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Record W2012644941 · doi:10.1021/ma1004056

Synthesis of 1,4-Polybutadiene Dendrimer−Arborescent Polymer Hybrids

2010· article· en· W2012644941 on OpenAlexaff
Mario Gauthier, Abdul Munam

Bibliographic record

VenueMacromolecules · 2010
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPolybutadieneBranching (polymer chemistry)DendrimerPolymerPolymer chemistrySide chainSteric effectsMolar mass distributionCondensation polymerChemistryMaterials scienceGraftingCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

A divergent synthetic scheme was developed for the preparation of high branching functionality hybrid polymers from carbosilane dendrimer substrates and polybutadiene side chains. Carbosilane dendrimers with 32, 64, or 128 peripheral Si−Cl functional groups were first coupled with 1,2-polybutadienyllithium chains having a number-average molecular weight M n ≈ 1000. The polybutadiene-grafted substrates were then hydrosilylated with dichloromethylsilane and reacted with high 1,4-microstructure content polybutadienyllithium chains to generate high branching functionality dendrimer−arborescent hybrids. Three series of hybrid polymers were synthesized containing 1,4-polybutadiene side chains with M n ≈ 1500, 5000, or 30 000. Size exclusion chromatography analysis of the polymers confirmed that a narrow molecular weight distribution was maintained ( M w /M n ≤ 1.14). The branching functionality of the arborescent hybrids varied from 140−335, 160−1110, and 360−2830 for the 32-, 64-, and 128-site coupling precursors, respectively. The experimental branching functionalities attained were lower than the theoretical values due to decreased coupling efficiency within each series, in particular for polymers with longer polybutadiene side chains, apparently due to steric limitations in the grafting reaction.

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 categoriesInsufficient 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 score0.013
Threshold uncertainty score0.999

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)0.0020.000

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 head0.006
GPT teacher head0.223
Teacher spread0.216 · 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

Citations26
Published2010
Admission routes1
Has abstractyes

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