photosynthesizing hydrogen peroxide (H₂O₂) from water and oxygen lies in the high O–H bond dissociation energy in water molecules, which leads to sluggish kinetics in hydrogen donor reactions. This study presents a novel catalytic strategy by constructing single-atom palladium sites (Pd–CNO₂) within a keto-form anthraquinone-based covalent organic framework (KfAQ-Pd), effectively promoting water oxidation reactions and achieving efficient H₂O₂ photosynthesis in neutral aqueous environments, with a high yield of 3828 μmol h⁻¹ g⁻¹. Experimental evidence and theoretical simulations reveal that the Pd–CNO₂ sites enhance the hydrophilicity of KfAQ, disrupt the hydrogen-bond network in surface-adsorbed water clusters, and facilitate the cleavage of O–H bonds. The generated H₃O⁺ can be reduced by photoexcited electrons, promoting the anthraquinone hydrogenation reaction and enabling the efficient H₂O₂ synthesis cycle. This study provides a new approach for solar-driven green hydrogen peroxide synthesis under neutral conditions.

Scientists from the City University of Hong Kong have found that nanoplastics can enter zebrafish via two pathways: waterborne exposure and dietary exposure. These tiny particles can cross biological barriers to enter the circulatory system, and then translocate to and accumulate in various organs, including the blood, brain, gills, liver, intestines, gonads, and muscles. The gills and intestines are the most important absorption organs, while the intestines serve as the primary excretion organ.

Strain engineering precisely tunes van der Waals materials' electronic and optical properties for flexible electronics. This review covers uniaxial, biaxial, and localized strain methods, analyzing their effects on band structure, carrier mobility, and phase transitions. Applications include high-performance sensors, transistors, memristors, photodetectors, and LEDs. Future challenges involve scalable material growth, efficient strain transfer, and integration toward intelligent wearable systems.

Researchers have reported initial findings from a public-private partnership between the ECOG-ACRIN Cancer Research Group and Caris Life Sciences to improve recurrence risk assessment in early-stage breast cancer using artificial intelligence (AI). They are pairing ECOG-ACRIN’s extensive clinical trial expertise and biorepository resources with Caris’ comprehensive MI Cancer Seek® whole exome and whole transcriptome profiling, whole slide imaging, and advanced machine learning platforms. New multimodal–multitask deep learning algorithms were trained on histopathologic imaging, clinical data, and molecular profiling data from over 4,000 patient cases in the biorepository of the groundbreaking TAILORx cancer clinical trial, one of the world’s largest such resources. Analyses of these AI-driven models demonstrated they were more effective than existing methods for assessing recurrence risk. This research highlights the potential of AI to support more personalized treatment decisions in early-stage breast cancer. Such a level of multimodal integration is unprecedented at this scale in the prognostication of early breast cancer.