Thermal transport through short-period SiGe nanodot superlattices
Bibliographic record
Abstract
The cross-plane thermal conductivity κ of multilayers of SiGe nanodots separated either by Si or SiGe can be decreased by reducing the period length or by increasing the nanodot density. It is, however, not clear how far κ can be reduced by using these strategies. In addition, the role of SiGe nanodots on the reduction of κ is still not fully understood. In this work, we addressed these issues by studying experimentally the cross-plane κ of Ge/Si superlattices with period lengths down to 1.5 nm. Although κ tends to preserve the decreasing trend with reducing the period length, for periods shorter than 2 nm we observed a drastic drop of the average thermal resistance per period. This finding indicates a weakening of the effect of the interfaces on phonon scattering and implies a lower limit for κ. To assess the role played by the nanodots in the reduction of κ we studied Ge/Si superlattices with nanodot densities varying from 0 to ∼8×1010 cm−2 and a fixed Si spacer thickness of 2.7 nm. The experimental results suggest that SiGe nanodots with ‘‘pyramid’’-shape have an effect comparable to nominally planar wetting layers on the cross-plane thermal transport. Finally, the comparison of superlattices with nanodots separated by Si1−xGex (with x from 0 to 0.2) shows that spacer alloying is beneficial in reducing the κ by ∼20%. The results presented in this work are expected to be relevant to micro/nanoscale energy conversion which requires minimizing the thermal conductivity of superlattice-based thin film thermoelectrics.
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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 | 0.000 |
| 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".