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Mechanically tunable spontaneous vertical charge redistribution in few-layer WTe2

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 Added by Zeyuan Ni
 Publication date 2018
  fields Physics
and research's language is English




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Broken symmetry is the essence of exotic properties in condensed matters. Tungsten ditelluride, WTe$_2$, exceptionally takes a non-centrosymmetric crystal structure in the family of transition metal dichalcogenides, and exhibits novel properties$^{1-4}$, such as the nonsaturating magnetoresistance$^1$ and ferroelectric-like behavior$^4$. Herein, using the first-principles calculation, we show that unique layer stacking in WTe$_2$ generates surface dipoles with different strengths on the top and bottom surfaces in few-layer WTe$_2$. This leads to a layer-dependence for electron/hole carrier ratio and the carrier compensation responsible for the unusual magnetoresistance. The surface dipoles are tunable and switchable using the interlayer shear displacement. This could explain the ferroelectric-like behavior recently observed in atomically thin WTe$_2$ films$^4$. In addition, we reveal that exfoliation of the surface layer flips the out-of-plane spin textures. The presented results will aid in the deeper understanding, manipulation, and further exploration of the physical properties of WTe$_2$ and related atom-layered materials, for applications in electronics and spintronic devices.



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When a crystal becomes thinner and thinner to the atomic level, peculiar phenomena discretely depending on its layer-numbers (n) start to appear. The symmetry and wave functions strongly reflect the layer-numbers and stacking order, which brings us a potential of realizing new properties and functions that are unexpected in either bulk or simple monolayer. Multilayer WTe2 is one such example exhibiting unique ferroelectricity and non-linear transport properties related to the antiphase stacking and Berry-curvature dipole. Here we investigate the electronic band dispersions of multilayer WTe2 (2-5 layers), by performing laser-based micro-focused angle-resolved photoelectron spectroscopy on exfoliated-flakes that are strictly sorted by n and encapsulated by graphene. We clearly observed the insulator-semimetal transition occurring between 2- and 3-layers, as well as the 30-70 meV spin-splitting of valence bands manifesting in even n as a signature of stronger structural asymmetry. Our result fully demonstrates the possibility of the large energy-scale band and spin manipulation through the finite n stacking procedure.
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Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for the further study. Traditional exploration of the optimal synthesis conditions of novel materials is based on the trial-and-error approach, which is time consuming, costly and laborious. Recently, machine learning (ML) has demonstrated promising capability in guiding material synthesis through effectively learning from the past data and then making recommendations. Here, we report the implementation of supervised ML for the chemical vapor deposition (CVD) synthesis of high-quality 1D few-layered WTe2 nanoribbons (NRs). The synthesis parameters of the WTe2 NRs are optimized by the trained ML model. On top of that, the growth mechanism of as-synthesized 1T few-layered WTe2 NRs is further proposed, which may inspire the growth strategies for other 1D nanostructures. Our findings suggest that ML is a powerful and efficient approach to aid the synthesis of 1D nanostructures, opening up new opportunities for intelligent material development.
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