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Record W2021136512 · doi:10.1002/mren.200600040

Recent Advances in the Study of Multifunctional Initiators in Free Radical Polymerizations

2007· article· en· W2021136512 on OpenAlexaff
M. J. Scorah, Ramdhane Dhib, Alexander Penlidis

Bibliographic record

VenueMacromolecular Reaction Engineering · 2007
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsBranching (polymer chemistry)PolystyrenePolymer chemistryMonomerMethyl methacrylatePolymerRadical polymerizationPolymerizationMaterials scienceMethacrylateChemical engineeringPolymer scienceChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract An overview of a systematic investigation of a tetrafunctional peroxide initiator's behaviour is presented. The study focuses on three main areas of research: kinetic experiments, polymer characterization and modelling efforts. The kinetic investigation compared the behaviour of the tetrafunctional initiator (JWEB50) to that of a monofunctional counterpart (TBEC) for a variety of monomers. Although higher rates of polymerization were generated with JWEB50 for all monomers investigated, switching from a mono‐ to a tetrafunctional initiator actually decreased the polymer molecular weight for methyl methacrylate. While chromatographic characterization methods were able to detect branching in polystyrene samples produced with JWEB50, this was not the case for poly(methyl methacrylate). However, evidence of branching was clearly observed for both polystyrene and PMMA when rheological methods were employed. In order to explain the experimental results, a mathematical model was developed. Through its use, the concentration and chain length of various polymer structures (i.e., linear, star or coupled stars) was found to depend upon monomer type and reaction conditions. 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 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)0.000
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 score0.096
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.235
Teacher spread0.227 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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