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Record W1968952512 · doi:10.1002/mren.200800011

Kinetics and Modeling of Vinyl Acetate Graft Polymerization from Poly(ethylene glycol)

2008· article· en· W1968952512 on OpenAlexaff
Xiao Xiang Zhu, Bo‐Geng Li, Linbo Wu, Yougui Zheng, Shiping Zhu, Klaus‐Dieter Hungenberg, Stefan Müssig, Birgit Reinhard

Bibliographic record

VenueMacromolecular Reaction Engineering · 2008
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
FundersZhejiang UniversityNational Natural Science Foundation of China
KeywordsPolymer chemistryPEG ratioPolymerizationKineticsEthylene glycolCopolymerGraftingMolar mass distributionChain transferChemistryVinyl acetateMaterials scienceChemical engineeringRadical polymerizationPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A kinetic model for the graft polymerization of VAc from PEG was developed using the method of moments. Experiments were carried out to verify the model. The effect of various parameters, such as initiator concentration, temperature, and PEG molecular weight on the polymerization kinetics was examined. Polymerization rate, grafting efficiency, graft copolymer molecular weight, and PEG grafted ratio were measured. The model was in good agreement with the experimental data. No gel effect was observed at the studied PEG/VAc weight ratio of 1:1. The chain transfer constant to PEG was correlated to be $C_{{\rm PEG}} = 0.139\;\exp ( - 11\,200/RT)$ . The model was also applied in a semi‐batch reaction and compared with the experimental results. magnified image

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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