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Chirality dependent electromechanical properties of single-layer MoS 2 under out-of-plane deformation: a DFT study.

Faling LingYi LingXiaoqing LiuLi LiXianju ZhouXiao TangChuan JingYongjie WangSha JiangYi Lu
Published in: Physical chemistry chemical physics : PCCP (2023)
2D transition metal dichalcogenides (TMDs) demonstrate significant promise in logic circuits and optoelectronic devices because of their unique structures and excellent semiconductor properties. However, they inevitably undergo out-of-plane deformation during practical applications due to their ultra-thin structures. Recent experiments have shown that out-of-plane deformation significantly affects the electronic structures of 2D TMDs. However, the underlying physical mechanism is largely unknown. Therefore, it is critical to have a deeper understanding of out-of-plane deformation in 2D TMDs to optimize their applications in different fields. Currently, one of the most pressing matters that requires clarification is the chirality dependence of out-of-plane deformation in tuning the electromechanical properties of 2D TMDs. In this work, using single-layer MoS 2 as a probe, we systematically investigate the effects of out-of-plane deformation along different chirality directions on the bond length, bending stiffness, electric polarization, and band structure of 2D TMDs by employing first-principles calculations based on density functional theory. Our results indicate that the bond length, bending energy, polarization strength, and band gap size of single-layer MoS 2 are isotropic under out-of-plane deformation, while the band gap type is closely related to the direction of deformation. Our study will provide an essential theoretical basis for further revealing the structure-performance relationship of 2D TMDs.
Keyphrases
  • density functional theory
  • transition metal
  • quantum dots
  • high resolution
  • room temperature
  • molecular dynamics
  • mental health
  • molecular dynamics simulations
  • mass spectrometry
  • single molecule
  • big data