Tuning thermal transport in highly cross-linked polymers by bond-induced void engineering
Bibliographic record
Abstract
Tuning the heat flow is fundamentally important for the design of advanced functional materials. Here polymers are of particular importance because they provide different pathways for the energy transfer. More specifically, the heat flow between the two covalently bonded monomers is over 100 times faster than between the two nonbonded monomers interacting via the van der Waals (vdW) forces. Therefore, the delicate balance between these two contributions often provides a guiding tool for the tunability in thermal transport coefficient $\ensuremath{\kappa}$ of the polymeric materials. Traditionally most studies have investigated $\ensuremath{\kappa}$ in the linear polymeric materials, the recent interests have also been directed towards the highly cross-linked polymers (HCP). In this work, using the generic molecular dynamics simulations, we investigate the factors effecting $\ensuremath{\kappa}$ of HCP. We emphasize the importance of the cross-linking bond types and their influence on the network microstructure, with a goal of providing a guiding principle for the tunability in $\ensuremath{\kappa}$. While these simulation results are discussed in the context of the available experimental data, we also make predictions.
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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 | 0.001 |
| 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".