Prof. WU Kaifeng's team from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences, has addressed a missing link in understanding proton-coupledelectronic processes: triplet energy transfer coupled with proton transfer. As another key energy transduction mechanism in natural and synthetic systems, triplet energy transfer isfundamentally distinct from singlet energy transfer.

Chinese scientists proposed an anti-interference diffractive deep neural network (AI D²NN) designed for object recognition in multi-object scenarios. Unlike conventional optical neural networks built for single-object classification, AI D²NN achieves robust target recognition while effectively suppressing interference from other objects. By incorporating optical multi-dimensional multiplexing, the system enables flexible, high-capacity parallel recognition of multiple targets. This approach is expected to accelerate the practical deployment of optical neural networks in autonomous driving, medical diagnostics, and security monitoring.

Researchers at the Center for Computational Sciences, University of Tsukuba, have developed an accessible platform to overcome the limitations of conventional static docking simulations, offering new avenues for education, training, and reproducible research in molecular recognition and supramolecular chemistry. Their Distance-Guided Fully Dynamic Docking (DFDD) platform is a cloud-ready simulation framework that enables students and researchers to explore dynamic docking, visualize molecular binding in motion, and understand how host-guest crystal structures emerge from molecular interactions.

Researchers developed high-performance, lead-free piezoelectric thin films on silicon that convert everyday vibrations into electricity. By using strain engineering and a novel sputtering method, they achieved record performance and demonstrated the compatibility of efficient, battery-free MEMS energy harvesters with standard semiconductor manufacturing processes. 

This study investigates the co-pyrolysis behavior and product distribution of peanut straw and polyethylene film blends through thermogravimetric analysis and gas chromatography-mass spectrometry. Thermogravimetric analysis results revealed distinct pyrolysis temperature intervals: 247-356°C for peanut straw, 448-505°C for polyethylene film, and an extended range of 247-510°C for their mixtures. Synergistic effects, quantified through experimental-theoretical deviations, demonstrated enhanced mass conversion rates and accelerated pyrolysis kinetics in blended systems. As the mass ratio of peanut straw to polyethylene increases from 1:1 to 1:7, the bio-oil yield increased from 62.1% to 76.86%, accompanied by elevated alkane from 20.84% to 31.41% and olefin from 24.73% to 42.89%. HZSM-5 catalyst further optimized product profiles, achieving 77.08% bio-oil yield with enhanced hydrocarbon selectivity (alkanes: 35.69%; olefins: 46.16%) while suppressing oxygenates from 20.07% to 8.85%. These findings establish that co-pyrolysis with catalytic intervention effectively promotes hydrocarbon production and inhibits oxygenated compounds, providing strategic insights for agricultural plastic waste valorization.

Optical neural networks (ONNs) offer a pathway toward low-latency, energy-efficient artificial intelligence (AI); however, their scalability in terms of parameter count remains constrained. Addressing this challenge, a research team from The Chinese University of Hong Kong has developed a metasurface-based optical learning machine that integrates 41 million parameters, achieving performance comparable to state-of-the-art AI models. This approach experimentally enables highly scalable machine vision, thereby offering a practical pathway toward large-scale, high-performance optical AI computing.

Catalytically powered micro-/nanomotors have become a compelling alternative to conventional catalysts for active and efficient removal of environmental pollutants in water remediation. We developed a novel biocatalytic nanomotor system by encapsulating catalase and peroxidase enzymes into metal–organic frameworks (MOFs), demonstrating exceptional speed and facilitated motion-induced convection and mass transfer. Leveraging a synergistic structural etching and surface engineering strategy using tannic acid (TA), we create a tailored microenvironment of the MOF’s framework with charge-selective and nanoconfinement properties. Both experimental and simulation results indicate that microenvironment modulation of MOF matrix could act in synergy with the encapsulated enzymes and significantly improve efficiency and selectivity in removing charged pollutants. Surface engineering of TA selectively preconcentrates target contaminants by modulating the MOF shell's surface charge, while etching-induced voids facilitate rapid mass transfer to the enzyme active sites. Finally, we also validated the applicability of these nanomotors in the transformative removal of pollutants from the aqueous phase into polymeric products via an enzyme-mediated polymerization pathway. This biocatalytic nanomotor system provides a promising water remediation paradigm for reducing carbon emissions and recycling chemical energy from emerging contaminants.

Australian scientists have made a significant leap forward in energy storage technology by developing and testing the world’s first proof-of-concept quantum battery, new research reveals. Researchers say the technology could transform how we store and use energy in the future, paving the way for super-fast charging of devices.