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Record W2290084672 · doi:10.1002/cjce.22458

Synthesis and characterization of high <i>cis</i>‐polymyrcene using neodymium‐based catalysts

2016· article· en· W2290084672 on OpenAlexvenueno aff
Ramón Díaz de León, Francisco Javier Enríquez‐Medrano, Hortensia Maldonado Textle, Ricardo Mendoza Carrizales, Karina Reyes Acosta, Ricardo López-González, José Luís Olivares-Romero, Luis E. Lugo–Uribe

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisPolymerNeodymiumMonomerPolymerizationStereospecificityPolymer chemistryCyclohexaneBoraneChemistryMolecular massSelectivityMaterials scienceOrganic chemistryLaser

Abstract

fetched live from OpenAlex

Abstract The monomer β ‐myrcene, a renewable resource, was polymerized in cyclohexane using two different Ziegler‐Natta catalyst systems based on neodymium Nd(O i‐ Pr) 3 and NdV 3 . The Nd(O i‐ Pr) 3 was combined with [HNMe 2 Ph][B(C 6 F 5 ) 4 ] (or [CPh 3 ][B(C 6 F 5 ) 4 ]) and Al( i ‐Bu) 3 (or Al( i ‐Bu) 2 H). Next, the NdV 3 was activated using Al( i ‐Bu) 3 and AlEt 2 Cl. Both catalyst systems exhibited high polymer yields near 100 % in the established reaction time, high polymer molecular masses, and broad molecular mass distributions. The catalyst systems gave an effective and stereospecific polymerization reaction of β ‐myrcene providing high cis selectivity of 1,4‐polymyrcenes (&gt; 92 %) with a glass transition temperature between −66 and −62 °C. The above‐mentioned features of resulting elastomers in conjunction with the polymer's molecular masses and molecular mass distributions proved to be sensitive to borane and alkylaluminum compounds molar ratios, [B]/[Nd] and [Al]/[Nd] using Nd(O i‐ Pr) 3 and [Cl]/[Nd] and [Al]/[Nd] with NdV 3 .

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 score0.005
Threshold uncertainty score0.678

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)0.0010.000

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 head0.170
Teacher spread0.162 · 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

Citations46
Published2016
Admission routes1
Has abstractyes

Explore more

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