Explore the vast range of titles available.

High-entropy alloys are a class of materials that contain five or more elements in near-equiatomic proportions 1 , 2 . Their unconventional compositions and chemical structures hold promise for achieving unprecedented combinations of mechanical properties 3 – 8 . Rational design of such alloys hinges on an understanding of the composition–structure–property relationships in a near-infinite compositional space 9 , 10 . Here we use atomic-resolution chemical mapping to reveal the element distribution of the widely studied face-centred cubic CrMnFeCoNi Cantor alloy 2 and of a new face-centred cubic alloy, CrFeCoNiPd. In the Cantor alloy, the distribution of the five constituent elements is relatively random and uniform. By contrast, in the CrFeCoNiPd alloy, in which the palladium atoms have a markedly different atomic size and electronegativity from the other elements, the homogeneity decreases considerably; all five elements tend to show greater aggregation, with a wavelength of incipient concentration waves 11 , 12 as small as 1 to 3 nanometres. The resulting nanoscale alternating tensile and compressive strain fields lead to considerable resistance to dislocation glide. In situ transmission electron microscopy during straining experiments reveals massive dislocation cross-slip from the early stage of plastic deformation, resulting in strong dislocation interactions between multiple slip systems. These deformation mechanisms in the CrFeCoNiPd alloy, which differ markedly from those in the Cantor alloy and other face-centred cubic high-entropy alloys, are promoted by pronounced fluctuations in composition and an increase in stacking-fault energy, leading to higher yield strength without compromising strain hardening and tensile ductility. Mapping atomic-scale element distributions opens opportunities for understanding chemical structures and thus providing a basis for tuning composition and atomic configurations to obtain outstanding mechanical properties. In high-entropy alloys, atomic-resolution chemical mapping shows that swapping some of the atoms for larger, more electronegative elements results in atomic-scale modulations that produce higher yield strength, excellent strain hardening and ductility.

Shear-coupled grain boundary (GB) migration is of general significance in the deformation of nanocrystalline and polycrystalline materials, but comprehensive understanding of the migration mechanism at the atomic scale remains largely lacking. Here, we systematically investigate the atomistic migration of Σ11(113) coherent GBs in gold bicrystals using a state-of-art in situ shear testing technique combined with molecular dynamic simulations. We show that shear-coupled GB migration can be realised by the lateral motion of layer-by-layer nucleated GB disconnections, where both single-layer and double-layer disconnections have important contributions to the GB migration through their frequent composition and decomposition. We further demonstrate that the disconnection-mediated GB migration is fully reversible in shear loading cycles. Such disconnection-mediated GB migration should represent a general deformation phenomenon in GBs with different structures in polycrystalline and nanocrystalline materials, where the triple junctions can act as effective nucleation sites of GB disconnections. Shear-induced grain boundary migration at the atomic level is still not well understood. Here the authors combine in situ shear testing experiments and molecular dynamic simulations to reveal the atomistic mechanism of disconnection-mediated GB migration in different gold nanostructures.

Grain boundary (GB) plasticity dominates the mechanical behaviours of nanocrystalline materials. Under mechanical loading, GB configuration and its local deformation geometry change dynamically with the deformation; the dynamic variation of GB deformability, however, remains largely elusive, especially regarding its relation with the frequently-observed GB-associated deformation twins in nanocrystalline materials. Attention here is focused on the GB dynamics in metallic nanocrystals, by means of well-designed in situ nanomechanical testing integrated with molecular dynamics simulations. GBs with low mobility are found to dynamically adjust their configurations and local deformation geometries via crystallographic twinning, which instantly changes the GB dynamics and enhances the GB mobility. This self-adjust twin-assisted GB dynamics is found common in a wide range of face-centred cubic nanocrystalline metals under different deformation conditions. These findings enrich our understanding of GB-mediated plasticity, especially the dynamic behaviour of GBs, and bear practical implication for developing high performance nanocrystalline materials through interface engineering. Grain boundary can change its structure upon deformation. Here, the authors show that during this process, grain boundary mobility can be tuned dynamically via a self-stimulated twinning process.

The sophisticated control of surface wettability for target-specific applications has attracted widespread interest for use in a plethora of applications. Despite the recent advances in modification of non-porous materials, surface wettability control of porous materials, particularly single crystalline, remains undeveloped. Here we contribute a general method to impart amphiphobicity on single-crystalline porous materials as demonstrated by chemically coating the exterior of metal-organic framework (MOF) crystals with an amphiphobic surface. As amphiphobic porous materials, the resultant MOF crystals exhibit both superhydrophobicity and oleophobicity in addition to retaining high crystallinity and intact porosity. The chemical shielding effect resulting from the amphiphobicity of the MOFs is illustrated by their performances in water/organic vapour adsorption, as well as long-term ultrastability under highly humidified CO 2 environments and exceptional chemical stability in acid/base aqueous solutions. Our work thereby pioneers a perspective to protect crystalline porous materials under various chemical environments for numerous applications. The inherent instabilities of metal-organic frameworks (MOFs) in the presence of water or organic compounds have limited their real-world applicability. Here, Ma and co-workers present a coating strategy to fabricate MOFs with amphiphobic surfaces, simultaneously protecting them from moisture and organic vapours.

To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI). A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue. The 93 patients were randomly divided into training and validation groups in 7:3 ratio. The imaging data of patients, including CT and MRI, were collected and processed using 3D Slicer to delineate the region of interest (ROI) for each tumor. Radiomics features were extracted from CT and MRI of patients using Python. Lasso regression analysis was used to select optimal radiomics features for MVI in the training group. The optimal radiomics features of CT and MRI were selected to establish the prediction model. The predictive performance of the model was evaluated using the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). After univariate and multivariate analyses, it was found that tumor diameter was significantly different between the MVI positive and negative groups. After extracting 2153 imaging phenotyping features from the CT and MRI images of the 93 patients using Python, ten standardized coefficient non-zero imaging phenotyping features were finally determined by Lasso regression analysis in the CT and MRI images. A comprehensive predictive model with clinical variable and optimal radiomics features was established. The area under the curve (AUC) of the training group was 0.916 (95%CI: 0.843-1.000), sensitivity: 95.2%, specificity: 79.2%. In the validation group, the predictive model diagnosed MVI with AUC = 0.816 (95%CI: 0.642-0.990), sensitivity: 84.2%, and specificity: 75.0%. The joint model that integrated the optimal radiomics features with clinical variables has good diagnostic performance for MVI of HCC and specific clinical applicability.

Non-alcoholic fatty liver disease (NAFLD) poses a global health challenge. While pyroptosis is implicated in various diseases, its specific involvement in NAFLD remains unclear. Thus, our study aims to elucidate the role and mechanisms of pyroptosis in NAFLD. Utilizing data from the Gene Expression Omnibus (GEO) database, we analyzed the expression levels of pyroptosis-related genes (PRGs) in NAFLD and normal tissues using the R data package. We investigated protein interactions, correlations, and functional enrichment of these genes. Key genes were identified employing multiple machine learning techniques. Immunoinfiltration analyses were conducted to discern differences in immune cell populations between NAFLD patients and controls. Key gene expression was validated using a cell model. Analysis of GEO datasets, comprising 206 NAFLD samples and 10 controls, revealed two key PRGs (TIRAP, and GSDMD). Combining these genes yielded an area under the curve (AUC) of 0.996 for diagnosing NAFLD. In an external dataset, the AUC for the two key genes was 0.825. Nomogram, decision curve, and calibration curve analyses further validated their diagnostic efficacy. These genes were implicated in multiple pathways associated with NAFLD progression. Immunoinfiltration analysis showed significantly lower numbers of various immune cell types in NAFLD patient samples compared to controls. Single sample gene set enrichment analysis (ssGSEA) was employed to assess the immune microenvironment. Finally, the expression of the two key genes was validated in cell NAFLD model using qRT-PCR. We developed a prognostic model for NAFLD based on two PRGs, demonstrating robust predictive efficacy. Our findings enhance the understanding of pyroptosis in NAFLD and suggest potential avenues for therapeutic exploration.

Operators in the main control room of a nuclear power plant have a crucial role in supervising all operations, and any human error can be fatal. By providing operators with information regarding the future trends of plant safety-critical parameters based on their actions, human errors can be detected and prevented in a timely manner. This paper proposed a Sequence-to-Sequence (Seq2Seq)-based Long Short-Term Memory (LSTM) model to predict safety-critical parameters and their future trends. The PCTran was used to extract data for four typical faults and fault levels, and eighty-six parameters were selected as characteristic quantities. The training, validation, and testing sets were collected in a ratio of 13:3:1, and appropriate hyperparameters were used to construct the Seq2Seq neural network. Compared with conventional deep learning models, the results indicated that the proposed model could successfully solve the complex problem of the trend estimation of key system parameters under the influence of operator action factors in multiple abnormal operating conditions. It is believed that the proposed model can help operators reduce the risk of human-caused errors and diagnose potential accidents.

Phosphorus atomic chains, the narrowest nanostructures of black phosphorus （BP）, are highly relevant to the in-depth development of BP-based one-dimensional （1D） nano-electronics components. In this study, we report a top-down route for the preparation of phosphorus atomic chains via electron beam sculpturing inside a transmission electron microscope （TEM）. The growth and dynamics （i.e., rupture and edge migration） of 1D phosphorus chains are experimentally captured for the first time. Furthermore, the dynamic behavior and associated energetics of the as-formed phosphorus chains are further investigated by density functional theory （DFT） calculations. It is hoped that these 1D BP structures will serve as a novel platform and inspire further exploration of the versatile properties of BP.

Bismuth oxide (Bi2O3)-doped yttria-stabilized zirconia (YSZ) were prepared via the solid state reaction method. X-ray diffraction and electron diffraction spectroscopy results indicate that doping with 2 mol% Bi2O3 and adding 10 mol% yttria result in a stable zirconia cubic phase. Adding Bi2O3 as a dopant increases the density of zirconia to above 96%, while reducing its normal sintering temperature by approximately 250 °C. Moreover, electrical impedance analyses show that adding Bi2O3 enhances the conductivity of zirconia, improving its capability as a solid electrolyte for intermediate or even lower temperatures.

BackgroundGamma-delta (γδ) T cells are depleted during cancer progression resulting in the progressive loss of anti-cancer activity. Elevated numbers of γδ T cells are associated with greater survival outcomes in both hematopoietic and solid malignancies. Induced pluripotent stem cell (iPSC) derived γδ T cells could address the therapeutic challenges of multiple allogeneic γδ T cell infusions as iPSCs possess nearly unlimited self-renewal and multi-lineage differentiation potential. These can be genetically modified, selected, and propagated to provide a source of potentially ‘off-the-shelf’ immune cells.MethodsPrecursor cells obtained from healthy volunteer donors were reprogrammed into iPSCs using non-integrating Yamanaka factors. A feeder-free multi-step strategy was used to differentiate iPSCs, leading to the generation of Vδ1+ γδ T cells. Characterization of the Vδ1+ T cell product included multiplex genomic PCR assays and Sanger sequencing to examine the rearrangement of the TCRγ and TCRδ gene loci, and G-band karyotype analysis. Pluripotent markers (Tra-1–60, OCT3/4 & SSEA4), HPC markers (CD34, CD43), γδ T cell surface markers (CD3, γδ TCR, CD4, CD8, CD16, CD56), effector memory markers (CD45RA, CD27), natural cytotoxicity receptors (NKG2D) were identified using multiparameter flow cytometry. T cell function was determined by flow cytometric cytotoxicity assays against K562, OLM13, U87MG, OVSAHO, OVCAR-3, KURAMUCHI targets at increasing Effector to Target (E:T) ratios. Th1/2/17 cytokine release was determined following PMA/ionomycin stimulation and LEGENDplex™ bead-based immunoassays.ResultsWe generated Vδ1T-iPSC lines (iVδ1T) identified as Vγ5-to-Jγ1/2 and Vδ1-to-Jδ1 recombination. One iPSC line showed normal karyotype with 99% cells expressing OCT3/4 & SSEA4. The differentiation process generated 70+ million iVδ1T cells from 3 million iPSCs expressing γδ T cell markers CD45, CD3, Vδ1-TCR, CD16, CD56, NKG2D, CD45RA, and CD27. Cytokine release following PMA/ionomycin stimulation showed increases of at least 50x for Granzyme A, 300x for IFN-γ, 1400x for TNF-α, ~10 to 20x for Granzyme B, ~5 to 10x for Perforin, ~6x for Granulysin. IL-6 was not detected either before or after stimulation, and IL17A was at low concentration. At a 16:1 E:T ratio, preliminary data shows that Vδ1+ γδ T cells killed K562 (CML) 95.7%; MOLM13 (AML) 60.3%; U87MG (glioblastoma) 70.3%; and ovarian cancer lines OVSAHO 57.1%, OVCAR-3 69.6%, and KURAMUCHI 55.1%.ConclusionsWe generated Vδ1+ iPSC derived γδ T cells with effector cytokine phenotype and low risk for cytokine release syndrome. Robust cytotoxic activity was seen across a variety of cancer cell lines, potentially providing an off-the-shelf platform for allogeneic cell therapy.