Dispense Printing of Silver Flake Inks on Hydrophilic and Hydrophobic Surfaces
Bibliographic record
Abstract
In printed electronics, achieving precise deposition of conductive materials on challenging substrates like Teflon is critical. This study delves into dispense printing, a technique that offers precision and versatility for creating electronic patterns. The deposition of high‐viscosity silver flake inks on both hydrophobic surfaces is investigated, such as Teflon, and hydrophilic ones, highlighting the significant interplay between ink viscosity and substrate wettability. This interaction is key to controlling ink spreading, drying behavior, and, ultimately, printing success. Research explores the relationships between critical parameters such as nozzle height, dispense pressure, and print speed, aiming to enhance the quality and functionality of printed electronics. The printing process is analyzed through its distinct phases: extrusion from the nozzle, spreading on the substrate, and line shrinking during drying. This methodological approach allows one to pinpoint how each parameter specifically influences the printing outcome, particularly on challenging substrates like Teflon. By advancing the understanding of these dynamics, the study offers valuable theoretical insights and practical advancements for fabricating high‐quality, flexible electronics across diverse substrates. The findings underscore dispense printing's potential to meet the growing demand for flexible 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".