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Record W1996997090 · doi:10.1021/ma010006o

Nitroxide-Mediated Styrene Miniemulsion Polymerization

2001· article· en· W1996997090 on OpenAlexaff
Michael F. Cunningham, Min Xie, Kimberley B. McAuley, Barkev Keoshkerian, Michael K. Georges

Bibliographic record

VenueMacromolecules · 2001
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsXerox (Canada)Queen's University
Fundersnot available
KeywordsMiniemulsionNitroxide mediated radical polymerizationPotassium persulfateStyreneEmulsion polymerizationPolymerizationPolymer chemistryChemistryRadical polymerizationRadicalPersulfateRadical initiatorAqueous solutionCopolymerOrganic chemistryPolymerCatalysis

Abstract

fetched live from OpenAlex

Living radical polymerization of styrene was conducted in a miniemulsion using TEMPO and the water-soluble initiator potassium persulfate (KPS). The effects of initiator concentration and the TEMPO:KPS ratio on conversion, molecular weight distribution, and particle size were studied. The miniemulsion polymerizations exhibit similar characteristics to bulk living radical systems but with unique features attributable to the heterogeneous nature of the system. There is a strong interaction between the KPS concentration and the TEMPO:KPS ratio, and therefore the effects of changing either variable depend strongly on the value of the other variable. Initiator efficiencies are considerably higher than in conventional KPS-initiated styrene emulsion or miniemulsion polymerizations, while the average number of active radicals per particle (∼10 -2 ) is much lower. Aqueous-phase kinetics and nitroxide partitioning determine the number of chains initiated and therefore also affect the polymerization rate and molecular weight.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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 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

Citations60
Published2001
Admission routes1
Has abstractyes

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