Deposited Nanoparticles Can Promote Air Clogging of Piezoelectric Inkjet Printhead Nozzles
Bibliographic record
Abstract
Piezoelectric inkjet printing is susceptible to printhead clogging when printing with inks that contain dispersed particles. This paper investigates the mechanisms by which 28-530 nm nanoparticle dispersions induce printhead clogging without forming large aggregates or thick deposited layers on printhead surfaces. Printing experiments were combined with nanoparticle deposition studies and with experiments where inks were pumped through printheads at a constant flow rate with a syringe pump. Submonolayer coverages of hydrophobic cationic polystyrene nanoparticles adhering to printhead surfaces promote rapid clogging by trapped air that enters from the nozzle opening. We propose that the deposited particles distort the shape of the ink/air meniscus, possibly causing air entrainment, and promote air bubble adhesion to the interior printhead surfaces. The printer's purge-blot cleaning procedure removes air clogs, but the clogs quickly reform when printing is resumed because the adsorbed nanoparticles are not removed by the cleaning procedure. Nondepositing anionic hydrophobic nanoparticles cause much less clogging, possibly because of filtration of trace large aggregates. Colloidal stability is a necessary but not sufficient criterion for ink dispersions; the ink particles must not adsorb onto the printhead surfaces. Thus, alternate surface chemistries for the printhead and ink particle surfaces may be required to print hydrophobic ink 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 | 0.001 |
| 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".