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Record W4391157114 · doi:10.1002/adfm.202314510

A Paper‐Like Hydrogel for Versatile Information Encryption and Decryption Via Chemical‐Induced Phase Separation

2024· article· en· W4391157114 on OpenAlexafffund
Xin‐Shi Chen, Yue Zhao

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilFonds de recherche du QuébecCentre québécois sur les matériaux fonctionnels
KeywordsMaterials scienceEncryptionPolymerDissociation (chemistry)NanotechnologyMoleculePhase (matter)Self-healing hydrogelsChemical engineeringComputer sciencePolymer chemistryComposite materialChemistryOrganic chemistryComputer security

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.251
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2024
Admission routes2
Has abstractyes

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