Quantitative analyses of enhanced thermoelectric properties of modulation-doped PEDOT:PSS/undoped Si (001) nanoscale heterostructures
Bibliographic record
Abstract
), resulting in a low power factor. Mixing PEDOT:PSS with nanostructured semiconductors can enhance the Seebeck coefficient and achieve an improved thermoelectric power factor. However, underlying mechanisms for those composite thermoelectric systems are scarcely understood so far. In this study, quantitative analyses on the electrical conductivity and Seebeck coefficient for the heterostructures of nanometer-thick PEDOT:PSS on single-crystal Si (001) on sapphire (SOS) are reported. The heterostructures have larger Seebeck coefficients up to 7.3 fold and power factors up to 17.5 fold relative to PEDOT:PSS. The electrical conductivity increased with decreasing combined thicknesses of PEDOT:PSS and Si, and the Seebeck coefficient increased with decreasing PEDOT:PSS thickness, which can be attributed to modulation doping caused by diffusion of holes from PEDOT:PSS into undoped Si. This hypothesis is supported by simulation per band alignment. The valence band offset between Si and PEDOT:PSS dominantly controls the electrical conductivity and Seebeck coefficient of the heterostructures. This study not only suggests mechanistic insights to increase the power factors of PEDOT:PSS-based composites but also opens the door for new strategies to enhance the thermoelectric efficiencies of heterostructured nanocomposite 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 | 0.000 | 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".