Effect of carbon nanotube incorporation into polythiophene-fullerene-based organic solar cells
Bibliographic record
Abstract
The effect of single-walled carbon nanotube (SWNT) incorporation within bulk heterojunction photovoltaic devices based on poly(3-hexylthiophene) − [6,6]-phenyl-C61-butyric acid methyl ester (P3HT−PCBM) (1:1 w/w) active layers was investigated. Both full-length and shortened SWNTs were introduced within the P3HT−PCBM layer at loadings in the range of 0−2 wt%. For full-length SWNTs, it was found that device efficiency decreased at all SWNT loading levels and annealing temperatures, which ranged from 80 to 225 °C. The highest average external efficiencies in the absence of SWNTs reached approximately 2%, while the best efficiencies in devices incorporating the full-length SWNTs only reached 1.3%. When shortened SWNTs were incorporated, device efficiency was unchanged upon annealing at 160 °C (average values of approximately 2%), but the efficiency improved by nearly 50%, relative to controls when devices were annealed at 70 °C. Active layer analysis by grazing incidence X-ray diffraction indicated that nanotubes did not increase polymer crystallinity. Knowing that shortened SWNTs are good hole conductors, it is postulated that the improved device efficiency is due to improved hole transport through the SWNTs in devices where the hole-transporting polymer has not been allowed to adopt its optimal morphology due to underannealing.
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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".