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Record W2021165844 · doi:10.1021/la063012s

Development of Dextran-Derivative Arrays To Identify Physicochemical Properties Involved in Biofouling from Serum

2007· article· en· W2021165844 on OpenAlexaff
Emmanuelle Monchaux, Patrick Vermette

Bibliographic record

VenueLangmuir · 2007
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAdsorptionProtein adsorptionPolymerX-ray photoelectron spectroscopyChemical engineeringBiofoulingDextranEthylene oxideSurface plasmon resonanceChemistryEthylene glycolMoleculeOxideMaterials scienceNanotechnologyChromatographyOrganic chemistryNanoparticleCopolymerMembrane

Abstract

fetched live from OpenAlex

To control protein adsorption on surfaces, low-fouling polymer coatings such as poly(ethylene oxide) (PEG or PEO) and polysaccharides are used. Their ability to resist protein adsorption is related to the layer structure, hence the immobilization mode. A polymer array technology was developed to study the structural diversity of carboxymethyl dextran (CMD) layers, whose immobilization conditions were varied. CMD arrays were analyzed by X-ray photoelectron spectroscopy (XPS) and by atomic force microscopy (AFM) colloidal probe force measurements. Serum protein adsorption was studied directly on the CMD arrays using surface plasmon resonance (SPR) microscopy. Physicochemical characterization revealed that pinning density regulates surface coverage and the amount of adsorbed molecules, and that salt concentration influences the surface structure of the charged polymer, forming extended or short layers. Protein adsorption experiments from serum showed that repulsive CMD layers are dense, with extended flexible chains. The present study underlines the usefulness of polymer arrays to study structural diversity of thin graft layers and to relate their physicochemical properties to their resistance to nonspecific protein adsorption.

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 score0.031
Threshold uncertainty score0.394

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 head0.058
GPT teacher head0.321
Teacher spread0.263 · 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

Citations35
Published2007
Admission routes1
Has abstractyes

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