A Nanoparticle Approach To Control the Phase Separation in Polyfluorene Photovoltaic Devices
Bibliographic record
Abstract
Polymer solar cell devices with nanostructured blend layers have been fabricated using single- and dual-component polymer nanospheres. Starting from an electron-donating and an electron-accepting polyfluorene derivative, PFB and F8BT, dissolved in suitable organic solvents, dispersions of solid particles with mean diameters of ca. 50 nm, containing either the pure polymer components or a mixture of PFB and F8BT in each particle, were prepared with the miniemulsion process. Photovoltaic devices based on these particles have been studied with respect to the correlation between external quantum efficiency and layer composition. It is shown that the properties of devices containing a blend of single-component PFB and F8BT particles differ significantly from those of solar cells based on blend particles, even for the same layer composition. Various factors determining the quantum efficiency in both kinds of devices are identified and discussed, taking into account the spectroscopic properties of the particles. An external quantum efficiency of ca. 4% is measured for a device made from polymer blend nanoparticles containing PFB:F8BT at a weight ratio of 1:2 in each individual nanosphere. This is among the highest values reported so far for photovoltaic cells using this material combination.
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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 | 0.001 | 0.000 |
| 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".