Determination of Solvent Systems for Blade Coating Thin Film Photovoltaics
Bibliographic record
Abstract
With lab‐scale solution‐processed thin film photovoltaic (TFPV) devices attaining market relevant efficiencies, the demand for environmentally friendly and scalable deposition techniques is increasing. Replacing toxic halogenated solvents is a priority for the industrialization of solution‐processed TFPV. In this work, a generalized five‐step process is presented for fabricating high‐performance devices from nonhalogenated inks. Resulting from this process, several new solvent systems are introduced based on thiophene, tetralin, 1,2,4‐trimethylbenzene, o ‐xylene, and anisole for blade coating of three different diketopyrrolopyrrole‐based (pDPP5T‐2, pPDPP5T‐2S, and P390) bulk heterojunctions applied in organic photovoltaic devices. Devices based on pDPP5T‐2S and P390 attain 5.6% and 6.1% efficiency, respectively, greater than the efficiency either material reached when processed from the halogenated solvent system commonly used. These processes are implemented without post‐deposition annealing treatments or additives. The Hansen solubility parameters of the pDPP5T‐2 material are obtained, and are used, along with wettability data on a variety of substrates, to determine optimum solvent combinations and ratios for deposition. This generalized five‐step process results in new nonhalogenated solvent pathways for the scalable deposition of thin film photovoltaic materials.
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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) | 0.000 | 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".