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Record W2282107232 · doi:10.1021/acssuschemeng.5b01644

Engineering Green Lubricants I: Optimizing Thermal and Flow Properties of Linear Diesters Derived from Vegetable Oils

2016· article· en· W2282107232 on OpenAlexafffund
Latchmi Raghunanan, Suresh S. Narine

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of CanadaIndustry CanadaTrent UniversityGrain Farmers of Ontario
KeywordsDifferential scanning calorimetryRheometryCrystallizationWaxViscosityMaterials scienceRaw materialChemical engineeringPhase (matter)Viscosity indexOrganic chemistryChemistryThermodynamicsRheologyComposite materialScanning electron microscope

Abstract

fetched live from OpenAlex

The crystallization, melting, and flow behaviors of a series of linear aliphatic diesters (chemical formula (C 17 H 33 COO) 2 [CH 2 ] n ) derived from vegetable oil feedstock were investigated as a function of the methylene spacer units between the two ester moieties (given by the diol chain length, n ). The crystallization and melting behaviors were determined by differential scanning calorimetry and flow behavior and viscosity by rotational rheometry. The results show that quantifiable structure–property relationships exist between the methylene spacer units of the molecules and their physical properties, which can be used to custom-design green materials with controlled phase composition and physical properties such as melting and viscosity suitable for use in applications such as lubricants, phase change energy storage, or waxes.

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 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 score0.001
Threshold uncertainty score0.407

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)0.0000.000

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 head0.154
Teacher spread0.146 · 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

Citations30
Published2016
Admission routes2
Has abstractyes

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