Highly Conducting Hybrid Silver-Nanowire-Embedded Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) for High-Efficiency Planar Silicon/Organic Heterojunction Solar Cells
Bibliographic record
Abstract
Embedding nanowires, such as silver nanowires (AgNWs), in a transparent conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) to enhance its conductivity is technologically important for improving the performances of devices comprising transparent conductive layers. Addition of nanowires in the highly conducting form of cosolvent (ethylene glycol) or mixed-cosolvent (ethylene glycol and methanol) modified PEDOT:PSS could change the nanowire structure and significantly alter the conductivity. Here, we report a simple method to embed AgNWs in PEDOT:PSS efficiently to improve its conductivity. By incorporating nanowires in the mixed cosolvent matrix prior to addition into PEDOT:PSS, this method preserves the structure of the nanowires while enabling conductivity enhancement. In contrast, the addition of AgNWs into cosolvent-premodified PEDOT:PSS leads to breaking of nanowires and conductivity impediment. The hybrid films with efficiently embedded AgNWs and mixed-cosolvent-modified PEDOT:PSS show a sheet resistance of 104 Ω/□, which is among the lowest ever reported for the as-deposited films, with conductivity enhancement of 33% relative to that of mixed-cosolvent-modified PEDOT:PSS. The resulting planar heterojunction solar cell (HSC) based on AgNW-embedded PEDOT:PSS exhibits a power conversion efficiency of greater than 15%. This demonstrates the importance of reducing sheet resistance by integrating nanowires into the PEDOT:PSS matrix as effective charge-transfer conduits interconnecting the highly conducting quinoid chains. The present approach to efficiently embed AgNWs in PEDOT:PSS could be readily extended to other nanowires or nanoparticles for improving the performance of PEDOT:PSS for applications in not just HSCs but indeed other electronic devices that require both transparent and highly conductive layers.
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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".