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Record W2326627678 · doi:10.1021/ie403731m

Controlled Radical Polymerization at High Conversion: Bulk ICAR ATRP of Methyl Methacrylate

2014· article· en· W2326627678 on OpenAlexafffund
A. Mohammad Rabea, Shiping Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMethyl methacrylatePolymerizationAtom-transfer radical-polymerizationRadical polymerizationPolymer chemistryChemistryMaterials scienceChemical engineeringOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Methyl methacrylate (MMA) was polymerized in bulk via atom transfer radical polymerization (ATRP) with azobis(isobutyronitrile) (AIBN) and tert -butyl peroxybenzoate (TBPB) as initiator for continuous activator regeneration (ICAR). The ATRP was first carried out up to high conversion at 70 °C. When the system reached its glass state at about 94%, the polymerization temperature was elevated to 120 °C to maximize the conversion. Livingness of the ICAR ATRP process and control of polymer molecular weight at high conversion were carefully examined to elucidate the mechanisms of diffusion-controlled reactions. The “gel effect” started at about 50% conversion, and it was accompanied by molecular weight distribution broadening. A dramatic increase in polydispersity was observed at above 90% conversion. The loss of control over molecular weight was caused by diffusion-controlled deactivation, which could be reduced to some extent by increasing deactivator concentration. However, despite the loss of control at high conversion, livingness of the system did not suffer, owing to diffusion-controlled termination. The polymer chains collected at very high conversion could be easily extended with high efficiency.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.284
Teacher spread0.248 · 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

Citations41
Published2014
Admission routes2
Has abstractyes

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