Twinning in two-dimensional materials and its application to electronic properties
Bibliographic record
Abstract
Abstract Controlled band gap engineering is crucial to the design of next-generation, flexible, two-dimensional (2D) electronic nanodevices. In 2D materials, defects have shown promise in manipulating electronic properties. Unfortunately, only a small number of topological defects are available in 2D materials, leading to the current open problem of overcoming challenges in tailoring material properties. We propose the exploitation of twin boundaries, as they can, in principle, be generated under controlled circumstances (e.g. by applying shear deformation or by shuffling atoms). Using a recently-developed twin framework, we investigate the use of twin boundaries to modify electronic properties in 2D materials. Taking graphene and molybdenum disulfide as representative materials, we study several twin modes predicted with our framework and compute their nucleation and formation energies, equilibrium positions, and thermal stability using atomistic simulations. We show that many of our predicted twin boundaries are seen in experimental characterization of 2D materials, with their energies being competitive relative to existing grain boundaries. To highlight the possibility of using twin boundaries in nanoscale devices, we compute their respective electronic properties and transmission gaps. By showing that gaps as large as 1.2 eV can be opened up with the introduction of nano-twins, we propose their use in the development of field-effect nano-transistors with tailored properties based on 2D materials.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".