MétaCan
Menu
Back to cohort
Record W3107695869 · doi:10.22215/etd/2018-12982

Shear Stability of Vegetable Oil-Based Lubricants with Ethyl Cellulose Viscosity Index Improver

2018· dissertation· en· W3107695869 on OpenAlexaff
Niall McCallum

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsCarleton University
Fundersnot available
KeywordsViscosity indexEthyl celluloseShearing (physics)ViscosityMaterials scienceComposite materialReduced viscosityRelative viscosityIntrinsic viscosityShear (geology)PolymerCelluloseChemistryOrganic chemistryBase oilScanning electron microscope

Abstract

fetched live from OpenAlex

A diesel fuel injector apparatus conforming to ASTM 6278 was used to examine the effects of mechanical shear on two vegetable oil-base lubricants, each blended with a different molecular weight ethyl cellulose polymer to improve its viscosity-temperature behaviour. Kinematic viscosity measurements conforming to ASTM D445 were used to determine the magnitude of viscosity loss after mechanical shearing. The lower molecular weight E45 sample was determined to have a permanent viscosity loss of 0.73% and a shear stability index of 1.56, whereas the higher molecular weight EC100 sample was determined to have a permanent viscosity loss of 3.57% and a shear stability index of 6.51. Results were comparable to traditional PMMA polymer additives of similar molecular weight which are commonly used in petroleum-based lubricants, indicating that the ethyl cellulose polymers may function well as VIIs even after mechanical shear degradation. Kinematic viscosity measurements of increasingly dilute solutions of prepared lubricants were used to determine the changes in the intrinsic viscosity of solution as an indicator of the decrease in polymer molecular weight after shearing.

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 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 score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicLubricants and Their AdditivesFrench-language works237,207