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Record W2903854894 · doi:10.1002/cjce.23429

Efficient epoxidation of vegetable oils through the employment of acidic ion exchange resins

2018· article· en· W2903854894 on OpenAlexvenueno aff
Olga Gómez‐Jiménez‐Aberasturi, Jonatan Perez‐Arce

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryIon-exchange resinCatalysisYield (engineering)HomogeneousHydrogen peroxideOrganic chemistryAcetic acidSunflower oilSelectivityVegetable oilChemical engineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract The epoxidation of vegetable oils is a chemical or biochemical reaction where oil triglycerides are converted into more reactive molecules. These will be further transformed into a broad variety of products with significant potential for industrial applications. The epoxidation of vegetable oils consists of a commercially established process that uses homogeneous mineral acids as catalysts. In this paper, different strong acidic ion exchange resins were evaluated as alternatives to substitute the commercial homogeneous catalysts. Amberlyst 39 was selected as the most promising one to explore the effect of the variables such as temperature, acetic acid, or hydrogen peroxide concentration in sunflower oil epoxidation. The optimal operational conditions that maximized the conversion and oxirane yield were determined. Then, these values were applied in several catalyst reuses for establishing the resin durability. Results show that by employing ion exchange resins, excellent product yields and selectivity are obtained, minimizing post‐reaction purification needs.

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 score0.129
Threshold uncertainty score0.206

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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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