High-Entropy Alloys in Solid-State Physics: Electronic Structure, Mechanical Properties, and Superconductivity
Keywords:
High-Entropy Alloys (HEAs), Solid-state physics, SuperconductivityAbstract
High-entropy alloys (HEAs) have emerged as a groundbreaking class of materials in solid-state physics due to their exceptional mechanical strength, tunable electronic structure, and unique superconducting properties. Unlike conventional alloys, HEAs consist of multiple principal elements in near-equiatomic ratios, resulting in high configurational entropy, which stabilizes single-phase structures such as face-centered cubic (FCC) and body-centered cubic (BCC) configurations. This study explores the electronic, mechanical, and superconducting properties of HEAs, providing both theoretical insights and experimental validation. Electronic structure analysis using density functional theory (DFT) reveals significant electronic disorder effects, impacting conductivity and bandgap variations. Mechanical testing highlights HEAs’ superior hardness, fracture toughness, and strain-hardening ability compared to conventional metals. Superconductivity in HEAs is investigated through resistivity and magnetic susceptibility measurements, demonstrating tunable critical temperatures (Tc) influenced by electron-phonon interactions and atomic disorder. Material synthesis methods such as arc melting, mechanical alloying, and magnetron sputtering are examined, along with advanced characterization techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman spectroscopy. The findings underscore HEAs’ potential for applications in aerospace, energy storage, and quantum computing, particularly in superconducting logic circuits and high-strength structural components. Despite their promising properties, challenges remain in large-scale fabrication, phase stability control, and integration into existing technologies. Future research must focus on optimizing HEA compositions for enhanced superconducting behavior, employing machine learning for predictive modeling, and exploring novel quantum applications. This study aims to bridge the gap between fundamental HEA research and real-world technological advancements, positioning HEAs as a cornerstone in next-generation materials science.