Temperature and interlayer coupling induced thermal transport across graphene/2D-SiC van der Waals heterostructure
Bibliographic record
Abstract
Abstract Graphene based two-dimensional (2D) van der Waals (vdW) materials have attracted enormous attention because of their extraordinary physical properties. In this study, we explore the temperature and interlayer coupling induced thermal transport across the graphene/2D-SiC vdW interface using non-equilibrium molecular dynamics and transient pump probe methods. We find that the in-plane thermal conductivity κ deviates slightly from the 1/T law at high temperatures. A tunable κ is found with the variation of the interlayer coupling strength χ. The interlayer thermal resistance R across graphene/2D-SiC interface reaches 2.71 $$\times$$ × 10–7 $${\text{Km}}^{2} /{\text{W}}$$ Km 2 / W at room temperature and χ = 1, and it reduces steadily with the elevation of system temperature and χ, demonstrating around 41% and 56% reduction with increasing temperature to 700 K and a χ of 25, respectively. We also elucidate the heat transport mechanism by estimating the in-plane and out-of-plane phonon modes. Higher phonon propagation possibility and Umklapp scattering across the interface at high temperatures and increased χ lead to the significant reduction of R. This work unveils the mechanism of heat transfer and interface thermal conductance engineering across the graphene/2D-SiC vdW heterostructure.
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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".