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Record W2041270217 · doi:10.1021/ma902445u

Compartmentalization Effects on the Rate of Polymerization and the Degree of Control in ATRP Aqueous Dispersed Phase Polymerization

2010· article· en· W2041270217 on OpenAlexafffund
Mary E. Thomson, Michael F. Cunningham

Bibliographic record

VenueMacromolecules · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerizationChemistryChain transferPolymer chemistryDispersityBulk polymerizationAtom-transfer radical-polymerizationChain-growth polymerizationRadical polymerizationDegree of polymerizationChemical engineeringOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Compartmentalization in atom transfer radical polymerization (ATRP) in an aqueous dispersed phase system has been investigated theoretically to understand the effects of particle size on the rate of polymerization and the degree of control on the livingness and polydispersity index (PDI) for the system n -butyl methacrylate/CuBr/EHA 6 TREN. The simulations indicate that there exists a defined range of particle sizes where the rate of polymerization is higher than that of a bulk system and where PDI and frequency of termination remain below that of bulk polymerization. For this highly active catalyst system, the livingness of the chains is a function only of the particle size and is independent of the rate of reaction. Furthermore, simulations conducted with very low catalyst concentrations suggest that the rate of polymerization is dependent on the absolute amount of catalyst in the system rather than the steady-state Cu(I)/Cu(II) ratio that applies for bulk polymerization. At low catalyst concentrations, the rate of polymerization decreases, and the PDI increases with diminishing catalyst concentration, whereas the chain livingness is improved.

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 score0.015
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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

Citations41
Published2010
Admission routes2
Has abstractyes

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