Correlation Between Corrugation-Induced Flexoelectric Polarization and Conductivity of Low-Dimensional Transition Metal Dichalcogenides
Bibliographic record
Abstract
The tunability of polar and semiconducting properties of low-dimensional transition metal dichalcogenides (TMDs) have propelled them to the forefront of fundamental and applied physical research. These materials can vary their electrophysical properties from nonpolar to ferroelectric, and from direct-band semiconducting to metallic. In addition to classical controlling factors, such as field effect, composition, and doping, new degrees of freedom emerge in TMDs due to the curvature-induced electron redistribution and the associated changes in electronic properties. Here we theoretically explore the elastic and electric fields, flexoelectric polarization and free charge density for a TMD nanoflake placed on a rough substrate with a sinusoidal corrugation profile. Finite element modelling results for different flake thickness and corrugation depth yield insights into the flexoelectric nature of the out-of-plane electric polarization and establish the unambiguous correlation between the polarization and static conductivity modulation. The modulation is caused by the coupling between the deformation potential and inhomogeneous elastic strains, which evolve in the TMD nanoflake due to the adhesion between the flake surface and corrugated substrate. We reveal a pronounced maximum in the thickness dependences of the electron and hole conductivity of ${\mathrm{Mo}\mathrm{S}}_{2}$ and ${\mathrm{Mo}\mathrm{Te}}_{2}$ nanoflakes placed on a corrugated substrate, which opens the way for the optimization of their geometry towards significant improvement in their polar and electronic properties, necessary for advanced applications in nanoelectronics and memory devices. Specifically, the obtained results can be useful for the development of nanoscale straintronic devices based on the bended ${\mathrm{Mo}\mathrm{S}}_{2}$, ${\mathrm{Mo}\mathrm{Te}}_{2}$, and $\mathrm{Mo}\mathrm{S}\mathrm{Te}$ nanoflakes, such as diodes and bipolar transistors with a bending-controllable sharpness of p-n junctions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".