Large Improvements in the Thermoelectric Properties of SnSe by Fast Cooling
Bibliographic record
Abstract
As reported during the last five years, SnSe is one of the leading thermoelectric (TE) materials with a very low lattice thermal conductivity. However, its elements are not as heavy as those of classical thermoelectric materials like PbTe or Bi2Te3. Its outstanding TE properties were revealed after repeated purification steps to minimize the amount of oxygen contamination, followed by spark plasma sintering. Recently, we showed that hot-pressing—once optimized—can yield comparable or even better TE performance using the examples of Na- and Cu- as well as Na- and Ag-co-doped SnSe. However, long-term stability remains a challenge during cycling between low and high temperatures. Here, we investigated whether the cooling procedure has a significant impact on the thermoelectric properties of SnSe. We compared cooling of the melt with a 1:1 ratio of Sn:Se from 1273 K down to room temperature in air with quenching in water. As typical for undoped SnSe, both materials were extrinsic p-type semiconductors due to Sn defects. The air-quenched sample exhibited higher thermal conductivity, lower electrical conductivity, and higher Seebeck coefficient, all consistent with a smaller number of defects and thus a smaller number of charge carriers due to the slower cooling procedure. This resulted in a comparatively low peak figure-of-merit value zT of 0.61 at 823 K for the air-quenched sample, compared to the substantially higher peak zT of 1.58 at 813 K for the water-quenched sample.
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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) | 0.002 | 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".