Limonene as a Green Solvent for Depositing Thin Layers of Molecular Electronic Materials with Controlled Interdiffusion
Bibliographic record
Abstract
In the fabrication of thin-film electronic devices such as solar cells, molecular components are often deposited from solution by spin-coating. Toxic chlorinated solvents are widely used in this process, and environmentally benign alternatives are desirable. Ideally, these alternatives should be inexpensive, derived from renewable sources, and able to dissolve typical molecular electronic materials. Moreover, they should allow the creation of thin films in which the interdiffusion of different components can be controlled to optimize the performance of the resulting device. In an initial survey, we have examined the deposition of layers of poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C 61 -butyric acid methyl ester (PCBM), which are benchmark molecular semiconductors that have been widely used together in solar cells as electron donor and acceptor, respectively. We have found that solutions in limonene (which is a green solvent produced by citrus fruits and other plants) are particularly effective for depositing PCBM on P3HT to create bilayer architectures. Wetting of the P3HT underlayer is improved when limonene is used in place of standard chlorinated solvents, and interdiffusion of PCBM and P3HT is reduced, as established by TOF-SIMS measurements. Our results underscore the potential of unconventional green solvents such as limonene for use in fabricating molecular thin-film electronic devices.
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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".