High-voltage cycling can quietly degrade LiCoO₂ cathodes, but a new low-dose nanoscale imaging method reveals the precise crystal transformations driving battery performance loss. By combining cepstral matching analysis with scanning nanobeam electron diffraction, researchers achieved nanometer-scale visualization with minimal beam damage. The study uncovered local structural changes at the cathode interface with the electrolyte, providing important clues to how batteries degrade and helping guide the design of longer-lasting, higher-energy lithium-ion batteries for future devices and electric vehicles.

Microbial proteins are attracting attention for their potential in biomanufacturing a variety of products, including pharmaceuticals, industrial enzymes, and diagnostic antibodies. These proteins can also be used for converting resources into biofuels and bioplastics. Producing proteins from Escherichia coli (E. coli) has become popular due to its cost-effectiveness and efficiency. However, yields of protein production in E. coli may be reduced depending on the specific gene sequence of the target protein. A research group in Japan has successfully developed a new technology that improves the efficiency of protein production in E. coli. Their findings could provide fundamental technology to support sustainable manufacturing that does not depend on petroleum.

High-entropy oxides (HEOs) have emerged as a promising class of memristive materials, characterized by entropy-stabilized crystal structures, multivalent cation coordination, and tunable defect landscapes. These intrinsic features enable forming-free resistive switching, multilevel conductance modulation, and synaptic plasticity, making HEOs attractive for neuromorphic computing. This review outlines recent progress in HEO-based memristors across materials engineering, switching mechanisms, and synaptic emulation. Particular attention is given to vacancy migration, phase transitions, and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems. Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined. While encouraging results have been achieved at the device level, challenges remain in conductance precision, variability control, and scalable integration. Addressing these demands a concerted effort across materials design, interface optimization, and task-aware modeling. With such integration, HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.

Most japonica rice varieties possess similar alleles for grain size, producing short and wide grains.

The gs9^ko allele has a widely applicable effect on grain appearance improvement. The increased leaf angle of the gs9^ko allele does not affect its improvement effect.

A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness. However, a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations, as well as mechanical motion artifacts. Herein, a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode, featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness, thereby realizing the long-term breath-induced pressure detection. Notably, molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration, driven by shear forces and interfacial interactions, which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer. The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation (0–120°, ~ 2.8 × 10^–3 A) and sensing accuracy without crosstalk (humidity 50%–100% and temperature 30–80 °C). Besides, the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages (average prediction accuracy ~ 90%). It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.

This paper expounds on the development status and relevant works of control and guidance methods of the aerospace vehicle in recent years. The control difficulties and the solutions in the related results are introduced briefly. Moreover, the guidance methods are then expounded in detail according to the flight phases of the whole flight mission. Guidance methods are usually included in each phase, and the corresponding trajectory design theories are also introduced where necessary. In addition, the potential future development direction prospects. Based on the above, a brief conclusion is then made as a summary.