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Record W3190904734 · doi:10.1002/vnl.21853

Poly(ε‐caprolactone)‐based additives: Plasticization efficacy and migration resistance

2021· article· en· W3190904734 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Vinyl and Additive Technology · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Science and PVC
Canadian institutionsCanadian General-Tower (Canada)McGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsPlasticizerMaterials scienceCaprolactoneElongationUltimate tensile strengthAlkylPhthalatePolymer chemistryChemical engineeringPolymerComposite materialChemistryOrganic chemistryPolymerization

Abstract

fetched live from OpenAlex

Abstract A family of poly(caprolactone) (PCL)‐based oligomeric additives was evaluated as plasticizers for poly(vinyl chloride) (PVC). We found that the entire family of additives, which consist of a PCL core, diester linker, and alkyl chain cap, were effective plasticizers that improve migration resistance. The elongation at break and tensile strength of the blends made with the PCL‐based additives were comparable to blends prepared with diisononyl phthalate (DINP), a plasticizer typically used industrially, and diheptyl succinate (DHPS), an alternative biodegradable plasticizer. Increasing concentration was found to decrease glass transition temperature ( T g ) and increase elongation at break, confirming their role as functional plasticizers. We found that all of the PCL‐based plasticizers exhibited significantly reduced leaching into hexanes compared to DINP and DHPS. The PCL‐based plasticizers with shorter carbon chain lengths reduced leaching more than those with longer carbon chain lengths.

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.

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)0.001
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 score0.022
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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