In-plane phonon transport in thin films
Bibliographic record
Abstract
The in-plane phonon thermal conductivities of argon and silicon thin films are predicted from the Boltzmann transport equation under the relaxation time approximation. We model the thin films using bulk phonon properties obtained from harmonic and anharmonic lattice dynamics calculations. The input required for the lattice dynamics calculations is obtained from interatomic potentials: Lennard-Jones for argon and Stillinger–Weber for silicon. The effect of the boundaries is included by considering only phonons with wavelengths that fit within the film and adjusting the relaxation times to account for mode-dependent, diffuse boundary scattering. Our model does not rely on the isotropic approximation or any fitting parameters. For argon films thicker than 4.3 nm and silicon films thicker than 17.4 nm, the use of bulk phonon properties is found to be appropriate and the predicted reduction in the in-plane thermal conductivity is in good agreement with results obtained from molecular dynamics simulation and experiment. We include the effects of boundary scattering without employing the Matthiessen rule. We find that the Matthiessen rule yields thermal conductivity predictions that are at most 12% lower than our more accurate results. Our results show that the average of the bulk phonon mean free path is an inadequate metric to use when modeling the thermal conductivity reduction in thin films.
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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 | 0.001 | 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".