Microscopic Model to Quantify the Difference of Energy-Transfer Rates between Bonded and Nonbonded Monomers in Polymers
Bibliographic record
Abstract
Thermal transport properties often dictate the usefulness of materials in a variety of applications. In this context, polymers are an important material class because they provide different pathways of energy transport due to the distinct microscopic interactions, i.e., via stiff, covalently bonded backbone interactions or via soft, nonbonded interactions, such as van der Waals (vdW) forces and/or hydrogen bonds (H-bond). Therefore, the precise control of the delicate balance between bonded and nonbonded energy transfer rates provides a possible strategy to tailor the thermal conductivity of a material. In this work, we devise a simple analytical model that decouples the microscopic bonded, G b, and nonbonded, G nb, contributions to the heat transport in polymeric materials. This model considers the diffusion of energy along the macromolecular backbone, involving multiple transfers before it can hop off to a neighboring chain molecule. We show how these individual microscopic components can be combined to obtain a diffusive contribution to the macroscopic thermal transport coefficient, κ. The ability of the model to describe thermal transport is validated by molecular simulations of one universal polymer model and three, chemically specific, all-atom polymer models. These results suggest strategies for tailoring κ of polymeric materials by macromolecular engineering of molecular architecture and conformations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".