Effects of Defect and Temperature on the Mechanical Performance of WS2: A Multiscale Analysis

Hongyu Tang, Dong Hu, Zhen Cui, Huaiyu Ye, Guoqi Zhang

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
95 Downloads (Pure)

Abstract

This paper analyzes the mechanical properties of tungsten disulfide (WS2) by means of multiscale simulation, including density functional theory (DFT), molecular dynamic (MD) analysis, and finite element analysis (FEA). We first conducted MD analysis to calculate the mechanical properties (i.e., Young's modulus and critical stress) of WS2. The influence of different defect types (i.e., point defects and line defects) on the mechanical properties are discussed. The results reveal that WS2 has a high Young's modulus and high critical stress. Next, the effects of defect density and temperature on the mechanical properties of the material were analyzed. The results show that a lower defect density results in improved performance and a higher temperature results in better ductility, which indicate that WS2 can potentially be a strain sensor. Based on this result, FEA was employed to analyze the WS2 stress sensor and then fabricate and analyze the device for benchmarking. It is found that the FEA model proposed in this work can be used for further optimization of the device. According to the DFT results, a narrower band gap WS2 is found with the existence of defects and the applied strain. The proposed multiscale simulation method can effectively analyze the mechanical properties of WS2 and optimize the design. Moreover, this method can be extended to other 2D/nanomaterials, providing a reference for a rapid and effective systematic design from the nanoscale to macroscale.

Original languageEnglish
Pages (from-to)2680-2690
Number of pages11
JournalJournal of Physical Chemistry C
Volume125
Issue number4
DOIs
Publication statusPublished - 2021

Fingerprint

Dive into the research topics of 'Effects of Defect and Temperature on the Mechanical Performance of WS2: A Multiscale Analysis'. Together they form a unique fingerprint.

Cite this