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Elastic stability is the basis for understanding structural responses to external stimuli in crystalline solids, including melting, incipient plasticity and fracture. In this work, elastic stability is investigated in a series of high-entropy alloys (HEAs) using in situ mechanical tests and atomic-resolution characterization in transmission electron microscopy. Under tensile loading, the HEA lattices are observed to undergo a sudden loss of ordering as the elastic strain reached ∽ 10%. Such elastic strain-induced amorphization stands in intrinsic contrast to previously reported dislocation-mediated elastic instability and defect accumulation-mediated amorphization, introducing a form of elastic instability. Together with the first principle calculations and atomic-resolution chemical mapping, we identify that the elastic strain-induced amorphization is closely related to the depressed dislocation nucleation due to the local atomic environment inhomogeneity of HEAs. Our findings provide insights for the understanding of the fundamental nature of physical mechanical phenomena like elastic instability and incipient plasticity. Lattice stability related to the structural response is the basis for understanding mechanical and physical behavior of crystalline solids. Here, the authors show a manifestation of elastic instability in high-entropy alloys via elastic strain induced amorphization.

Tribology, which is the study of friction, wear, and lubrication, largely deals with the service performance of structural materials. For example, newly emerging high-entropy alloys (HEAs), which exhibit excellent hardness, anti-oxidation, anti-softening ability, and other properties, enrich the wear-resistance alloy family. To demonstrate the tribological behavior of HEAs systematically, this review first describes the basic tribological characteristics of single-, dual-, and multi-phase HEAs and HEA composites at room temperature. Then, it summarizes the strategies that improve the tribological property of HEAs. This review also discusses the tribological performance at elevated temperatures and provides a brief perspective on the future development of HEAs for tribological applications.

With the rapid development of artificial intelligence technology and increasing material data, machine learning- and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science. Machine learning methods, based on an interdisciplinary discipline between computer science, statistics and material science, are good at discovering correlations between numerous data points. Compared with the traditional physical modeling method in material science, the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials. This review starts with data preprocessing and the introduction of different machine learning models, including algorithm selection and model evaluation. Then, some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition, structure, processing, and performance. The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed. Finally, the applicability and limitations of machine learning in the material field are summarized, and future directions and prospects are discussed.

Leukemia is a kind of hematological malignancy originating from bone marrow, which provides essential signals for initiation, progression, and recurrence of leukemia. However, how to specifically deliver drugs to the bone marrow remains elusive. Here, we develop biomimetic vesicles by infusing hematopoietic stem and progenitor cell (HSPC) membrane with liposomes (HSPC liposomes), which migrate to the bone marrow of leukemic mice via hyaluronic acid-CD44 axis. Moreover, the biomimetic vesicles exhibit superior binding affinity to leukemia cells through intercellular cell adhesion molecule-1 (ICAM-1)/integrin β2 (ITGB2) interaction. Further experiments validate that the vesicles carrying chemotherapy drug cytarabine (Ara-C@HSPC-Lipo) markedly inhibit proliferation, induce apoptosis and differentiation of leukemia cells, and decrease number of leukemia stem cells. Mechanically, RNA-seq reveals that Ara-C@HSPC-Lipo treatment induces apoptosis and differentiation and inhibits the oncogenic pathways. Finally, we verify that HSPC liposomes are safe in mice. This study provides a method for targeting bone marrow and treating leukemia. Effective delivery of drugs to bone marrow has potential for leukemia treatment. Here the authors report the delivery of chemotherapy drug Ara-C with HSPC cell membrane derived-biomimetic vesicles, which target leukemia stem cells thereby effectively inhibit its progression.

Stoichiometric homeostasis, the degree to which an organism maintains its C:N:P ratios around a given species- or stage-specific value despite variation in the relative availabilities of elements in its resource suplies, is a key parameter in ecological stoichiometry. Howerver, its regulation and role in affecting organismal and ecosystem processes is still poorly understood in vascular plants. We performed a sand culture experiment and a field nitrogen (N) and phosphorus (P) addition experiment to evaluate the strength of N, P and N:P homeostasis in higher plants in the Inner Mongolia grassland. Our results showed that homeostatic regulation coefficients (H) of vascular plants ranged from 1.93 to 14.5. H varied according to plant species, aboveground and belowground compartments, plant developmental stage, and overall plant nutrient content and N:P ratio. H for belowground and for foliage were inversely related, while H increased with plant developmental stage. H for N (H N ) was consistently greater than H for P (H P ) while H for N:P (H N:P ) was consistently greater than H N and H P . Furthermore, species with greater N and P contents and lower N:P were less homeostatic, suggesting that more homeostatic plants are more conservative nutrient users. The results demonstrate that H of plants encompasses a considerable range but is stronger than that of algae and fungi and weaker than that of animals. This is the first comprehensive evaluation of factors influencing stoichiometric homeostasis in vascular plants.

Electrofusion is a widely used technique for inducing membrane merging in biological systems, with applications ranging from hybrid lipid architectures to therapeutic delivery. However, the direct application of conventional electrofusion to advanced nanoscale drug carriers, such as cell membrane hybrid LNPs (cLNPs), faces challenges due to diminished dielectric response and uncontrolled particle dynamics. To address these limitations, we report a periodic asymmetric field (PAF) strategy that combines microfluidic flow with periodic electric fields to enhance nanoscale electrofusion. We systemically investigate the underlying mechanisms using computational fluid dynamics simulations, and subsequently fabricate and optimize a PAF-guided microfluidic electrofusion device (PAF-MED) for the controlled synthesis of cLNPs. These PAF-MED-synthesized cLNPs demonstrate improved fusion efficiency, augmented targeting capability, and superior therapeutic efficacy in bleomycin-induced pulmonary fibrosis murine models. This approach represents a unique advancement in the nanoscale manipulation of drug carriers towards better bio-functionality and reproducibility beyond conventional capability of electrofusion. Electrofusion enables membrane fusion but struggles with lipid nanoparticles. Here, the authors present a nanofabrication platform that combines microfluidics with microelectrode arrays, achieving efficient, controlled nanoscale electrofusion of LNPs for effective siRNA delivery

There is evidence to suggest that the primary tumor induces the formation of a pre-metastatic niche in distal organs by stimulating the production of pro-metastatic factors. Given the fundamental role of the pre-metastatic niche in the development of metastases, interruption of its formation would be a promising strategy to take early action against tumor metastasis. Here we report an enzyme-activated assembled peptide FR17 that can serve as a “flame-retarding blanket” in the pre-metastatic niche specifically to extinguish the “fire” of tumor-supportive microenvironment adaption. We show that the in-situ assembled peptide nano-blanket inhibits fibroblasts activation, suppressing the remodeling of the metastasis-supportive host stromal tissue, and reversing vascular destabilization and angiogenesis. Furthermore, we demonstrate that the nano-blanket prevents the recruitment of myeloid cells to the pre-metastatic niche, regulating the immune-suppressive microenvironment. We show that FR17 administration effectively inhibits the formation of the pulmonary pre-metastatic niche and postoperative metastasis, offering a therapeutic strategy against pre-metastatic niche formation. Primary tumors “spread the spark” by establishing a pre-metastatic niche. Here the authors develop an in-situ assembled peptide FR17 to serve as a “flame-retarding blanket” to extinguish the “fire” of the pre-metastatic microenvironment.

Martensite is an important microstructure in ultrahigh-strength steels, and enhancing the strength of martensitic steels often involves the introduction of precipitated phases within the martensitic matrix. Despite considerable research efforts devoted to this area, a systematic summary of these advancements is lacking. This review focuses on the precipitates prevalent in ultrahigh-strength martensitic steel, primarily carbides (e.g., MC, M 2 C, and M 3 C) and intermetallic compounds (e.g., NiAl, Ni 3 X, and Fe 2 Mo). The precipitation-strengthening effect of these precipitates on ultrahigh-strength martensitic steel is discussed from the aspects of heat treatment processes, microstructure of precipitate-strengthened martensite matrix, and mechanical performance. Finally, a perspective on the development of precipitation-strengthened martensitic steel is presented to contribute to the advancement of ultrahigh-strength martensitic steel. This review highlights significant findings, ongoing challenges, and opportunities in the development of ultrahigh-strength martensitic steel.

Grazing cuts N 2 O emission Levels of the greenhouse gas nitrous oxide have increased since pre-industrial times, mainly because of agricultural activities. Among these changes it has been reported that livestock grazing substantially increases nitrous oxide emissions from temperate grasslands. New data obtained from year-round monitoring at ten steppe grassland sites in Inner Mongolia, China, challenge this view by highlighting a previously overlooked interaction. The measurements made using the automatic chamber system show that nitrous oxide release is dominated by pulses during springtime thawing, is highest in ungrazed steppe and decreases with increasing stocking rate. So surprisingly, grazing decreases rather than increases nitrous oxide emissions by changing the soil water balance and microbial activity. To examine the effect of increased livestock numbers on nitrous oxide emissions the authors report year-round nitrous oxide flux measurements at ten steppe grassland sites in Inner Mongolia. They find that nitrous oxide emission is much higher during spring thaw and is highest in ungrazed steppe, decreasing with increasing stocking rate, which suggests that grazing decreases rather than increases nitrous oxide emissions. Atmospheric concentrations of the greenhouse gas nitrous oxide (N 2 O) have increased significantly since pre-industrial times owing to anthropogenic perturbation of the global nitrogen cycle 1 , 2 , with animal production being one of the main contributors 3 . Grasslands cover about 20 per cent of the temperate land surface of the Earth and are widely used as pasture. It has been suggested that high animal stocking rates and the resulting elevated nitrogen input increase N 2 O emissions 4 , 5 , 6 , 7 . Internationally agreed methods to upscale the effect of increased livestock numbers on N 2 O emissions are based directly on per capita nitrogen inputs 8 . However, measurements of grassland N 2 O fluxes are often performed over short time periods 9 , with low time resolution and mostly during the growing season. In consequence, our understanding of the daily and seasonal dynamics of grassland N 2 O fluxes remains limited. Here we report year-round N 2 O flux measurements with high and low temporal resolution at ten steppe grassland sites in Inner Mongolia, China. We show that short-lived pulses of N 2 O emission during spring thaw dominate the annual N 2 O budget at our study sites. The N 2 O emission pulses are highest in ungrazed steppe and decrease with increasing stocking rate, suggesting that grazing decreases rather than increases N 2 O emissions. Our results show that the stimulatory effect of higher stocking rates on nitrogen cycling 4 , 7 and, hence, on N 2 O emission is more than offset by the effects of a parallel reduction in microbial biomass, inorganic nitrogen production and wintertime water retention. By neglecting these freeze–thaw interactions, existing approaches may have systematically overestimated N 2 O emissions over the last century for semi-arid, cool temperate grasslands by up to 72 per cent.

Solidification structure is a key aspect for understanding the mechanical performance of metal alloys, wherein composition and casting parameters considerably influence solidification and determine the unique microstructure of the alloys. By following the principle of free energy minimization, the phase-field method eliminates the need for tracking the solid/liquid phase interface and has greatly accelerated the research and development efforts geared toward optimizing metal solidification microstructures. The recent progress in the application of phase-field simulation to investigate the effect of alloy composition and casting process parameters on the solidification structure of metals is summarized in this review. The effects of several typical elements and process parameters, including carbon, boron, silicon, cooling rate, pulling speed, scanning speed, anisotropy, and gravity, on the solidification structure are discussed. The present work also addresses the future prospects of phase-field simulation and aims to facilitate the widespread applications of phase-field approaches in the simulation of microstructures during solidification.