Enhanced Thermoelectric Properties for PEDOT:PSS/Undoped Ge Thin‐Film Bilayered Heterostructures
Bibliographic record
Abstract
Abstract Modulation doping is one of the strategies to improve thermoelectric power factors of nanocomposites and thin‐film bilayered heterostructures by effectively increasing electrical conductivity. Here, it is reported that thin‐film heterostructures of heavily doped p‐type organic conducting polymer, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and undoped thin‐film Ge can enhance thermoelectric power factor by modulation doping. The maximum power factor and Seebeck coefficient of the bilayered heterostructures are 154 µW m−1 K−2 and 398 µV K−1, respectively, corresponding to 47‐fold and 41‐fold increases compared to those of bulk PEDOT:PSS and 64‐fold increase compared to power factor of undoped Ge. The enhancements in power factor and Seebeck coefficient are quantitatively described by the hole transfer from PEDOT:PSS to Ge, which takes into account the band alignment at the interface detected by Kraut's method. Agreement between the simulation and experiment results also implies predictability of thermoelectric performances of nanoscale bilayered heterostructures in general, when band offset, Fermi level, and individual electronic properties are available. This work can be further extended to predict performance of other nanoscale combinations of thermoelectric and other electronic materials in general.
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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) | 0.000 | 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".