Scalable structural color printing using pixelated nanostructures in RGB primary colors
Bibliographic record
Abstract
Commercially available conventional color printing techniques mainly rely on patterning pigment-based pixels on a substrate. In recent years, structural colors have become increasingly important for their intrinsic advantages such as chemical stability, high resolution and color properties. However, to apply structural color pixels in printing color images for consumer-based demands remains a daunting challenge because such pixels usually require very high resolution patterning at a high speed and low cost. In this paper, we present novel color printing techniques based on micro-patterning of prefabricated nanostructure pixels in RGB primary colors. According to the micro-patterning techniques, the presented techniques are: a) solvent-free optical and thermal patterning of nanostructure pixels, b) photographic exposure through nanostructure color filters and c) inkjet printing of silver on nanostructures. These three presented techniques share some similar characteristics with popular conventional techniques, and can be considered as new-generation printing techniques evolved from their conventional counterparts. The preliminary results suggest that implementing the presented techniques, full-color images can be printed with much improved throughput than other nano-patterning techniques and imply these techniques can potentially be applied towards color production for general consumer use.
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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 | 0.001 |
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".