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Record W2079763751 · doi:10.1002/app.29911

Synthesis of poly(methyl methacrylate) nanoparticles initiated by azobisisobutyronitrile using a differential microemulsion polymerization technique

2009· article· en· W2079763751 on OpenAlexaff
Chaiwat Norakankorn, Qinmin Pan, Garry L. Rempel, Suda Kiatkamjornwong

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAzobisisobutyronitrilePolymer chemistryPolymerizationDispersityMaterials scienceMicroemulsionMethyl methacrylateMolar mass distributionPoly(methyl methacrylate)PolymerChemical engineeringMonomerTacticityGlass transitionPulmonary surfactantComposite material

Abstract

fetched live from OpenAlex

Abstract Nanosized poly(methyl methacrylate) (PMMA) particles with a high molecular weight of 10 6 g mol −1 and a polydispersity index of about 1–2 were synthesized, for which 2,2′‐azobisisobutyronitrile was used as the initiator and a differential microemulsion polymerization technique was employed. The kinetics of the polymerization, the glass transition temperature, tacticity, the particle size distribution, and the morphology of the nanosized PMMA synthesized were investigated. The dependence of the number of the polymer particles ( N p ) and the number of the micelles ( N m ) on the concentration of the surfactant was discussed. The molecular weight distribution was found to be nearly constant over the polymerization time, which was attributed to the significance of micellar polymerization. The resultant nanosized PMMA has a rich syndiotactic configuration (53–57% rr triads) with a glass transition temperature of about 125°C. A beneficial operation condition was discovered where the conversion reached a maximum at a high monomer‐to‐water ratio. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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 score0.053
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.259
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.

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

Citations20
Published2009
Admission routes1
Has abstractyes

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