Random Polymerization Strategy Leads to a Family of Donor Polymers Enabling Well‐Controlled Morphology and Multiple Cases of High‐Performance Organic Solar Cells
Bibliographic record
Abstract
Developing high-performance donor polymers is important for nonfullerene organic solar cells (NF-OSCs), as state-of-the-art nonfullerene acceptors can only perform well if they are coupled with a matching donor with suitable energy levels. However, there are very limited choices of donor polymers for NF-OSCs, and the most commonly used ones are polymers named PM6 and PM7, which suffer from several problems. First, the performance of these polymers (particularly PM7) relies on precise control of their molecular weights. Also, their optimal morphology is extremely sensitive to any structural modification. In this work, a family of donor polymers is developed based on a random polymerization strategy. These polymers can achieve well-controlled morphology and high-performance with a variety of chemical structures and molecular weights. The polymer donors are D-A1-D-A2-type random copolymers in which the D and A1 units are monomers originating from PM6 or PM7, while the A2 unit comprises an electron-deficient core flanked by two thiophene rings with branched alkyl chains. Consequently, multiple cases of highly efficient NF-OSCs are achieved with efficiencies between 16.0% and 17.1%. As the electron-deficient cores can be changed to many other structural units, the strategy can easily expand the choices of high-performance donor polymers for NF-OSCs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".