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Record W2014152784 · doi:10.1021/ma802006c

Modeling of Branching and Gelation in RAFT Copolymerization of Vinyl/Divinyl Systems

2008· article· en· W2014152784 on OpenAlexaff
Rui Wang, Yingwu Luo, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueMacromolecules · 2008
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComonomerBranching (polymer chemistry)Chain transferPolymer chemistryRaftCopolymerMonomerChemistryPolymerizationRadical polymerizationGel pointReversible addition−fragmentation chain-transfer polymerizationMolar mass distributionOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

The reversible addition−fragmentation chain transfer radical polymerization (RAFT) with branching/cross-linking is theoretically investigated on the basis of the method of moments. The system considered consists of the copolymerization of vinyl monomer in the presence of a small amount of divinyl comonomer. It is found that the gel point is significantly postponed by increasing the RAFT agent concentration. Flory’s criterion, ρ r w,0 = 1, is found to be satisfied at the gel point in the RAFT cross-linking process regardless of the unequal reactivities of vinyl/divinyl monomers in the absence of cyclization. The gel conversion can be analytically expressed and is determined by the polymerization recipe and the relative reactivities of various double bonds. The gel point is postponed by the presence of intramolecular cyclization, and its effect becomes significant in a dilute polymerization system. Branching distribution is found to be very broad with a large fraction of linear primary and slightly branched chains. By the introduction of the dependence of the reactivity of the pendant double bond on the local heterogeneity, the branching distribution becomes narrower and can be fine-tuned.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations82
Published2008
Admission routes1
Has abstractyes

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