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Record W2134715410 · doi:10.2174/1876531901103010115

Adhesion Properties of Self-Polymerized Dopamine Thin Film

2011· article· en· W2134715410 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Open Surface Science Journal · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolydimethylsiloxaneContact angleAdhesionPolymerizationPolymerElastomerComposite materialAdhesiveThin filmLayer (electronics)Nanotechnology

Abstract

fetched live from OpenAlex

We report an experimental study of the adhesion properties of polydopamine thin films and their bonding behavior on polymer, glass and metal surfaces. Dopamine is able to adsorb onto all surfaces and self-polymerize into a thin hydrophilic film. Dynamic contact angle measurements revealed a large contact angle hysteresis between advancing and receding angles and a possible hydration layer when exposed to water. Polydopamine-coated surfaces in air are relatively inert having a low self-adhesion compared with polydimethylsiloxane elastomer surfaces, reflecting the nonconformal, glassy nature of polydopamine thin films. The dopamine aqueous solution was found able to bond two rigid surfaces (e.g. aluminum and glass) but it might not be suitable for joining soft or flexible polymer surfaces as polydopamine films are glassy and subject to internal cracks induced by the mismatch of elastic modulus. The research findings provide insights into the potential application of dopamine self-polymerization for adhesive bonding or joining of dissimilar materials.

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.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.071
GPT teacher head0.296
Teacher spread0.225 · 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