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Record W2087795419 · doi:10.1021/ie0613624

Topology-Engineered Hyperbranched High-Molecular-Weight Polyethylenes as Lubricant Viscosity-Index Improvers of High Shear Stability

2007· article· en· W2087795419 on OpenAlex
Jianli Wang, Zhibin Ye, Shiping Zhu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2007
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsMcMaster UniversityLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerLubricantMaterials scienceViscosityShear (geology)Shear rateTopology (electrical circuits)Chemical engineeringPolymer chemistryPolymer scienceComposite material

Abstract

fetched live from OpenAlex

Chain walking polymerization provides a novel strategy to synthesize highly branched high-molecular-weight polyethylenes with controllable chain topologies ranging from linear to hyperbranched dendritic structure. In this work, we report the performance of this novel series of polyethylenes as lubricant viscosity-index improvers. By examining a range of such model polymers possessing various tailor-designed chain topologies, we report the first systematic study on the unique effects of chain topology on the polymers' viscosity thickening ability and shear stability. It is demonstrated that hyperbranched polyethylenes we prepared possess extremely high shear stability with almost zero shear degradation regardless of their high molecular weight. Our study shows that these hyperbranched polymers have great potential for formulating high-performance lubricants with superior properties.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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