Improvements of the thermoelectric properties of PbTe via simultaneous doping with indium and iodine
Bibliographic record
Abstract
The thermoelectric properties of n-type InxPb1−xTe1−yIy (with x = 0.005, 0.01, 0.015; y = 0.001, 0.002, 0.004, 0.006) were investigated at elevated temperatures up to 655 K. This co-doping significantly affected the Seebeck coefficient and electrical conductivity of all samples within the measured temperature regime except for the sample with the largest concentration of In, wherein the effects of I-doping are comparatively minor. For a given concentration of In, the sample with the largest amount of iodine possesses the highest electrical conductivity, which is consistent within all three sets of samples in our present study. Thermal conductivity values are generally lower than those of undoped PbTe. An increasing iodine concentration at fixed In content was found to gradually increase the dimensionless figure-of-merit, ZT, an effect most significantly observed when x = 0.01.
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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".