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Record W2078521774 · doi:10.1002/pola.21635

Reversible‐addition fragmentation chain transfer radical emulsion polymerization by a nanoprecipitation process

2006· article· en· W2078521774 on OpenAlexaff
Andrea R. Szkurhan, Takahito Kasahara, Michael K. Georges

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2006
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmulsion polymerizationChain transferPolymer chemistryStyreneRadical polymerizationPolymerizationMonomerPolymerPrecipitation polymerizationChemistryEmulsionAqueous solutionTransfer agentChemical engineeringCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Polymerizations of styrene under emulsion reversible‐addition fragmentation chain transfer polymerization conditions are reported. Using a recently developed nanoprecipitaiton process, emulsion particles were formed by the precipitation of an acetone solution of a macroRAFT agent into an aqueous solution of poly(vinyl alcohol). The particles were then swollen with monomer and subsequently polymerized. Emulsion polymerizations were performed at 65 and 75 °C in which either KPS, BPO, or a combination of both was used as an initiating source. Reactions were also performed at temperatures over 100 °C in which the thermal initiation of styrene was used as an initiating source. In all cases, the polymerizations proceeded in a living manner, yielding polymers that showed an incremental increase in molecular weight with time and had narrow molecular weight distributions. Plots of number‐ average molecular weight versus conversion were linear, indicating a controlled polymerization. The resulting latices were colloidally stable and gave particle size distributions with a typical average particle diameter in the 150 nm range. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 5708–5718, 2006

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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

Citations10
Published2006
Admission routes1
Has abstractyes

Explore more

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