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Record W2079787171 · doi:10.1002/app.40328

Surface modifying oligomers used to functionalize polymeric surfaces: Consideration of blood contact applications

2014· article· en· W2079787171 on OpenAlexafffund
María Luisa López-Donaire, J. Paul Santerre

Bibliographic record

VenueJournal of Applied Polymer Science · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Toronto
FundersOntario Ministry of Economic Development and InnovationOntario Research Foundation
KeywordsMaterials scienceContact anglePolymerSurface modificationNanotechnologyCharacterization (materials science)CoagulationAdhesionAtomic force microscopyChemical engineeringComposite material

Abstract

fetched live from OpenAlex

ABSTRACT The surface modification of existing polymeric biomaterials represents a key strategy for improving the hemocompatibility in long‐ and short‐term biomedical materials without altering their bulk properties. Several techniques have been widely explored to generate surfaces that can prevent the activation of the coagulation system and lead to subsequent clot formation on the surfaces of polymeric blood contacting devices. In particular, strategies whereby the base polymer is blended with surface additives (SMAs) and surface modifying macromolecules (SMMs) are now recognized as practical and effective methods to improve surface polymeric materials. This review highlights the more recent advances in the synthesis of such additives and their blending with base polymers, with a specific focus on SMAs and SMMs with a molecular weight in the oligomeric range (

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.026
GPT teacher head0.286
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2014
Admission routes2
Has abstractyes

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