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Record W2115008116 · doi:10.1002/app.23394

Factorial experimental design for graft copolymerization of styrene and methyl methacrylate onto styrene–butadiene rubber

2006· article· en· W2115008116 on OpenAlexaff
Wanvimon Arayapranee, Pattarapan Prasassarakich, Garry L. Rempel

Bibliographic record

VenueJournal of Applied Polymer Science · 2006
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCopolymerPolymer chemistryNatural rubberMaterials scienceGraftingStyreneMethyl methacrylateStyrene-butadieneMonomerChain transferPolymerizationEmulsion polymerizationRadical polymerizationChemical engineeringComposite materialPolymer

Abstract

fetched live from OpenAlex

Abstract The graft copolymerization of styrene (ST) and methyl methacrylate (MMA) onto styrene–butadiene rubber (SBR) latex prepared by seeded emulsion polymerization has been studied under various reaction conditions using cumene hydroperoxide redox initiator. The mechanism of graft copolymerization has been investigated. The synthesized graft copolymers were purified and then characterized by proton nuclear magnetic resonance ( 1 H NMR) analysis. A 2 2 fractional factorial experimental design was applied to study the effects of the process variables such as the amount of initiator and emulsifier, the presence or absence of chain‐transfer agent, ST to MMA ratio, monomer to rubber ratio, and reaction temperature on the grafting efficiency. The analysis of the results from the design showed the sequence of the main effect on the observed response of the grafting of ST and MMA onto SBR and that the amount of chain‐transfer agent had a significant effect. Transmission electron microscopy was used to study the morphology of the graft copolymers. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2867–2874, 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 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 score0.044
Threshold uncertainty score0.815

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 communication0.0000.001
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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations14
Published2006
Admission routes1
Has abstractyes

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