arXiv subjects

Shihao Tu

Publications and source records attributed to Shihao Tu.

4 recordsLinked to original sources

arXiv2026

Individual Vanadium Dopants Form Deep In-Gap States in Monolayer WS2

Point defects in atomically thin materials have a strong impact on physical properties and those that induce in-gap states are advantageous for quantum information science and engineering (QISE). However, dopant engineering consisting of well-controlled synthesis and robust identification of in-gap states is challenging. In this work, we addressed this challenge by first using finely tuned chemical vapor deposition to incorporate vanadium dopants into a monolayer WS2 (V-WS2). Next, we utilized a suite of scanned probe microscopy techniques to identify and characterize individual dopants. The latter included conductive atomic force microscopy (cAFM), low temperature scanning tunneling microscopy and spectroscopy (STM/STS), and scanning transmission electron microscopy and unambiguously revealed that vanadium dopants form deep in-gap states 0.35 eV above the valence band maximum in V-WS2. Our experimental results are well supported by first principles calculations and taken together demonstrate that V-WS2 is a promising platform for QISE applications.

cond-mat.mtrl-sci↗

arXiv2026

Spin lifetime anisotropy in graphene induced by the SiO2 interface

Understanding how common dielectric substrates influence the spin transport properties of graphene is essential for advancing graphene-based spintronic technologies. Here we use a comprehensive set of numerical simulations to reveal how a SiO$_2$ substrate modifies the spin texture and governs spin relaxation in graphene. Using first-principles density matrix dynamics simulations, as well as tight-binding (TB) transport simulations, we quantify the effects of electron-phonon scattering, impurity scattering, and electrostatic disorder on the spin relaxation process. We find that a 2D SiO$_2$ substrate induces a predominantly Rashba-type helical spin texture in graphene, leading to a spin lifetime anisotropy of 1/2. Meanwhile, bulk SiO$_2$ breaks in-plane symmetry in graphene, leading to anisotropic in-plane and out-of-plane components in the spin texture, which we capture with a newly-developed TB model of graphene. Transport simulations under realistic disorder conditions reveal a spin lifetime anisotropy between 0.5 and 1, similar to what is seen in measurements of graphene spin valves on a SiO$_2$ substrate. Our results reveal a more complex picture of spin relaxation at the ubiquitous graphene/SiO$_2$ interface, beyond the standard Rashba model, providing critical insight for interpreting experiments and guiding substrate engineering for graphene spintronics.

cond-mat.mes-hall↗

arXiv2025

Relation of Continuous Chirality Measure to Spin and Orbital Polarization, and Chiroptical Properties in Solids

Chirality introduces intriguing topological, electronic, and spin-optronic properties to molecules and solids. In this work, we provide a quantitative metric for the degree of chirality in solids, independent of the type of system and the dimensionality, through the continuous chirality measure (CCM). We quantitatively analyze the correlation between CCM and spin and orbital angular momentum (OAM) polarization, as well as circular dichroism (CD) and the circular photogalvanic effect (CPGE). By internal spin-orbit field analysis, we demonstrate a distinct character (proportionality among Rashba, Deresselhaus, and Weyl contributions) and chirality dependence among different chiral solids. Furthermore, unlike CD, we found that absorption dissymmetry factor $g_{CD}$ could remain unchanged as a function of chirality and show anisotropic dependence on CCM. In addition, we show that the relation between CCM and CPGE is rather complex. At low excitation energy close to the bandgap transition, the CCM continuously tunes the total SOC, and therefore, the CPGE response. However, at high excitation energy, CPGE includes more than just band edge transitions, which complicates the relation of chirality and CPGE due to changes in the optical dipole strength and electron-hole group velocity difference. Ultimately, this causes CPGE to be only correlated with chirality at excitation energies close to the band edge. At the end, we discussed strategies of manipulating chiral-optical properties through chirality transfer at interfaces or applying strain. The insights developed in this work will inspire the design of materials for future spintronics and orbitronics, as well as spin-optronics applications.

cond-mat.mtrl-sci↗

arXiv2024

PowerPM: Foundation Model for Power Systems

The emergence of abundant electricity time series (ETS) data provides ample opportunities for various applications in the power systems, including demand-side management, grid stability, and consumer behavior analysis. Deep learning models have advanced ETS modeling by effectively capturing sequence dependence. Nevertheless, learning a generic representation of ETS data for various applications remains challenging due to the inherently complex hierarchical structure of ETS data. Moreover, ETS data exhibits intricate temporal dependencies and is suscepti ble to the influence of exogenous variables. Furthermore, different instances exhibit diverse electricity consumption behavior. In this paper, we propose a foundation model PowerPM to model ETS data, providing a large-scale, off-the-shelf model for power systems. PowerPM consists of a temporal encoder and a hierarchical encoder. The temporal encoder captures both temporal dependencies in ETS data, considering exogenous variables. The hierarchical encoder models the correlation between hierarchy. Furthermore, PowerPM leverages a novel self-supervised pretraining framework consisting of masked ETS modeling and dual-view contrastive learning, which enable PowerPM to capture temporal dependency within ETS windows and aware the discrepancy across ETS windows, providing two different perspectives to learn generic representation. Our experiments involve five real world scenario datasets, comprising private and public data. Through pre-training on massive ETS data, PowerPM achieves SOTA performance on diverse downstream tasks within the private dataset. Impressively, when transferred to the public datasets, PowerPM maintains its superiority, showcasing its remarkable generalization ability across various tasks and domains. Moreover, ablation studies, few-shot experiments provide additional evidence of the effectiveness of our model.

cs.LG↗