Giant valley splitting and tunable anisotropic spin plasmons in a Janus ferrovalley monolayer

IF 11.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Zhihua Zhang, Haotian Sun, Mimi Dong, Yiyi Guo, Mingwen Zhao
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Abstract

Manipulating the spin and valley degrees of freedom of electrons is crucial for next-generation information technologies. Altermagnets, as an emerging magnetic phase, provide a quantum platform with intrinsic spin-valley locking, enabling multi-state manipulation of both spin and valley. Here, we propose a Janus monolayer CaCoFeN2, achieved through in situ substitution of magnetic transition metal atoms in the two-dimensional (2D) altermagnet Ca(CoN)2 [Phys. Rev. Lett. 133, 056401 (2024)]. Our first-principles calculations identify CaCoFeN2 as an anisotropic spin-plasmon ferrovalley semiconductor, with a large valley splitting of 273 meV solely through crystal symmetry breaking, without any involvement of spin-orbit coupling (SOC). Furthermore, its anisotropic electronic structures facilitate highly directional spin plasmon propagation. Carrier-type switching (n-type ↔ p-type) reverses the anisotropy along orthogonal axes, yielding open equi-frequency contours in n-type CaCoFeN2. The integration of spontaneous spin and valley polarization within a single material without SOC, offers new opportunities for advancements in spintronics and valleytronics.

Abstract Image

Janus铁谷单层中的巨谷分裂和可调谐各向异性自旋等离子体
操纵电子的自旋和谷自由度对下一代信息技术至关重要。交替磁体作为一种新兴的磁相,提供了一个具有固有自旋谷锁定的量子平台,实现了自旋和自旋谷的多态操作。在这里,我们提出了一种Janus单层cofen2,通过在二维(2D)交替磁体Ca(CoN)2中原位取代磁性过渡金属原子来实现。Rev. Lett. 133, 056401(2024)]。我们的第一性原理计算确定了CaCoFeN2是一种各向异性自旋等离子体激元铁谷半导体,仅通过晶体对称破缺就能产生273 meV的大山谷分裂,而不涉及自旋轨道耦合(SOC)。此外,其各向异性电子结构有利于高定向自旋等离子体传播。载波型交换(n型↔p型)使n型CaCoFeN2沿正交轴的各向异性相反,产生开的等频轮廓。自发自旋和谷极化在没有SOC的单一材料中的集成,为自旋电子学和谷电子学的进步提供了新的机会。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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