Inkjet Printing on Hydrophobic Surface: Practical Implementation of Stacked Coin Strategy
Bibliographic record
Abstract
While inkjet printing on many hydrophilic surfaces is achieved through control of drop spacing and droplet deposition delay, the same for hydrophobic substrates proves challenging. Low surface energies of hydrophobic surfaces prevents intact and uniform lines of low‐viscosity ink to form. In this article, the stacked coin printing strategy used for hydrophilic surfaces, is adapted for hydrophobic surfaces. Stacked coin morphology is seen when droplet deposition time between two sequentially deposited droplets is longer than the evaporation time of the first droplet. On hydrophobic surfaces, the parameter window for successful printing is smaller than on hydrophilic surfaces, thus an investigation is needed to implement this methodology. Experiments were conducted using an inkjet printer with variable stage speed and stage temperature. Silver nanoparticle ink was used to print on Teflon–AF substrates. We identified the following regimes: isolated droplets, isolated multi‐droplets, broken line, true stacked coin, and delamination. The relationship between substrate temperature, drop spacing, and droplet deposition delay controls the occurence of each regime. In this study, 180 °C was identified as the critical temperature for instantaneous drying of the studied ink, and a maximum drop spacing of 20 μm to print continuous lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".