Scalable Inkjet-Based Structural Color Printing by Molding Transparent Gratings on Multilayer Nanostructured Surfaces
Bibliographic record
Abstract
To enable customized manufacturing of structural colors for commercial applications, up-scalable, low-cost, rapid, and versatile printing techniques are highly demanded. In this paper, we introduce a viable strategy for scaling up production of custom-input images by patterning individual structural colors on separate layers, which are then vertically stacked and recombined into full-color images. By applying this strategy on molded-ink-on-nanostructured-surface printing, we present an industry-applicable inkjet structural color printing technique termed multilayer molded-ink-on-nanostructured-surface (M-MIONS) printing, in which structural color pixels are molded on multiple layers of nanostructured surfaces. Transparent colorless titanium dioxide nanoparticles were inkjet-printed onto three separate transparent polymer substrates, and each substrate surface has one specific subwavelength grating pattern for molding the deposited nanoparticles into structural color pixels of red, green, or blue primary color. After index-matching lamination, the three layers were vertically stacked and bonded to display a color image. Each primary color can be printed into a range of different shades controlled through a half-tone process, and full colors were achieved by mixing primary colors from three layers. In our experiments, an image size as big as 10 cm by 10 cm was effortlessly achieved, and even larger images can potentially be printed on recombined grating surfaces. In one application example, the M-MIONS technique was used for printing customizable transparent color optical variable devices for protecting personalized security documents. In another example, a transparent diffractive color image printed with the M-MIONS technique was pasted onto a transparent panel for overlaying colorful information onto one's view of reality.
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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) | 0.000 | 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 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".