Performance Enhancement by Secondary Doping in PEDOT:PSS/Planar-Si Hybrid Solar Cells
Bibliographic record
Abstract
Solar cells depend on effectively absorbing light and converting it into electrical current. It is therefore essential to increase conductivity and to limit both reflection and parasitic absorbance to achieve higher photoconversion efficiency. Here, we examine the effect of post-treatment on the absorbance and conductivity of hybrid solar cells comprised of p-type poly(3,4-ethylene-dioxythiophene) poly(styrenesulfonate) (PEDOT:PSS) on an n-type silicon substrate. Three sets of cells based on pristine PEDOT:PSS film, cosolvent enhanced PEDOT:PSS film using ethylene glycol as a cosolvent, and post-treated PEDOT:PSS film using a novel 1:1 binary mixture of ethylene glycol and methanol have been studied. Markedly different film conductivities have been found for the pristine (∼0.8 S/cm), cosolvent added (637 S/cm), and post-treated films (1334 S/cm). The photoconversion efficiency obtained over a large set of samples (72 cells) was used to evaluate the cosolvent addition and post-treatment. Post-treatment is found to reproducibly provide films with not only the highest conductivities but also the highest efficiencies along with higher open-circuit voltage and fill factor but lower short-circuit current density when compared to those of the cosolvent added films. The decrease in the latter is attributed to the increase in absorbance in the PEDOT:PSS film. The present work illustrates the delicate challenge in improving the conductivity and carrier collection efficiency of the cells not at the expense of other properties such as absorption.
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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".