Fine-Tuning of Fluorinated Thieno[3,4-b]thiophene Copolymer for Efficient Polymer Solar Cells
Bibliographic record
Abstract
Novel low band gap fluorinated copolymers have been synthesized and characterized. The performance of inverted polymer solar cells made by these fluorinated copolymers blended with fullerene derivatives was also investigated. The studies of the effect of fluorine units on the electronic properties and the thin film morphologies of fluorinated copolymers demonstrated that an optimal window of 20–40% fluorine units coupled with thieno[3,4-b]thiopehene exists for a maximum hole mobility that correlated well with a dispersed interconnected morphology of the fluorinated copolymer, thus enhancing the performance of polymer solar cells. More fluorine units coupled with thieno[3,4-b]thiopehene adversely affected the phase morphology to coarsen that in turn reduced polymer solar cells performance. Nevertheless, the polymer solar cell performance of the highest fluorinated content polymer was twice as much as the nonfluorinated copolymer. These results indicate that the electronic properties as well as the thin film morphologies, not only in nanoscale but also in microscale, of electroactive and photoactive polymers are collectively very important for organic photovoltaic devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".