(Invited) Development of Tailor Made Donor-Acceptor Pairs for Fullerene-Free Organic Solar Cells
Bibliographic record
Abstract
Organic solar cells (OCS) are now an established clean energy technology.1 These photovoltaic devices can be printed at low temperatures onto large area, flexible substrates, and can be made semi-transparent, making them a low-cost alternative to traditional inorganic devices, and can be utilized in many niche applications. Current state-of-the art relies on a polymer-fullerene bulk-heterojunction active layer, with power conversion efficiencies reaching 10%.2 While promising, certain limitations have prevented large scale commercialization. In an effort to advance OCS, the development of low-cost and sustainable active layer materials that can be mass-produced and yield high performance devices is essential. One area of our research at Dalhouise University centers on the development of small molecue based organic p-conjugated materials to replace the common fullerene acceptors.3 This talk will highlight our materials design strategy and discuss key thin-film characterization data and solar cell performance. (1) Su, Y.-W.; Lan, S.-C.; Wei, K.-H. Mater. Today 2012, 15, 554. (2) Li, G.; Zhu, R.; Yang, Y. Nat. Photonics 2012, 6, 153. (3) Eftaiha, A. F.; Hill, I. G.; Sun, J.-P.; Welch, G. C. J. Mater. Chem. A 2014, 2, 1201.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".