Defect‐Minimized PEDOT:PSS/Planar‐Si Solar Cell with Very High Efficiency
Bibliographic record
Abstract
Hybrid solar cells made of a p‐type conducting polymer, poly(3,4‐ethyl thiophene):polystyrenesulfonate (PEDOT:PSS), on Si have gained considerable interest in the fabrication of cost‐effective high‐efficiency devices. However, most of the high power conversion efficiency (PCE) performances have been obtained from solar cells fabricated on surface‐structured Si substrates. High‐performance planar single‐junction solar cells have considerable advantages in terms of processing and cost, because they do not require the complex surface texturing processes. The interface of single‐junction solar cells can critically influence the performance. Here, we demonstrate the effect of adding different surfactants in a co‐solvent‐optimized PEDOT:PSS polymer, which, in addition to acting as a p‐layer and as an anti‐reflective coating, also enhances the device performance of a hybrid planar‐Si solar cell. Using time‐of‐flight secondary ion mass spectrometry, we conduct three‐dimensional chemical imaging of the interface, which enables us to characterize the micropore defects found to limit the PCE. Upon minimizing these micropore defects with the addition of optimized amounts of fluorosurfactant and co‐solvent, we achieve a PEDOT:PSS/planar‐Si cell with a record high PCE of 13.3% for the first time. Our present approach of micropore defect reduction can also be used to improve the performance of other organic electronic devices based on PEDOT:PSS.
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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 | 0.001 |
| 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".