Tunable electronic properties in stanene and two dimensional silicon-carbide heterobilayer: A first principles investigation
Bibliographic record
Abstract
A novel two-dimensional heterobilayer, stanene-silicon carbide (Sn/SiC) is predicted using first principles calculations. Three representational stacking configurations are considered to study the structure and electronic properties of Sn/SiC heterobilayer in detail. All the stacking patterns of the heterobilayer manifest a wide band gap of ∼160meV at the K point with the Dirac cone well preserved, exhibiting the largest energy band gap among all stanene-based two dimensional heterostructures. Moreover, the energy gap can be efficiently varied through changing the interlayer distance between stanene and SiC layer as well as applying biaxial strain. Our computed small effective mass (∼0.0145mo) and the characteristic of nearly linear band dispersion relation of the heterobilayer also suggest high mobility of the carriers. The space charge distribution of the valence and conduction bands and the density of states (DOS) of the heterostructure unravel that SiC monolayer retains the various excellent electrical properties of stanene in a great extent and allows the carriers to move through the stanene layer only. This implies the potentiality of 2D SiC as a good substrate for stanene to adopt the heterobilayer. Our results reveal that Sn/SiC heterobilayer would be a promising platform for future Sn-based high speed nanoelectronic and spintronic devices.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".