The Mixed Alloyed Chemical Composition of Chloro-(chloro)<sub><i>n</i></sub>-Boron Subnaphthalocyanines Dictates Their Performance as Electron-Donating and Hole-Transporting Materials in Organic Photovoltaics
Bibliographic record
Abstract
Chloro(chloro) n boron subnaphthalocyanine (Cl–Cl n BsubNc) from a commercial source and two synthetic routes were each tested as electron-donating and hole-transporting materials in planar as well as bulk heterojunction (PHJ and BHJ) organic photovoltaic (OPV) devices. We have previously reported that each Cl–Cl n BsubNc sample is a mixed alloyed composition, wherein each has a varying degree of bay-position chlorination. We have determined that increasing bay chlorination has a beneficial effect on the fill factor of PHJs. Comparison between this new and our past OPV data sets, which utilized the same set of Cl–Cl n BsubNcs as electron acceptors and transporters, reveals that the increase of fill factor and performance is likely due to improved exciton transport and higher levels of bay-position chlorination. While we identify two possible mechanisms for this, further studies will be required to determine whether the phenomenon is driven by decreased radiative relaxation or due to enhanced thermal hopping from a narrower density of states. We conclude that the usage of Cl–Cl n BsubNc with higher levels of bay-position chlorination, achieved through the “nitrobenzene process,” is likely to result in higher-performance OPVs.
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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 | 0.000 | 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".