Quasilinear dispersion in electronic band structure and high Seebeck coefficient in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>CuFe</mml:mi><mml:msub><mml:mi mathvariant="normal">S</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>-based thermoelectric materials
Bibliographic record
Abstract
The earth-abundant natural mineral chalcopyrite $\mathrm{CuFe}{\mathrm{S}}_{2}$ is a potential $n$-type thermoelectric material because of its large Seebeck coefficient at high carrier concentrations. For a long time, the large Seebeck coefficient of $\mathrm{CuFe}{\mathrm{S}}_{2}$ has been attributed to a large electron effective mass, but the reasons for this and the unusual carrier concentration dependent behavior have rarely been discussed. Here, we systematically investigated the special transport behavior of $\mathrm{CuFe}{\mathrm{S}}_{2}$ and found the classical parabolic band model to be inadequate in explaining it. Our experimental and theoretical studies indicate that there are two flat electronic pockets at the \ensuremath{\Gamma} and $Z$ points of the Brillouin zone near the conduction band edge of $\mathrm{CuFe}{\mathrm{S}}_{2}$ that dominate the charge transport. These electronic pockets result from nonparabolic quasilinearly dispersing bands that give rise to a linear wave vector dependent energy ($E\ensuremath{\sim}k$) and a carrier density dependent effective mass (${m}^{*}\ensuremath{\sim}{m}_{0}{n}^{1/3}$). Such a strong carrier concentration dependent carrier effective mass results in the high Seebeck coefficient of $\mathrm{CuFe}{\mathrm{S}}_{2}$ compound under a large carrier density. The work demonstrates that quasilinearly dispersing bands can give strongly enhanced Seebeck coefficient, and could be useful in optimizing the properties of thermoelectric materials.
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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) | 0.002 | 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".