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Record W3084142316 · doi:10.5650/jos.ess20055

Catalytic Transfer Hydrogenation of Low-erucic-acid Rapeseed Oil over a Ni-Ag<sub>0.15</sub>/SBA15 Catalyst

2020· article· en· W3084142316 on OpenAlexaff
Dianyu Yu, Jun Chen, Qi Zhou, Xu Wang, Yan Chen, Liqi Wang, Bohan Yu

Bibliographic record

VenueJournal of Oleo Science · 2020
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsScience North
Fundersnot available
KeywordsErucic acidRapeseedAmmonium formateChemistryCatalysisOleic acidIodine valueSodium formateOrganic chemistryLinoleic acidNuclear chemistryFood scienceFatty acidBiochemistryFormic acid

Abstract

fetched live from OpenAlex

The kinetics of catalytic transfer hydrogenation (CTH) of low-erucic-acid rapeseed oil using ammonium formate as a hydrogen donor over a Ni-Ag0.15/SBA15 catalyst were studied. Then, a kinetic model for the hydrogenation of low-erucic-acid rapeseed oil was established, and it was found that the reaction rate constants of hydrogenations of 9c-18:1 and 12c-18:1 oleic acid were 0.1262 and 0.0148, and the catalytic selectivity of linoleic acid was 2.04. For the catalyst loading of 0.23%, the hydrogenation temperature was 80°C, the ammonium formate concentration was 0.32 mol/50 mL, and the low-erucic-acid rapeseed oil was hydrogenated in 90 min; it was also found that the iodine value of low-erucic-acid rapeseed oil was 80 g I2/100 g, the oleic acid content was 65%, and the trans fatty acids (TFAs) content was only 6.7%. Therefore, CTH may be widely used in the modification of oils and fats.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.212
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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