Solvent-free optical recording of structural colours on pre-imprinted photocrosslinkable nanostructures
Bibliographic record
Abstract
Abstract Optical digital information storage media, despite their ever-increasing storage capacity and data transfer rate, are vulnerable to the potential risk of turning inaccessible. For this reason, long-term eye-readable full-colour optical archival storage is in high demand for preserving valuable information from cultural, intellectual, and scholarly resources. However, the concurrent requirements in recording colours inexpensively and precisely, and preserving colours for the very long term (for at least 100 years), have not yet been met by existing storage techniques. Structural colours hold the promise to overcome such challenges. However, there is still the lack of an inexpensive, rapid, reliable, and solvent-free optical patterning technique for recording structural colours. In this paper, we introduce an enabling technique based on optical and thermal patterning of nanoimprinted SU-8 nanocone arrays. Using photocrosslinking and thermoplastic flow of SU-8, diffractive structural colours of nanocone arrays are recorded using ultra-violet (UV) exposure followed by the thermal development and reshaping of nanocones. Different thermal treatment procedures in reshaping nanocones are investigated and compared, and two-step progressive baking is found to allow the controllable reshaping of nanocones. The height of the nanocones and brightness of diffractive colours are modulated by varying the UV exposure dose to enable grey-scale patterning. An example of recorded full-colour image through half-tone patterning is also demonstrated. The presented technique requires only low-power continuous-wave UV light and is very promising to be adopted for professional and consumer archival storage applications.
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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 | 0.000 |
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".