Pooja Vyas | Searcharxiv

arXiv2025

Disorder mediated fully compensated ferrimagnetic spin-gapless semiconducting behaviour in Cr3Al Heusler alloy

Spin-gapless semiconductors (SGSs) that simultaneously host fully compensated ferrimagnetism are highly sought for energy-efficient and stray-field-free spintronic technologies, yet their realization in chemically disordered systems has remained elusive. Here, we demonstrate that the binary Heusler alloy Cr3Al despite adopting a fully A2-disordered structure exhibits a rare coexistence of SGS transport and a fully compensated ferrimagnetic (FCF) ground state. Single-crystalline and polycrystalline Cr3Al samples were synthesized, and comprehensive structural analyses using single crystal XRD, synchrotron powder XRD, and neutron powder diffraction reveal complete Cr/Al site mixing. Remarkably, this chemical disorder does not disrupt magnetic order; instead, magnetization, X-ray magnetic circular dichroism (XMCD), and temperature-dependent neutron diffraction establish a robust compensated ferrimagnetic state with a vanishingly small ordered moment of 0.1(1) muB/f.u and a high Curie temperature of 773(2) K. Electrical and thermal transport measurements uncover clear SGS characteristics, including weak temperature-dependent conductivity, very low Seebeck coefficients, and electron-hole compensated transport. Hall measurements show unusual temperature-dependent carrier concentrations consistent with disorder-modified electronic states. First-principles calculations on an A2-disordered SQS structure reproduce the experimentally observed negligibly small magnetization (0.0072 muB/f.u) and reveal a vanishing spin-up band gap unambiguously supporting SGS behavior driven by chemical disorder. Our results identify Cr3Al as the first experimentally verified A2-disordered Heusler alloy exhibiting both fully compensated ferrimagnetism and spin-gapless semiconducting transport, positioning it as a robust and disorder-tolerant platform for next-generation, high-temperature spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Leveraging Clinically Relevant Biometric Constraints To Supervise A Deep Learning Model For The Accurate Caliper Placement To Obtain Sonographic Measurements Of The Fetal Brain

Multiple studies have demonstrated that obtaining standardized fetal brain biometry from mid-trimester ultrasonography (USG) examination is key for the reliable assessment of fetal neurodevelopment and the screening of central nervous system (CNS) anomalies. Obtaining these measurements is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. In this study, we propose a deep learning (DL) approach to compute 3 key fetal brain biometry from the 2D USG images of the transcerebellar plane (TC) through the accurate and automated caliper placement (2 per biometry) by modeling it as a landmark detection problem. We leveraged clinically relevant biometric constraints (relationship between caliper points) and domain-relevant data augmentation to improve the accuracy of a U-Net DL model (trained/tested on: 596 images, 473 subjects/143 images, 143 subjects). We performed multiple experiments demonstrating the effect of the DL backbone, data augmentation, generalizability and benchmarked against a recent state-of-the-art approach through extensive clinical validation (DL vs. 7 experienced clinicians). For all cases, the mean errors in the placement of the individual caliper points and the computed biometry were comparable to error rates among clinicians. The clinical translation of the proposed framework can assist novice users from low-resource settings in the reliable and standardized assessment of fetal brain sonograms.

eess.IV↗