Role of vibrational properties and electron–phonon coupling on thermal transport across metal-dielectric interfaces with ultrathin metallic interlayers
Bibliographic record
Abstract
Abstract We systematically analyze the influence of 5 nm thick metal interlayers inserted at the interface of several sets of different metal-dielectric systems to determine the parameters that most influence interface transport. Our results show that despite the similar Debye temperatures of Al2O3 and AlN substrates, the thermal boundary conductance measured for the Au/Al2O3 system with Ni and Cr interlayers is ∼2× and >3× higher than the corresponding Au/AlN system, respectively. We also show that for crystalline SiO2 (quartz) and Al2O3 substrates having highly dissimilar Debye temperature, the measured thermal boundary conductance between Al/Al2O3 and Al/SiO2 are similar in the presence of Ni and Cr interlayers. We suggest that comparing the maximum phonon frequency of the acoustic branches is a better parameter than the Debye temperature to predict the change in the thermal boundary conductance. We show that the electron–phonon coupling of the metallic interlayers also alters the heat transport pathways in a metal-dielectric system in a nontrivial way. Typically, interlayers with large electron–phonon coupling strength can increase the thermal boundary conductance by dragging electrons and phonons into equilibrium quickly. However, our results show that a Ta interlayer, having a high electron–phonon coupling, shows a low thermal boundary conductance due to the poor phonon frequency overlap with the top Al layer. Our experimental work can be interpreted in the context of diffuse mismatch theory and can guide the selection of materials for thermal interface engineering.
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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) | 0.000 | 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) | 0.001 | 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".