Microstructural Instability and Its Effects on Thermoelectric Properties of SnSe and Na-Doped SnSe
Bibliographic record
Abstract
Effects of thermal cycling on the microstructure and thermoelectric properties are studied for the undoped and Na-doped SnSe samples using X-ray computed tomography and property measurements. It is observed that thermal cycling causes significant cracks to develop, which decrease both the electrical and lattice thermal conductivities but do not affect the thermopower. The zT values are drastically reduced after the repeated heat treatment. It is important to account for density changes during cycling to obtain accurate values of the thermal conductivity. Even before thermal cycling, the spark-plasma sintered (SPS) samples have a significant number of microcracks. The orientation of cracks within the SPS pellets and their effect on the microstructure are influenced by the presence of a Na-rich impurity. The SnSe and Sn 0.995 Na 0.005 Se samples without the impurity develop cracks and exhibit grain growth parallel to the pellet surface, which is also the plane of the 2D SnSe layers. The Sn 0.97 Na 0.03 Se sample containing the impurity develops cracks that are orthogonal to the pellet surface. Such an orientation of cracks in Sn 0.97 Na 0.03 Se inhibits grain growth. All samples appear mechanically unstable after thermal cycling.
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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".