An article in Nature Communications presents a one-step spinning technique to fabricate soft magnetic high-entropy alloy fibres that exhibit enhanced plasticity and low coercivity.

An article in Nature Communications demonstrates a modular synthetic method to create metal–organic polyhedra that maintain porosity during phase transformation.

An article in Science Advances reports the computational design and experimental validation of Al-enriched refractory high-entropy alloys that combine high strength and ductility with reduced cost and good oxidation resistance.

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In a recent Review article, Joseph Wang and colleagues examined how multimodal wearable sensors could potentially be used to transform healthcare1. Multiple clinical settings were highlighted, including applications in neonatal care, sepsis, diabetes and neurological disease. However, the use of multimodal sensors is also fundamental to the future delivery of surgical care. The early postoperative

For TSMC, Geoffrey Yeap and colleagues report a 2 nm logic platform. Based on gate-all-around nanosheet transistors, it features a scalable copper-based redistribution layer and a flat passivation layer, as well as through-silicon vias. Compared with their previous 3 nm technology node, which was reported at the 2022 IEEE International Electron Devices Meeting, this platform offers an increase in chip

Adv. Electron. Mater. 2022, 8, 2101124 DOI: 10.1002/aelm.202101124 There is an error in Figure 2 in the original published version of the article (https://doi.org/10.1002/aelm.202101124). One SEM (x = 0.035) was unintentionally zoomed from another image. Fortunately, none of the other results were affected by this unintentional error. The corrected version of Figure 2 is attached. Original figure:

Jürgen Rödel, Franz Faupel and Stefan Klein discuss how scientific societies can aid in the transition for a more sustainable society.

Alexander Gabourie and colleagues — who are based at DeepSim, Inc., the University of California Davis and Stanford University — now report an artificial intelligence (AI)-accelerated thermal model than can work effectively at multiple length scales, from atomistic- to centimetre-scale circuits. The model first uses molecular dynamics to model thermal transport for atomic structures and interfaces

The researchers — who are based at Université Grenoble Alpes — fabricated a 58 × 60-pixel array in which each 70 μm × 70 μm pixel has its own circuit to measure the incident light phase and modulate it via applying an electric field to the liquid-crystal layer. The pixels thus combine the sensing and modulating functions. The co-integration means the system is compact and its optical alignment is naturally

The researchers — who are based at Zhejiang University and Westlake University — used a 2.5D out-of-plane architecture that integrates graphene monolayers on a gold–silicon columnar metasurface. These structures effectively act as individual photodetection units that allow a wide field-of-view to be achieved, similar to the ommatidia in the compound eye of some animals. The team created a 16 × 16-pixel

The researchers — who are based at Tsinghua University, Beijing InnoMem Technologies Co., Ltd and the Beijing Microelectronics Technology Institute — increased the endurance to 107 cycles through device-, circuit- and operation-level optimization. On the device level, the team introduced an interface modulation layer between the hafnium oxide (HfOx)-based resistive switching layer and oxygen exchange

The rapid rise of artificial intelligence (AI) is revolutionizing industries such as healthcare, robotics, and automation, creating a demand for more efficient, adaptive computing systems. In recognition of AI's profound impact, the 2024 Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton for their pioneering work in machine learning and artificial neural networks.[1-3] This