In situ hydrolysis for dihydroxystearic acid production from catalytic epoxidation of oleic acid
Bibliographic record
Abstract
Abstract Concern regarding the setback of dependency on using fossil fuels as the main resources as the precursor for many derivatives had drawn attention to further study the production of dihydroxystearic acid (DHSA) by in situ hydrolysis of epoxidized oleic acid. Epoxidized oleic acid was produced by using in situ formed performic acid. Performic acid was formed by mixing formic acid as the oxygen carrier with hydrogen peroxide as the oxygen donor. The Taguchi method had proposed that optimum parameter for DHSA production is hydrogen peroxide/oleic acid unsaturation molar ratio of 1.5:1, formic acid/oleic acid unsaturation molar ratio of 0.5, reaction temperature of 35°C, and agitation speed of 200 rpm. Based on the optimized parameters, the highest DHSA hydroxyl value of 267 mg KOH/g was achieved. Additionally, a mathematical model was developed using MATLAB software, employing the fourth‐order Runge–Kutta method and simulated annealing optimization to accurately describe the kinetic behaviour of the reaction. The numerical simulations were performed using a genetic algorithm, and the results showed good agreement between the simulation and experimental data, which validates the kinetic model.
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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 | 0.001 |
| 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".