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Record W2077495081 · doi:10.1002/app.37883

Synthesis of nanosized ethylene–propylene rubber latex via polyisoprene hydrogenation

2012· article· en· W2077495081 on OpenAlexaff
Anong Kongsinlark, Garry L. Rempel, Pattarapan Prasassarakich

Bibliographic record

VenueJournal of Applied Polymer Science · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePolymer chemistryPolymerizationCopolymerEthylene propylene rubberNatural rubberMonomerEthylenePolymerChemistryOrganic chemistryCatalysisComposite material

Abstract

fetched live from OpenAlex

Abstract Nanosized ethylene–propylene rubber (EPM) latex with a particle size of 47 nm was synthesized via an alternative route consisting of isoprene (IP) polymerization followed by hydrogenation. First, the IP monomer was polymerized by differential microemulsion polymerization to obtain polyisoprene (PIP) rubber latex with a particle size of 42 nm. The structure of synthetic PIP was hydrogenated at the carbon–carbon double bonds to produce an ethylene–propylene copolymer by diimide reduction in the presence of hydrazine and hydrogen peroxide using boric acid as promotor. The degree of hydrogenation was determined by proton nuclear magnetic resonance ( 1 H‐NMR) spectroscopy and the structure of the ethylene–propylene copolymer was identified by 13 C‐NMR spectroscopy. In nanosized PIP hydrogenation, the hydrogenation level was found to be increased by boric acid addition. An EPM yield of 94% was achieved using a hydrogen peroxide : hydrazine ratio of 1.5 : 1. The EPM produced from PIP has high thermal stability with the maximum decomposition temperature of 510°C and a glass transition temperature of ‐42.4°C close to commercial ethylene–propylene diene rubber. Dynamic mechanical analysis indicated that EPM had a maximum storage modulus due to the saturated carbons domains of the ethylene segments in the polymer chains. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

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)0.002
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 score0.011
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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