Nano-media: New nano-photofabric for rapid imprinting of color images and covert data storage
Bibliographic record
Abstract
We present the concept of `nano-media' as a novel carrier to imprint and display color images with embedded covert information. The key novelty of the proposed nanoscale technology is (1) the introduction of nano-substrate such as polymer (plastic), paper, glass, metal, or other tissue/fabric and (2) the imprinting processes. Nano-substrate consists of the pixelated nano-structures that are specially designed to display red, green and blue primary colors, and infrared radiation, and can be pre-fabricated on any substrate. The imprinting process activates the pixels according to the desired image that is transferred onto nano-substrate. The effective optical intensity of the pixelated nano-structures is tuned by an intensity control layer (ICL), which is patterned according to the desired color image and covert information. Using this technology, any given full-color image and/or covert data can be embedded on a prefabricated nano-substrate. This new technology makes possible to practically and efficiently use the nano-structures as a visual information display (next generation of holography) and as an high density and long term optical storage medium. In this paper, the concept, the design of the nano-media and the proof-of-concept experimental work are presented. We successfully produced full-color images with 1,270 pixels per inch (PPI) resolution using various ICLs on the fabricated nano-substrate. We also embedded covert information into the nano-media by patterning the intensity of infrared sub-pixels and successfully read the information using an infrared camera device. The texts, QR codes and photos were all tested as the covert information which was retrieved without any loss. The color images and stored data are of very high quality that can be controlled by the imprinting processes.
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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".