Structural colour QR codes for multichannel information storage with enhanced optical security and life expectancy
Bibliographic record
Abstract
Current schemes for encoding and decoding anticounterfeiting optical quick response (QR) codes involve miscellaneous challenges. The need for using multiple light sources to read out the wavelength-multiplexed data from optically encoded organic dyes, photoblinking from quantum dots, and autofluorescence from carbon dots are some typical examples. In order to address these restrictions, we exploited our previously devised nanoimprinting-exposure-thermal-treatment (NETT) data storage approach to present a new structural-colour-based regime for optical encoding of high-security QR codes. The angle-dependent readability of our diffraction-based nanostructures poses an enhanced optical security feature that can substitute the existing inefficient encoding strategies by eliminating the constraints associated with them. Additionally, in comparison with conventional optical encoding media, using the long-lasting photocrosslinked SU-8 in the NETT method considerably enhances the life expectancy of the proposed QR codes. Also, considering the rapid NETT-based Ni stamp origination method, which was previously introduced by our group, mass-generation of the proposed codes is feasible. Owing to the special optically variable effects provided by the nanostructures, duplication of our QR codes is very difficult. The colour code design, which embeds 766 characters in 2907 modules in red, green and blue channels, was generated and fabricated onto generic nanostructure arrays using the NETT process. The encoded information was successfully read out from the pattern using a broadband light source and a digital camera. Higher capacities are also deemed to be reachable by implementing image processing and machine learning algorithms to overcome in-channel module recognition and cross-channel interferences.
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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) | 0.000 | 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 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".