Engineering topics

Tang, Xiuyu

Publications and source records attributed to Tang, Xiuyu.

4 records

DOE OSTI2024

Strong interlayer coupling and long-lived interlayer excitons in two-dimensional perovskite derivatives and transition metal dichalcogenides van der Waals heterostructures

Two-dimensional (2D) van der Waals (vdW) heterostructures offer new platforms for exploring novel physics and diverse applications ranging from electronics and photonics to optoelectronics at the nanoscale. The studies to date have largely focused on transition-metal dichalcogenides (TMDCs) based samples prepared by mechanical exfoliation method, therefore it is of signi ficant interests to study high-quality vdW heterostructures using novel materials prepared by a versatile method. Here, we report a two-step vapor phase growth process for the creation of high-quality vdW heterostructures based on perovskites and TMDCs, such as 2D Cs 3 Bi 2 I 9 /MoSe 2 , with a large lattice mismatch. Supported by experimental and theoretical investigations, we discover that the Cs 3 Bi 2 I 9 /MoSe 2 vdW heterostructure possesses hybrid band alignments consisting of type -I and type-II heterojunctions because of the existence of defect energy levels in Cs 3 Bi 2 I 9 . More importantly, we demonstrate that the type-II heterojunction in the Cs 3 Bi 2 I 9 /MoSe 2 vdW heterostructure not only shows a higher interlayer exciton density, but also exhibits a longer interlayer exciton lifetime than traditional 2D TMDCs based type-II heterostructures. We attribute this phenomenon to the reduced overlap of electron and hole wavefunctions caused by the large lattice mismatch. Finally, our work demonstrates that it is possible to directly grow high-quality vdW heterostructures based on entirely different materials which provide promising platforms for exploring novel physics and cutting-edge applications, such as optoelectronics, valleytronics, and high-temperature super fluidity.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Domain-dependent strain and stacking in two-dimensional van der Waals ferroelectrics

Van der Waals (vdW) ferroelectrics have attracted significant attention for their potential in next-generation nano-electronics. Two-dimensional (2D) group-IV monochalcogenides have emerged as a promising candidate due to their strong room temperature in-plane polarization down to a monolayer limit. However, their polarization is strongly coupled with the lattice strain and stacking orders, which impact their electronic properties. Here, we utilize four-dimensional scanning transmission electron microscopy (4D-STEM) to simultaneously probe the in-plane strain and out-of-plane stacking in vdW SnSe. Specifically, we observe large lattice strain up to 4% with a gradient across ~50 nm to compensate lattice mismatch at domain walls, mitigating defects initiation. Additionally, we discover the unusual ferroelectric-to-antiferroelectric domain walls stabilized by vdW force and may lead to anisotropic nonlinear optical responses. Our findings provide a comprehensive understanding of in-plane and out-of-plane structures affecting domain properties in vdW SnSe, laying the foundation for domain wall engineering in vdW ferroelectrics.

42 ENGINEERING↗

DOE OSTI2022

Forecasting of in situ electron energy loss spectroscopy

Abstract Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.

36 MATERIALS SCIENCE↗

DOE OSTI2022

pnnl/EELSTM

EELSTM: Time Series Prediction on Electron Energy Loss Spectra - long short-term memory model approach to forecasting of electron energy loss spectroscopy, based on a Bayesian deep learning model

Doty, Christina↗