From Invisible to Visible: Pressure‐Responsive Photonic Crystals for Advanced Information Encryption
Bibliographic record
Abstract
Abstract Counterfeiting has emerged as an increasingly severe global concern, posing significant threats to the security of individuals and society. In this study, a series of multilayered information‐encrypted composite materials is developed by embedding a chiral nematic cellulose nanocrystal ( cn ‐CNC) film inside a shape‐memory polymer (SMP) matrix to produce pressure‐responsive photonic crystals. The design also incorporates a cellulose acetate film, as a thermoset information carrier, and graphene oxide (GO), as a secrecy filter. In its initial state, the composite shows no information, while a pattern becomes visible to reveal the information upon hot‐pressing, due to the region‐dependent differences in its photonic responsiveness caused by the rigid pattern. A discernible mark remains even after recovery, serving as tangible evidence of the decrypted information. Moreover, the high color fidelity of these materials is confirmed using reflectance spectroscopy and color analysis over multiple cycles, demonstrating their potential in cyclic information encryption. By harnessing thermal and mechanical stimuli for anti‐counterfeiting purposes, these composites diversify the strategies available for information encryption.
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 | 0.000 |
| 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".