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Null matrix alloys are special types of alloys with no peaks in the neutron diffraction patterns. These types of materials have tremendous applications in the neutron scattering community as main components for manufacturing advanced reaction vessels for measurements. In the literature, null matrix alloys are often reported to be composed of two different elements/isotopes. In other words, null matrix alloys with more than two elements/isotopes have never been reported. One main reason was the limited solubility of dopants in the parent phases. As such, their physical/chemical properties could not be easily modified through the conventional doping strategies. For the first time, a new family of high-entropy V-based (HEV) alloys, composed of more than five elements, were developed using a two-step arc-melting method. The six investigated samples included VNbTaAlFeSn (HEV1), VNbTaAlFeMo (HEV2), VNbTaAlSnMo (HEV3), VNbTaFeSnMo (HEV4), VNbAlFeSnMo (HEV5), and VTaAlFeSnMo (HEV6). All six HEV alloys did not show any diffraction peaks in the neutron diffraction patterns. The dopants were found to be homogeneously distributed over the 2a sites of the 3̅ structure of V by the Rietveld refinement on high-resolution X-ray diffraction data. X-ray pair distribution function (PDF) analysis and small-angle neutron scattering (SANS) ruled out the possibility of chemical ordering or clustering in the investigated samples. Based on the chemical oxidation analysis of O at high temperatures, the HEV alloys are found to be more resistant than that the binary V-based alloys. Among the investigated HEV alloys, HEV4 was found to have the highest engineering yield strength. This work provides an important guideline of designing complex null matrix alloys/materials with novel properties.
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http://dx.doi.org/10.1021/acsami.5c03487 | DOI Listing |
Parasit Vectors
August 2025
Laboratório de Ultraestrutura Celular Hertha Meyer, Centro de Pesquisa em Medicina de Precisão (CPMP), Instituto de Biofísica Carlos Chagas Filho, Universidade Federal Do Rio de Janeiro, Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-590, Brazil.
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View Article and Find Full Text PDFJ Am Stat Assoc
March 2025
Department of Statistics, The Pennsylvania State University, USA.
This paper proposes an innovative double power-enhanced testing procedure for inference on high-dimensional linear hypotheses in high-dimensional regression models. Through a projection approach that aims to separate useful inferential information from the nuisance one, our proposed test accurately accounts for the impact of high-dimensional nuisance parameters. We discover that with a carefully-designed projection matrix, the projection procedure enables us to transform the problem of interest into a test on moment conditions, from which we construct a -statistic-based test that is applicable in simultaneous inference on a diverging number of linear hypotheses.
View Article and Find Full Text PDFRett syndrome (RTT), caused by mutations in , is a complex neurological disorder characterized by myriad physiological disruptions, including early closure of the critical period of developmental plasticity and precocious formation of perineuronal nets (PNNs). PNNs are lattice-like substructures of extracellular matrix (ECM) that enwrap specific subpopulations of neurons. PNNs are essential in the modulation of neuronal plasticity and brain maturation, and their enzymatic disruption can partially restore plasticity in adults and improve memory.
View Article and Find Full Text PDFPhys Rep
August 2025
Department of Mathematics, Dartmouth College, USA.
Many empirical networks originate from correlational data, arising in domains as diverse as psychology, neuroscience, genomics, microbiology, finance, and climate science. Specialized algorithms and theory have been developed in different application domains for working with such networks, as well as in statistics, network science, and computer science, often with limited communication between practitioners in different fields. This leaves significant room for cross-pollination across disciplines.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Department of Electrical and Computer Engineering, College of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Anomaly detection in industrial imaging is critical for ensuring quality and reliability in automated manufacturing processes. While recently several methods have been reported in the literature that have demonstrated impressive detection performance on standard benchmarks, they necessarily rely on computationally intensive CNN architectures and post-processing techniques, necessitating access to high-end GPU hardware and limiting practical deployment in resource-constrained settings. In this study, we introduce a novel anomaly detection framework that leverages feature maps from a lightweight convolutional neural network (CNN) backbone, MobileNetV2, and cascaded detection to achieve notable accuracy as well as computational efficiency.
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