Effect of Grain Boundaries on the Lattice Thermal Transport Properties of Insulating Materials: A Predictive Model
Bibliographic record
Abstract
We present two theoretical models to predict the lattice thermal conductivity degradation of insulating materials at high temperature (above one‐third of the Debye temperature). This degradation is due to the presence of grains, with known sizes and shapes, inducing thermal resistance at their boundaries. The first model is derived directly from the kinetic theory of gases (KTG). The formulation of the second is based on a localized continuum model (LCM), assuming phonon Umklapp scattering and the Debye approximation of phonon density of state. The two proposed models are purely predictive, as no experimental information related to the grain size dependence of the thermal conductivity is necessary for the parameterization of the models. The predictive accuracy of the two proposed models is tested on several different types of electrically insulating compounds. Although the model derived from the KTG is similar to the well‐known Kapitza thermal resistance formalism, it fails to predict the grain size dependence of the lattice thermal conductivity. The one derived from a LCM is a new formalism predicting, with good accuracy, the lattice thermal conductivity as a function of the average grain size. It is applicable for microstructures with a grain size typically above 20–50 nm.
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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 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".