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Record W2290724349 · doi:10.1002/anie.201510319

Nanomechanics of Poly(catecholamine) Coatings in Aqueous Solutions

2016· article· en· W2290724349 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

VenueAngewandte Chemie International Edition · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningMinistry of Oceans and Fisheries
KeywordsNanomechanicsAqueous solutionMaterials scienceCatecholamineComposite materialNanotechnologyChemistryAtomic force microscopyOrganic chemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Mussel-inspired self-polymerized catecholamine coatings have been widely utilized as a versatile coating strategy that can be applied to a variety of substrates. For the first time, nanomechanical measurements and an evaluation of the contribution of primary amine groups to poly(catecholamine) coatings have been conducted using a surface-forces apparatus. The adhesive strength between the poly(catecholamine) layers is 30-times higher than that of a poly(catechol) coating. The origin of the strong attraction between the poly(catecholamine) layers is probably due to surface salt displacement by the primary amine, π-π stacking (the quadrupole-quadrupole interaction of indolic crosslinks), and cation-π interactions (the monopole-quadrupole interaction between positively charged amine groups and the indolic crosslinks). The contribution of the primary amine group to the catecholamine coating is vital for the design and development of mussel-inspired catechol-based coating 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)0.000
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 score0.053
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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