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Record W3086387551 · doi:10.1002/aocs.12416

Cold Plasma Treatment of Soybean Oil with Hydrogen Gas

2020· article· en· W3086387551 on OpenAlexaff
Ximena Yépez, Haci Baykara, Lei Xu, Kevin M. Keener

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2020
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistrySoybean oilGas chromatographyHydrogen peroxideFourier transform infrared spectroscopyHydrogenInfrared spectroscopyAtmospheric pressureFatty acidPlasmaChromatographyOrganic chemistryChemical engineeringBiochemistry

Abstract

fetched live from OpenAlex

Abstract High‐voltage atmospheric cold plasma (HVACP) treatment generates reactive gas species that induce inter‐ and intramolecular reactions in soybean oil. The goal of this study is to analyze the effect of HVACP treatment on the chemical structure of soybean oil in a hydrogen gas environment at atmospheric pressure. HVACP was used to treat soybean oil (15 g) for up to 6 hours by triplicate. Plasma‐generated reactive gas species interact with the sample, producing three distinct fractions identified as a liquid, gel, and solid. Fatty acid profile, Fourier‐transform infrared spectroscopy, proton and carbon nuclear magnetic resonance, size‐exclusion chromatography, thermal properties, and peroxide value were used to characterize the chemical structure. Results indicated a lower content of polyunsaturated fatty acids, increased content of saturated fatty acids, and the presence of isomers. An insoluble portion was observed in the solid fraction and increased with treatment time up to 42% in the 6‐h treated samples. Plasma species may cause two main reactions: polymerization and hydrogenation.

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 score0.020
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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
Published2020
Admission routes1
Has abstractyes

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