An additive manufacturing approach based on electrohydrodynamic printing to fabricate P3HT:PCBM thin films
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) enables the production of high value and high performance components with applications from aerospace to biomedical fields. We report here on the fabrication of poly(3-hexylthiophene): phenyl-C61-butyric acid methyl ester (P3HT:PCBM) thin films through the electrohydrodynamic atomization (EHDA) process and its integration as absorber layer for organic solar cells. Prior to the film fabrication, the optimization of the process was carried out by developing the operating envelope for the P3HT:PCBM ink to determine the optimal flow rate and the appropriate applied voltage to achieve a stable-cone deposition mode. The EHDA printed thin-film’s topography, morphology and optical properties were systematically analyzed. The root-mean-square roughness was found to vary significantly with the annealing temperature and the flow rate and ranged from 1.938 to 3.345 nm. The estimated film mass and thickness were found between 3.235 and 23.471 mg and 597.5 nm to 1.60 µm, respectively. The films exhibited a broad visible absorption spectrum ranging from ~ 340 to ~ 600 nm, with a maximum peak λmax located at ~ 500 nm. As the annealing temperature and the flow rate were increased, discernible alterations in the PCBM clusters were consequently observed in the blends of the film and the size of the PCBM clusters has decreased by 3% while the distance between them was highly reduced by as much as 82%.
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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".