Highly Conductive and Wettable PEDOT:PSS for Simple and Efficient Organic/c‐Si Planar Heterojunction Solar Cells
Bibliographic record
Abstract
The key for fabricating efficient organic/n‐Si planar heterojunction solar cells is the organic semiconductor layer, which governs key steps in photocarrier harvesting such as charge carrier separation and extraction. Typical organic semiconductors, however, are inadequate to yield good device performance due to their low electrical conductivities and undesirable work functions. Herein, a method is proposed to boost charge carrier concentration in poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) from 1018 to 1022 cm−3 by combining solvent p‐doping and DMF treatment. This high carrier concentration induces significant band bending in the Si substrate and thus leads to an extended inversion zone near an organic/Si heterojunction. It is found that this type of heterojunction creates a broad built‐in electric field and effective interface passivation. The highly conductive PEDOT:PSS enables a simple and efficient organic/n‐Si planar heterojunction solar cell with a power conversion efficiency exceeding 13% without using complex light‐trapping structures. This power efficiency is comparable with the highest value reported to date. Herein, the possibility of making effective organic/n‐Si planar heterojunction solar cells simply by applying a layer of extremely conductive organic coating is demonstrated.
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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 | 0.001 |
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".