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Record W4416600902 · doi:10.1021/acs.langmuir.5c04559

Hyperbranched Poly(octadecyl Acrylate)s as Lubricant Additive Studied by Resonance Shear Measurement

2025· article· en· W4416600902 on OpenAlexaff
Yutaka Takahashi, Yuzhong He, Yu-Han Jiang, Masatoshi Tosaka, Masashi Mizukami, Shigeru Yamago, Kazue Kurihara

Bibliographic record

VenueLangmuir · 2025
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsHatch (Canada)
FundersCore Research for Evolutional Science and TechnologyInstitute for Chemical Research, Kyoto UniversityJapan Science and Technology AgencyJapan Society for the Promotion of Science
KeywordsLubricantLubricationBoundary lubricationViscosityPolymerViscosity indexShear (geology)Friction coefficient

Abstract

fetched live from OpenAlex

Polymer additives are commonly used in lubricants, yet previous studies have predominantly focused on those with linear chain structures. Generally, low viscosity lubricants are effective in the hydrodynamic lubrication regime, but they lead to increased friction and wear in the boundary lubrication regime. In this study, hyperbranched poly(octadecyl acrylate)s (HBPs) were proposed as a novel class of lubricant additives, and their performance was compared to that of linear polymers. HBPs with controlled macromolecular structures were synthesized by the organotellurium-mediated radical polymerization and evaluated as additives in a poly(α-olefin) (PAO) base oil. Viscosity measurements showed that HBP solutions maintained low fluid resistance comparable to PAO itself, unlike linear polymers, which increased in viscosity. Resonance shear measurements demonstrated superior nanoscopic lubrication efficiency for HBPs, HBPs > linear polymers > PAO, and macroscopic friction tests confirmed a significant reduction in the coefficient of friction in the boundary lubrication regime. These results revealed that HBPs offered effective lubrication for both the hydrodynamic and boundary lubrication regimes.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

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)0.0010.001

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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