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Record W2123518134 · doi:10.1002/app.13512

Hydrogenation of <i>cis</i>‐1,4‐polyisoprene catalyzed by Ru(CHCH(Ph))Cl(CO)(PCy<sub>3</sub>)<sub>2</sub>

2004· article· en· W2123518134 on OpenAlexaff
Rungnapa Tangthongkul, Pattarapan Prasassarakich, Neil T. McManus, Garry L. Rempel

Bibliographic record

VenueJournal of Applied Polymer Science · 2004
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCatalysisActivation energyEthyleneArrhenius equationChemistryHydrogenDouble bondRutheniumPolymerPolymer chemistryKineticsCopolymerArrhenius plotPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Hydrogenation is a useful method which has been used to improve oxidative and thermal degradation resistance of diene‐based polymers. The quantitative hydrogenation of cis ‐1,4‐polyisoprene which leads to an alternating ethylene–propylene copolymer was studied in the present investigation. To examine the influence of key factors on the reaction, such as catalyst concentration, polymer concentration, hydrogen pressure, and temperature, a detailed study of the hydrogenation of cis ‐1,4‐polyisoprene catalyzed by the Ru complex, Ru(CHCH(Ph))Cl(CO)(PCy 3 ) 2 was carried out by monitoring the amount of hydrogen consumed. Infrared and 1 H‐NMR spectroscopic measurements confirmed the final degree of hydrogenation. The hydrogenation of cis ‐1,4‐polyisoprene followed pseudo‐first‐order kinetics in double‐bond concentration up to high conversions of double bond, under all sets of conditions studied. The kinetic results suggested a first‐order behavior with respect to total catalyst concentration as well as with respect to hydrogen pressure. The apparent activation energy for the hydrogenation process, obtained from an Arrhenius plot, was 51.1 kJ mol −1 over the temperature range of 130 to 180°C. Mechanistic aspects of the catalytic process are discussed. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91: 3259–3273, 2004

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 categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.243
Teacher spread0.235 · 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

Citations17
Published2004
Admission routes1
Has abstractyes

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