Theoretical studies on the thermopower of semiconductors and low-band-gap crystalline polymers
Bibliographic record
Abstract
A numerical procedure has been used for the prediction of the Seebeck coefficient of a crystalline material based on its electronic band structure with the goal of testing this approach on the simple polymers polythiophene and polyaminosquaraine. The investigation of several representative materials, including the crystalline solids $\ensuremath{\beta}\text{\ensuremath{-}}{\mathrm{Zn}}_{4}{\mathrm{Sb}}_{3}$ and $\mathrm{Au}{\mathrm{In}}_{2}$ and these polymers, under ambient or external pressure conditions, indicates that Seebeck coefficients can be calculated within the rigid-band and constant-relaxation-time approximations. The results are in semiquantitative agreement with experiment and provide a basic understanding of the mechanisms for thermopower. These theoretical results together with previous similar studies show that band-structure calculations can be used to guide the rational design of high-performance thermoelectric materials. We also suggest that appropriate and specially engineered doped low-band-gap polymers may be promising candidate materials for thermoelectric applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".