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Record W1982588095 · doi:10.1081/pre-120024424

Diffusion Limited Chain Transfer in Styrene Emulsion Polymerization: Use of n‐Dodecanethiol to Regulate Molecular Weight

2003· article· en· W1982588095 on OpenAlexafffund
Michael F. Cunningham, T. Witty

Bibliographic record

VenuePolymer Reaction Engineering · 2003
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmulsion polymerizationStyreneMonomerChain transferPolymer chemistryChemistryPolymerizationPolymerRadical polymerizationEmulsionDiffusionTransfer agentChemical engineeringParticle (ecology)Organic chemistryCopolymerThermodynamics

Abstract

fetched live from OpenAlex

Low volatility chain transfer agents such as n‐dodecanethiol (n‐DDT) are important for controlling molecular weight in the production of polymers containing styrene. The high reactivity of n‐DDT towards the styrenic radical, coupled with its low solubility in the aqueous phase, often leads to conditions of diffusion limited chain transfer in which the transport rate of n‐DDT from the monomer droplets to the polymerizing particles is inadequate to maintain equilibrium concentration in the particles. The extent to which the particles are “starved” of n‐DDT has been studied by measuring n‐DDT consumption and molecular weight during styrene emulsion polymerizations. Varying surfactant and/or initiator concentration is shown to alter the n‐DDT consumption and the molecular weight profiles. The two‐film diffusion theory was applied to our data using monomer droplet and polymer particle measurements. Experimental data and theoretical considerations support the existence of monomer droplets beyond the expected end of Interval II for styrene emulsion polymerization.

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 score0.004

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 communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.199
Teacher spread0.189 · 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

Citations8
Published2003
Admission routes2
Has abstractyes

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