Distribution of inkjet ink components via ToF‐SIMS imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To optimize the performance of the inkjet printing process it is of significant importance to have greater understanding of the spatial arrangement of not only ink colourant, but also other ink components such as surfactant, solvent and/or cosolvent. In this work, the capabilities of Time‐of‐Flight (ToF) SIMS are applied to study the spreading (xy distribution) of a custom inkjet ink formulation, containing cationic crystal violet dye, ethoxylated surfactant and ink vehicle/solvent marked by lithium salt on uncoated and coated papers. High spatially resolved images obtained by ToF‐SIMS clearly illustrate differentiation of individual ink components, with irregular spreading on uncoated paper leading to poor edge definition, and as a result, poor print quality. ToF‐SIMS images and distribution profiles of ink components on ‘best’ and ‘worst’ commercial paper samples show that the cationic dye is preferentially adsorbed by both substrates, colocalizing with the surfactant. However, the solvent, marked by lithium salt, spreads 20% more than cationic dye on coated paper, and 25% more than the dye on uncoated paper. The variability in preferential absorption of ink components due to morphology, chemistry and topography of paper, may be taken as an indication of print quality. Copyright © 2010 John Wiley & Sons, Ltd.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it