A Paper‐Like Hydrogel for Versatile Information Encryption and Decryption Via Chemical‐Induced Phase Separation
Bibliographic record
Abstract
Abstract Information coding, recording, encryption, and decryption are of great importance in the field of anti‐counterfeiting, especially in the current AI information era. Herein, a paper‐like hydrogel composed of solely H‐bonded poly(vinyl alcohol) (PVA) and poly(n‐vinylcaprolactam) (PNVCL), namely VAPN, is developed for multiple ways of encryption and decryption based on chemical‐induced phase separation. It not only exhibits excellent ability of ink absorption and retention by the noncovalent H‐bonds and n−π* interactions and good mechanical strength but also maintains a negligible volume change during the phase separation that is crucial for the information fidelity. Given that the noncovalent interactions are the driving force to trigger the phase separation in the hydrogel, available chemical inks are numerous ranging from small molecules to polymers. Furthermore, together with thermally induced phase separation, the different dynamic processes of the association and dissociation between ink molecules and the hydrogel endow the latter with reversible information recording and self‐erasing, temporary or permanent, and customized encryption and decryption.
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".