Mass transfer and reaction kinetics of soybean oil epoxidation in a formic acid‐autocatalyzed reaction system
Bibliographic record
Abstract
In this study, the mass transfer and reaction kinetics of soybean oil epoxidation using concentrated hydrogen peroxide in a formic acid‐autocatalyzed reaction system were studied in detail. Studying the mass transfer of formic acid showed that the influence of reactant diffusion near the interface is eliminated when the stirring rate is > 120 rpm in a double‐stirred cell, and the mass transfer rate decreases greatly with the conversion of double bonds and a decrease of reaction temperature. A temperature increase has little impact on the equilibrium of formic acid in the oil/water system, while an increase of epoxidized soybean oil significantly increases the value of the partition coefficient of formic acid. Another important aspect in the kinetic study is the decomposition of performic acid, which can cause the reduction of H2O2 and formic acid during the reaction. Finally, a biphasic model, which considers all reactions in oil and aqueous phases, the equilibrium and mass transfer of reagents and products between the phases, and the evolution of proton concentrations with time, was developed to describe the epoxidation process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".