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Record W2103212270 · doi:10.1039/c5nr06460k

A degradable polydopamine coating based on disulfide-exchange reaction

2015· article· en· W2103212270 on OpenAlexaff
Daewha Hong, Hojae Lee, Beom Jin Kim, Taegyun Park, Ji Yu Choi, Matthew Park, Juno Lee, Hyeoncheol Cho, Seok‐Pyo Hong, Sung Ho Yang, Sun Ho Jung, Sung‐Bo Ko, Insung S. Choi

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsCoatingAdhesiveDegradation (telecommunications)Materials scienceDisulfide bondCleavage (geology)Substrate (aquarium)Covalent bondMoleculeNanotechnologyChemical engineeringChemistryOrganic chemistryComposite materialComputer scienceBiochemistry

Abstract

fetched live from OpenAlex

Although the programmed degradation of biocompatible films finds applications in various fields including biomedical and bionanotechnological areas, coating methods have generally been limited to be substrate-specific, not applicable to any kinds of substrates. In this paper, we report a dopamine derivative, which allows for both universal coating of various substrates and stimuli-responsive film degradation, inspired by mussel-adhesive proteins. Two dopamine moieties are linked together by the disulfide bond, the cleavage of which enables the programmed film degradation. Mechanistic analysis of the degradable films indicates that the initial cleavage of the disulfide linkage causes rapid uptake of water molecules, hydrating the films, which leads to rapid degradation. Our substrate-independent coating of degradable films provides an advanced tool for drug delivery systems, tissue engineering, and anti-fouling strategies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.044
GPT teacher head0.290
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2015
Admission routes1
Has abstractyes

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