Optimization of the Telluride Tl<sub>10–<i>x</i>–<i>y</i></sub>Sn<i><sub>x</sub></i>Bi<i><sub>y</sub></i>Te<sub>6</sub> for the Thermoelectric Energy Conversion
Bibliographic record
Abstract
Abstract We present the thermoelectric properties of the quaternary telluride series Tl10–x–ySnxBiyTe6 including Seebeck coefficient (S), electrical conductivity (σ), and thermal conductivity (κ). Three different Tl concentrations, namely 9, 8.67, and 8.33 Tl per formula unit, were selected followed by variations of the Sn:Bi ratio to tune the properties at each Tl concentration. Additionally, crystal structure data and electronic structure calculations were used to model the data and support the findings. The Tl9(Sn, Bi)Te6 system was found to have the highest power factor (S2σ) displaying a value of 8.1 μW·cm–1·K–2 at 587 K, realized with Tl9Sn0.2Bi0.8Te6. Tl8.67Sn0.50Bi0.83Te6 however, displays values closer to 4 μW·cm–1·K–2 at comparable temperatures. Ultimately, the thermoelectric Figure‐of‐merit was determined to reach a competitive 0.6 at 575 K and 525 K for Tl8.33Sn1.12Bi0.55Te6 and Tl8.67Sn0.50Bi0.83Te6, respectively, thereby outperforming the ternary variants Tl10–xSnxTe6.
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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 | 0.001 |
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".