Lateral and flexural thermal transport in stanene/2D-SiC van der Waals heterostructure
Bibliographic record
Abstract
Abstract Thermal management is one of the key challenges in nanoelectronic and optoelectronic devices. The development of a van der Waals heterostructure (vdWH) using the vertical positioning of different two-dimensional (2D) materials has recently appeared as a promising way of attaining desirable electrical, optical, and thermal properties. Here, we explore the lateral and flexural thermal conductivity of stanene/2D-SiC vdWH utilizing the reverse non-equilibrium molecular dynamics simulation and transient pump-probe technique. The effects of length, area, coupling strength and temperature on the thermal transport are studied systematically. The projected lateral thermal conductivity of a stanene/2D-SiC hetero-bilayer is found to be 66.67 W m − 1 K − 1 , which is greater than stanene, silicene, germanene, MoSe 2 and even higher than some hetero-bilayers, including MoS 2 /MoSe 2 and stanene/silicene. The lateral thermal conductivity increases as the length increases, while it tends to decrease with increasing temperature. The computed flexural interfacial thermal resistance between stanene and 2D-SiC is 3.0622 × 10 − 7 K.m 2 W −1 , which is close to other 2D hetero-bilayers. The interfacial resistance between stanene and 2D-SiC is reduced by 70.49% and 50.118% as the temperature increases from 100 K to 600 K and the coupling factor increases from χ = 0.5 to χ = 5 , respectively. In addition, various phonon modes are evaluated to disclose the fundamental mechanisms of thermal transport in the lateral and flexural direction of the hetero-bilayer. These results are important in order to understand the heat transport phenomena of stanene/2D-SiC vdWH, which could be useful for enhancing their promising applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".