Interaction between filler species in double-filled skutterudites
Bibliographic record
Abstract
Filled skutterudites are workhorse materials in thermoelectric research. In recent years, double- or even triple-filling has emerged as a promising strategy to improve the thermoelectric figure of merit in skutterudites. For example, one type of filler is used to reduce thermal conductivity while the other filler is used to control the electron filling. However, in these studies, each filler atom is considered independent of each other and the interaction between the two filler atoms is completely ignored. Here we present our detailed investigation of the local structure of filler atoms in (${\mathrm{Yb},\mathrm{In})}_{x}{\mathrm{Co}}_{4}{\mathrm{Sb}}_{12}$ and show that significant interaction does exist between filler species. While Yb or In filler atoms at low concentration occupy the usual $2a$ filler site on their own, when Yb filler concentration goes above a critical value $x\ensuremath{\approx}0.15$, Yb pushes In atoms from the $2a$ site and into the $24g$ substitutional site replacing Sb atoms. This behavior is in stark contrast to that of Ga, which forms a dual-site defect even in the absence of Yb fillers. The temperature-dependent, extended x-ray absorption fine structure (EXAFS) data further reveal distinct lattice dynamical properties around In, Ga, and Yb filler atoms. Our findings point out that the filler interaction should be an important design consideration for new thermoelectric skutterudites.
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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 | 0.001 | 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".