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Record W2288158703 · doi:10.4271/2004-01-3047

The Effect of Controlled Polymer Architecture on VI and Other Rheological Properties

2004· article· en· W2288158703 on OpenAlexaff
Peter Callais, Scott C. Schmidt, Noah Macy

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsRheologyPolymerMaterials scienceArchitectureComputer scienceComposite materialArt

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Controlled architecture polymers, including block copolymers, have been an area of interest for a number of years. Block copolymers have been shown to be useful as viscosity index improvers, for example; block copolymers of poly (dienes or hydrogenated dienes) and poly(styrene). However, anionic polymerization, the method used to produce commercial styrenic block copolymers, has limitations on the types of monomers that can be used. Controlled Radical Polymerization (CRP) uses a free radical initiating system and a polymerization controller. This technology results in block copolymers containing a wider range of monomer compositions and leads to predictable structures, molecular weights and polydispersities. Furthermore, these block copolymers can be produced using standard free-radical polymerization techniques by virtue of a new nitroxide capable of controlling a wide range of monomers including acrylic and methacrylic monomers. We have found that acrylic block copolymers formed via CRP, produce excellent viscosity index (VI) improvement in lubricating oils. These acrylic block polymers, in some cases, can exhibit greater than 50% viscosity index improvement as compared to random copolymers or other block compositions. Reported are the results of an experimental design study investigating the composition of the acrylic blocks, molecular weight, and block architecture (diblock, triblock, and gradient) on the resultant VI.</div>

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)0.001
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 score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

Explore more

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