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Record W2622565699 · doi:10.1002/app.45308

Single‐ and two‐step procedures of <scp>AGET</scp> emulsion <scp>ATRP</scp> of methyl methacrylate in a well‐mixed batch reactor

2017· article· en· W2622565699 on OpenAlexafffund
Kishor Regmi, Mehrab Mehrvar, Ramdhane Dhib

Bibliographic record

VenueJournal of Applied Polymer Science · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaRyerson University
KeywordsPolymer chemistryMethyl methacrylateAtom-transfer radical-polymerizationEmulsion polymerizationMonomerChemistryPolymerizationBromideRadical polymerizationPolymerChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT This study investigates the atom transfer radical emulsion polymerization of methyl methacrylate in a 2 L well‐mixed stirred batch reactor using activators generated by electron transfer as the initiation technique. The polymerization was carried out with ethyl‐2‐bromoisobutyrate as the initiator, copper bromide with 4,4′‐di‐5‐nonyl‐2,2′‐bipyridine as the catalyst system, Brij 98 as the surfactant, and ascorbic acid as the reducing agent. The reaction was carried out at constant temperature in the range of 50 to 70 °C under a blanket of nitrogen to minimize the presence of air in the system. Polymerizations were carried out according to single‐step and two‐step procedures. The coagulation was found to be a major problem, especially at high monomer conversion. However, adding more surfactant and lowering the reaction temperature weakened the effect of the coagulation but at the expense of the low monomer conversion. Measurement of molecular weight distribution and Đ using gel permeation show that the two‐step techniques produced polymers with living features of atom transfer radical emulsion polymerization much better than those in the single‐step procedure. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 45308.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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.

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

Citations12
Published2017
Admission routes2
Has abstractyes

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