Customized nanomanufacturing of structural color images using inkj et on nanostructured foils
Bibliographic record
Abstract
Structural colors based on optical nanostructures possess appealing advantages compared to conventional ink- or pigment-based colors. To apply structural colors in commercial products, one key challenge is the throughput of the nanomanufacturing technique for printing any custom-input color images in large area. Our recent work has demonstrated an up-scalable structural color printing technique named multilayer molded-ink-on-nanostructured-surface (M-MIONS) printing, which holds the promise for customized nanomanufacturing of structural colors for broad-range commercial applications. In this work, we present our recent progress in scaling up M-MIONS printing. We implemented low-cost nanostructured thin foils manufactured from industrial roll-to-roll nanoimprint as substrates. A regular desktop inkjet printer was implemented to rapidly print dielectric nanoparticle inks onto the subwavelength gratings on the foils. An office-use thermal laminator and regular hot-lamination films were applied to achieve the index-matching lamination. In addition, we significantly shortened the post-printing baking and also eliminated the time-consuming surface chemical treatment of the substrates. With these modifications, the production speed was significantly boosted by multiple times and the average material cost was reduced into a fraction, compared to our previous M-MIONS printing process. In our experiments, we successfully manufactured full-color patterns in the size of 100 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the practical production speed can reach more than 10 images per hour. This progress paves the way towards industrial-scale printing of structural colors for commercial applications.
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 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) | 0.001 | 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 teacher head, 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".