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Record W2315665975 · doi:10.1021/am200241b

Preparation of Protein- and Cell-Resistant Surfaces by Hyperthermal Hydrogen Induced Cross-Linking of Poly(ethylene oxide)

2011· article· en· W2315665975 on OpenAlexafffund
Colin Bonduelle, Woon‐Ming Lau, Elizabeth R. Gillies

Bibliographic record

VenueACS Applied Materials & Interfaces · 2011
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsMaterials scienceEthylene oxidePolymerAdsorptionSurface modificationProtein adsorptionChemical engineeringOxideEthylenePolymerizationRadicalHydrogenPolymer chemistryOrganic chemistryCopolymerComposite materialChemistryCatalysis

Abstract

fetched live from OpenAlex

The functionalization of surfaces with poly(ethylene oxide) (PEO) is an effective means of imparting resistance to the adsorption of proteins and the attachment and growth of cells, properties that are critical for many biomedical applications. In this work, a new hyperthermal hydrogen induced cross-linking (HHIC) method was explored as a simple one-step approach for attaching PEO to surfaces through the selective cleavage of C-H bonds and subsequent cross-linking of the resulting carbon radicals. In order to study the effects of the process on the polymer, PEO-coated silicon wafers were prepared and the effects of different treatment times were investigated. Subsequently, using an optimized treatment time and a modified butyl polymer with increased affinity for PEO, the technique was applied to butyl rubber surfaces. All of the treated surfaces exhibited significantly reduced protein adsorption and cell growth relative to control surfaces and compared favorably with surfaces that were functionalized with PEO using conventional chemical methods. Thus HHIC is a simple and effective means of attaching PEO to non-functional polymer surfaces.

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)0.001
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 score0.005
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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

Citations22
Published2011
Admission routes2
Has abstractyes

Explore more

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