Guiding the Selection of Processing Additives for Increasing the Efficiency of Bulk Heterojunction Polymeric Solar Cells
Bibliographic record
Abstract
In bulk heterojunction (BHJ) polymeric organic solar cells (OSCs), the use of processing additives in the material formulation has emerged as a promising, cost‐effective, and widely applicable method for optimizing the phase separation between the donor (D) and acceptor (A) materials, thus increasing their efficiency. So far, however, there has been no systematic approach for identifying suitable processing additives for a given D:A system. A method based on the Hansen solubility parameters (HSPs) is proposed for guiding the selection of processing additives for a given D:A combination. The method is applied to the archetypical poly(3‐hexylthiophene) (P3HT) and [6,6]‐phenyl‐C61‐butyric acid methyl ester (PCBM) system. The HSPs of these materials are determined and used to define a set of numerical criteria that need to be satisfied by a processing additive in order for it to be effective in realizing a higher efficiency OSC. Applying the selection criteria results in the identification of three novel processing additives. OSCs made of these formulations demonstrate an increase in their short‐circuit current density (JSC) and power conversion efficiency (PCE). These results demonstrate the efficiency of these novel processing additives and show that the HSPs represent a useful tool to determine and explore new types of processing additives for BHJ‐OSCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".