MétaCan
Menu
Back to cohort
Record W2756499581 · doi:10.1177/1350650117732716

Utilizing macroscopic rheometry to investigate triglyceride self-assembly

2017· article· en· W2756499581 on OpenAlexaff
Hamid A Elemsimit, Dana Grecov

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology · 2017
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRheometryRheometerRheologyMaterials scienceDifferential scanning calorimetryComposite materialThermodynamics

Abstract

fetched live from OpenAlex

The rheological behavior of a canola oil-based bio-lubricant was correlated with its surface activities using a rotary rheometer. The experiments on the gap size, substrate, and surface conditions led to a consistent and conceivable assumption that the self-assembled monolayer was extended to a degree that was sufficient to make a significant change in the bulk properties. In parallel, the thermal behavior was also connected to the surface activities. Differential scanning calorimetry was used to measure what is thought to be the monolayer melting point without using a graphite substrate. Crystallization points were measured at relatively high temperatures. Tribological tests were conducted and explained based on the rheological and thermal findings. A scenario describing both the boundary and bulk regimes was hypothesized. Rheometry and thermal analysis techniques are promising because of the variety of variables that could be controlled, such as temperature, shear, time, gap and substrate. To the best of our knowledge, this is the first study using macroscopic rheology to study the surfactant activities of vegetable oil. The influence of substrate and gap size on rheological behavior could change the current standards for using rotary rheometers.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.198
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering TribologySame topicLubricants and Their AdditivesFrench-language works237,207