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Record W2014325147 · doi:10.1021/la504412v

Preparation and Post-Functionalization of Hyperbranched Polyurea Coatings

2015· article· en· W2014325147 on OpenAlexaff
Xiang Fei, Lia Amelia Tresna Wulan Asri, Oleksii Ivashenko, Petra Rudolf, Ton Loontjens

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsPolyureaSurface modificationMonomerContact anglePolymer chemistryFourier transform infrared spectroscopyCovalent bondX-ray photoelectron spectroscopyPolymerMaterials scienceChemistryChemical engineeringInfrared spectroscopyOrganic chemistryCoatingComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

Postfunctionalizable hyperbranched polyurea coatings were prepared by the bulk polycondensation of AB2 monomers on preactivated silicon substrates. As previously shown, AB2 monomers were prepared, comprising a secondary amino group (A) and two blocked isocyanates (B) connected by hexyl spacers, in a single step and in quantitative yields. Covalent anchoring of the coatings on substrates was accomplished by reacting the secondary amino group in the focal point of the polymers with the blocked isocyanates (BIs) of the covalently attached coupling agent. The BIs in the top layer of the coatings were storage-stable under ambient conditions but well-modifiable with amino- or hydroxyl-functional compounds on heating. Attachment of polyethylene glycol or perfluoro-1-decanol afforded hydrophilic or hydrophobic surfaces. Immobilization and quaternization of polyethylenimines yielded highly charged surfaces. The coatings were extensively characterized by a number of techniques, such as Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, atomic force microscopy, ellipsometry, and contact -angle measurements.

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 score0.061
Threshold uncertainty score0.213

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 communication0.0000.000
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 head0.019
GPT teacher head0.265
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

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

Citations8
Published2015
Admission routes1
Has abstractyes

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