Plastics are essential to modern life, but their inherent poor mechanical, thermal and electrical properties limit advanced applications. Researchers from the Suzhou Institute of Nano-Tech and Nano-Bionics and partners reported in National Science Review a scalable in-situ CNT-polymer fusion and hot-working strategy. This method breaks the bottleneck of high-loading CNT incorporation into thermoplastic matrices, and develops novel CNT superplastics applicable to thermoplastic processing and 3D printing.

A recent work published in National Science Review report the first realization of a higher-order Weyl exceptional ring semimetal in a 3D lossy acoustic metamaterial. This study systematically analyzes the non-Hermitian topological properties of such metamaterial, and focuses on two types of higher-order topological phenomena: topological hinge states and hinge-dependent skin effect.

The advancement of electronic skins toward commercialization calls for cost-effective and robust flexible conductors, among which metal films are attractive candidates but suffer from poor stretchability. Conventionally, cracks formed in strained metal films are universally considered detrimental and sought to be suppressed. Herein, we demonstrate that cracking is not a drawback, but rather an effective design parameter for on-demand tailoring of film properties. By exploiting the size effect in crack engineering, we propose a bioinspired, dual-scale architecture that endows metal film conductors with up to 325% stretchability. We show that through the synergy of nanoscale pore implantation and microscale substrate roughening, progressively evolved crack patterns can be programmed within metal films by modulating structural parameters. This tunable film cracking behavior yields a nearly 25-fold regulation of stretchability, representing a remarkably wide tunable range among previously deposited metal counterparts. Leveraging these advances, a stretchable metal film-based, AI-empowered electronic skin is constructed, demonstrating excellent practicality across diverse flexible sensing scenarios. Our strategy highlights the pivotal role of the size effect in precise crack manipulation, and the multi-scale regulation paradigm establishes a fresh route to performance improvement of flexible conductors.

The mechanism of high-temperature (T_C) superconductivity is a key challenge in condensed matter physics. Recently, Chinese scientists made significant progress in the study of high-T_C nickelate superconductors.

For the first time, scientists observed a nodeless superconducting gap and discovered electron-boson coupling by measuring the electronic structures of Ruddlesden-Popper bilayer nickelate superconducting thin films. These results provide crucial evidence for two fundamental issues in the mechanism of high-T_C nickelates: “superconducting gap symmetry” and “superconducting pairing mechanism”.

As pets become part of everyday family life, scientists are asking a new question: could the hair shed by cats and dogs help reveal the chemical pollutants circulating inside our homes?

Microplastics are now found across oceans, soils, food chains, and even human biological samples. Yet scientists still face a major challenge: these tiny particles are difficult to detect, track, and link to health risks using traditional laboratory methods alone. A new perspective article argues that artificial intelligence could help change that.

A new international study, published in Science Advances, fully accounts for what is driving the world's rising oceans over six decades.

A new study published in Big Earth Data systematically evaluates popular Uncertainty Quantification (UQ) methods and metrics for AI/ML-based geospatial applications, addressing challenges of predictive uncertainty, trustworthiness, and real-time integration in complex Earth system modelling. Using a PM2.5 calibration case study, it demonstrates that Deep Ensembles and Bayesian Neural Networks implemented in TensorFlow achieved the best reliability and calibration performance, while also highlighting framework-specific differences between TensorFlow and PyTorch in geospatial UQ applications.

A research team from China has developed a high-entropy single-atom material that significantly enhances electromagnetic wave absorption, offering a promising solution to mitigate growing electromagnetic pollution from modern electronics and communication technologies. The innovative material demonstrates exceptional performance, with a minimal reflection loss of -76.9 dB and a broad effective absorption bandwidth of 5.00 GHz.

Growing environmental impacts of fossil fuels drive demand for zero-emission energy technologies (e.g., PEMFCs, CO₂ electroreduction) to advance carbon neutrality—making oxygen reduction reaction (ORR) and CO₂ reduction reaction (CO₂RR) critical research focuses. Biomass-derived carbon offers cost-effectiveness and sustainability vs. precious metal catalysts, but tailoring its hierarchical structure remains challenging. A team led by Yang Haiping at HUST developed a ZIF-8-mediated assembly strategy to synthesize 3D flower-like N-doped biomass carbon, which exhibits Pt/C-comparable ORR activity and high CO₂RR performance, providing insights for customizing biomass carbon for energy applications.