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Record W2088037414 · doi:10.1021/ma070603w

Compartmentalization in TEMPO-Mediated Styrene Miniemulsion Polymerization

2007· article· en· W2088037414 on OpenAlexafffund
Hideo Maehata, Catherine Buragina, Michael F. Cunningham, Barkev Keoshkerian

Bibliographic record

VenueMacromolecules · 2007
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsXerox (Canada)Queen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMiniemulsionPolymerizationPolystyreneStyrenePolymer chemistryEmulsion polymerizationPolymerParticle sizeChemistryCopolymerMolar mass distributionChain transferParticle (ecology)Radical polymerizationMaterials scienceChemical engineeringOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

TEMPO-mediated styrene miniemulsion polymerizations were conducted at varying particle sizes (∼50, 90, and 180 nm) to study possible effects of compartmentalization. Polymerizations were initiated using a TEMPO-terminated polystyrene macroinitiator that also acted as costabilizer for the miniemulsion. A bulk polymerization was conducted as a control. Conversion, molecular weight distribution, and the chain livingness were measured to assess the effects of particle size. Decreasing particle size resulted in lower rates of polymerization; after 6 h of polymerization, conversions were 59% for ∼180 nm particles and 43% for ∼50 nm particles. More importantly, large differences in the polymer chain livingness were observed, with smaller particles exhibiting superior livingness at equivalent conversions. Minor effects were also observed on molecular weight; decreasing particle size resulted in higher M n at a specified conversion, signifying lower chain concentrations in smaller particles.

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 score0.003

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 integrity0.0000.001
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 head0.009
GPT teacher head0.244
Teacher spread0.236 · 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

Citations74
Published2007
Admission routes2
Has abstractyes

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