Heat Transfer at the Interface of Graphene Nanoribbons with Different Relative Orientations and Gaps
Bibliographic record
Abstract
Because of their high thermal conductivity, graphene nanoribbons (GNRs) can be employed as fillers to enhance the thermal transfer properties of composite materials, such as polymer-based ones. However, when the filler loading is higher than the geometric percolation threshold, the interfacial thermal resistance between adjacent GNRs may significantly limit the overall thermal transfer through a network of fillers. In this article, reverse non-equilibrium molecular dynamics is used to investigate the impact of the relative orientation (i.e., horizontal and vertical overlap, interplanar spacing and angular displacement) of couples of GNRs on their interfacial thermal resistance. Based on the simulation results, we propose an empirical correlation between the thermal resistance at the interface of adjacent GNRs and their main geometrical parameters, namely the normalized projected overlap and average interplanar spacing. The reported correlation can be beneficial for speeding up bottom-up approaches to the multiscale analysis of the thermal properties of composite materials, particularly when thermally conductive fillers create percolating pathways.
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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) | 0.002 | 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".