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Record W2324475857 · doi:10.1021/bm300913q

Photo-Cross-Linked Poly(ethylene carbonate) Elastomers: Synthesis, in Vivo Degradation, and Determination of in Vivo Degradation Mechanism

2012· article· en· W2324475857 on OpenAlexafffund
L. A. Cornacchione, Bo Qi, John Bianco, Z. Zhou, Brian G. Amsden

Bibliographic record

VenueBiomacromolecules · 2012
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElastomerDegradation (telecommunications)Ethylene carbonatePhotoinitiatorIn vivoPolymerPolymer chemistryChemistryEthylenePolymer degradationMaterials scienceSuperoxidePhotochemistryBiophysicsOrganic chemistryCatalysisMonomer

Abstract

fetched live from OpenAlex

Low-molecular-weight poly(ethylene carbonate) diols of varying molecular weight were generated through catalyzed thermal degradation of high-molecular-weight poly(ethylene carbonate). These polymers were then end functionalized with acrylate groups. The resulting α,ω-diacrylates were effectively photo-cross-linked upon exposure to long-wave UV light in the presence of a photoinitiator to yield rubbery networks of low sol content. The degree of cross-linking effectively controlled the in vivo degradation rate of the networks by adherent macrophages; higher cross-link densities yielded slower degradation rates. The cross-link density did not affect the number of adherent macrophages at the elastomer/tissue interface, indicating that cross-linking affected the susceptibility of the elastomer to degradative species released by the macrophages. The reactive species likely responsible for in vivo degradation appears to be superoxide anion, as the in vivo results were in agreement with in vitro degradation via superoxide anion, while cholesterol esterase, known to degrade similar poly(alkylene carbonate)s, had no affect on elastomer degradation.

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 score0.015
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.251
Teacher spread0.232 · 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

Citations17
Published2012
Admission routes2
Has abstractyes

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