Hybrid Surface Acoustic Wave- Electrohydrodynamic Atomization (SAW-EHDA) For the Development of Functional Thin Films
Bibliographic record
Abstract
Conventional surface acoustic wave - electrostatic deposition (SAW-ED) technology is struggling to compete with other thin film fabrication technologies because of its limitation in atomizing high density solutions or solutions with strong inter-particle bonding that requires very high frequency (100 MHz) and power. In this study, a hybrid surface acoustic wave - electrohydrodynamic atomization (SAW-EHDA) system has been introduced to overcome this problem by integrating EHDA with SAW to achieve the deposition of different types of conductive inks at lower frequency (19.8 MHZ) and power. Three materials, Poly [2-methoxy-5-(2-ethylhexyloxy)-1, 4-phenylenevinylene] (MEH-PPV), Zinc Oxide (ZnO), and Poly(3, 4-ethylenedioxythiophene):Polystyrene Sulfonate ( PEDOT: PSS) have been successfully deposited as thin films through the hybrid SAW-EHDA. The films showed good morphological, chemical, electrical, and optical characteristics. To further evaluate the characteristics of deposited films, a humidity sensor was fabricated with active layer of PEDOT: PSS deposited using the SAW-EHDA system. The response of sensor was outstanding and much better when compared to similar sensors fabricated using other manufacturing techniques. The results of the device and the films' characteristics suggest that the hybrid SAW-EHDA technology has high potential to efficiently produce wide variety of thin films and thus predict its promising future in certain areas of printed electronics.
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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".