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Record W2214375763 · doi:10.1163/156855507781505110

Cationic polymerization using mixed cationic photoinitiator systems

2007· article· en· W2214375763 on OpenAlexaff
L. Kris Kostanski, Young‐Min Kim, John F. MacGregor, A. E. Hamielec

Bibliographic record

VenueDesigned Monomers & Polymers · 2007
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCationic polymerizationPhotoinitiatorPolymerizationPhotopolymerPolymer chemistryMaterials sciencePolymerChemistryMonomerOrganic chemistry

Abstract

fetched live from OpenAlex

Photoinitiated cationic polymerization is useful wherever the lack of oxygen inhibition, low shrinkage and formation of polymers with good adhesion properties are essential. Rates and conversions for cationic polymerizations of epoxycyclohexylmethyl-epoxycyclohexane carboxylate (ECHC) with various cationic photoinitiators and their blends were compared. Glass-transition temperatures of polymers were determined by dynamic mechanical analysis. When photopolymerizations of ECHC with initiator blends were compared with single-initiator systems, strong synergies leading to high polymerization rates and conversions were discovered for blends of diaryliodonium hexafluoroantimonate (IA) with tolylcumyliodonium tetrakis(pentafluorophenyl)borate (IB), and cumenecyclopentadienyliron(II) hexafluorophosphate with IA and IB. Mechanisms responsible for the discovered synergies are proposed.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

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