Effect of Heteroatom and Doping on the Thermoelectric Properties of Poly(3‐alkylchalcogenophenes)
Bibliographic record
Abstract
Abstract This study reports on the thermoelectric properties of poly(3‐alkylchalcogenophene) thin films (500 nm) as a function of heteroatom (sulfur, selenium, tellurium), and how these properties change with dopant (ferric chloride) concentration. UV–vis–NIR spectroscopy shows that polaronic charge carriers are formed upon doping. Poly(3‐alkyltellurophene) (P3RTe) is most easily doped followed by poly(3‐alkylselenophene) (P3RSe) and poly(3‐alkylthiophene) (P3RT), where R = 3,7‐dimethyloctyl chain is the pendant alkyl group. Thermoelectric properties vary as functions of the heteroatom and doping level. At low dopant concentrations (≈1 × 10 −3 m ), P3RTe shows the highest power factor of 10 µW m −1 K −2 , while, at higher dopant concentrations (≈5 × 10 −3 m ), P3RSe shows the highest power factor of 13 µW m −1 K −2 . Most notably, it is found that the measured properties are consistent with Mott's polaron hopping model and not consistent with other transport models. Additionally, temperature‐dependent conductivity measurements show that for a given dopant concentration, the activation energies for electronic transport decrease as the heteroatom is changed from sulfur to selenium to tellurium. Overall, this work presents a systematic study of poly(chalcogenophenes) and indicates the potential of polymers beyond P3HT by tuning the heteroatom and doping level for optimized thermoelectric performance.
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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".