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Record W1991410691 · doi:10.1007/s11746-007-1168-8

Kinetic Parameters of a Homogeneous Transmethylation of Soybean Oil

2008· article· en· W1991410691 on OpenAlexafffund
Ryan Doell, Samir K. Konar, D. G. B. Boocock

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2008
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMethoxideTransmethylationChemistryMethanolSoybean oilReaction rate constantCatalysisOrganic chemistryMedicinal chemistryKineticsBiochemistry

Abstract

fetched live from OpenAlex

Abstract The kinetic parameters of a truly homogeneous base‐catalyzed transmethylation of soybean oil at four temperatures from 23 to 50 °C have been determined for the first time. The addition of oxolane, as well as a high methanol/oil molar ratio (27:1), was used to achieve and maintain a monophasic system throughout the reactions. Second order rate constants were determined in terms of the concentrations of the mechanistic reactants, which were the glycerides and methoxide ions (and not methanol). Doubling the methoxide concentration increased the reaction rate twofold. At 23 °C the rate constants for the conversion of triglycerides to diglycerides, diglycerides to monoglycerides, and monoglycerides to glycerol were 6.3, 15.3 and 13.0 L mol −1 min −1 , respectively. These increased to 54.2, 136 and 139 L mol −1 min −1 at 50°C. These latter values (adjusted to 60 °C) were 65, 770 and 190 times larger, respectively, than values cited previously (and adjusted to the methoxide basis) for a transmethylation of Pongamia oil, which was also claimed to be homogeneous. Activation energies for all three steps were the same (63 kJ mol −1 ). Pre‐exponential factors showed that in diglycerides and triglycerides the second and third ester chains sterically hindered the attack of methoxide ions on the ester carbonyl groups.

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 score0.030
Threshold uncertainty score0.262

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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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

Citations25
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Oil Chemists SocietySame topicBiodiesel Production and ApplicationsFrench-language works237,207