Wang Zhijun | Searcharxiv

arXiv2026

Growth and hydrostatic-pressure study of a type-II superconductor Bi$_2$Ta$_3$S$_6$ single crystal

We report the growth and physical properties of single-crystalline Bi$_2$Ta$_3$S$_6$ crystallizing in $P6_3/mcm$ space group, which comprises alternating Ta-S layers and Bi layers with each Bi atom connected with adjacent S atoms. Temperature-dependent electrical resistivity measurements reveal a superconducting transition at 0.84 K, with upper critical field 231 Oe under an out-of-plane magnetic field. The magnetization measurements confirm its nature as a type-II superconductor, with anisotropic Ginzburg-Landau parameter $κ_{ab}$ = 7.67 and $κ_c$ = 4.50. Hall measurements indicate the dominant carriers as hole. Hydrostatic pressure is applied, under which both the superconducting transition temperature and upper critical field increase sharply under low pressure before undergoing slight suppression under higher pressure. Density functional theory calculations reveal non-trivial topological surface states on (100) surface in Bi$_2$Ta$_3$S$_6$, which may offer a new avenue for exploring potential topological superconductivity in layered transition metal dichalcogenides.

cond-mat.supr-con↗

arXiv2022

A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes

This paper investigates the design of self-triggered control for networked control systems (NCS), where the dynamics of the plant is unknown apriori. To deal with the nature of the self-triggered control, in which state measurements are transmitted to the controller a-periodically, we propose to lift the continuous-time dynamics to a novel dynamical model by taking an inter-event time as an additional input, and then, the lifted model is learned by the Gaussian processes (GP) regression. Moreover, we propose a learning-based approach, in which a self-triggered controller is learned by minimizing a cost function, such that it can take inter-sample behavior into account. By employing the lifting approach, we can utilize a gradient-based policy update as an efficient method to optimize both control and communication policies. Finally, we summarize the overall algorithm and provide a numerical simulation to illustrate the effectiveness of the proposed approach.

eess.SY↗