In-Situ Synthesis and Characterization of Biodegradable Estolides via Epoxidation from Canola Biodiesel
Bibliographic record
Abstract
Research on the formulation of estolides from plant seed oils has attracted substantial attention due to their favorable low-temperature properties and environmentally friendly nature. The present research investigates the formulation of canola biodiesel derived estolides for low-temperature applications. The dual-step research method includes ring opening of epoxidized canola biodiesel in the presence of oleic acid, followed by esterification with oleic acid to produce estolides using a mesoporous aluminosilicates possessing Modernite Framework Inverted (MFI) type pentasil structure as a heterogeneous acidic catalyst. Prepared catalyst was characterized to measure the properties essential for the effective catalysis. The catalyst demonstrated promising activity for the estolides formation, >95% conversion was achieved at 110 °C for 6 h using 15 wt % of catalyst loading. 1H NMR technique and oxirane oxygen titrimetric analysis were employed to determine product purity. Physicochemical properties of the reaction products were determined by standard methods and characterization results revealed that the formulated estolides had improved low-temperature, lubricity and rheological properties, and thermo-oxidative stability. Also, biodegradability of the estolides was found to be 92% within 28 days as per the bio-kinetic model. Wear scar diameter of 106 µm was noticed for 10% of alkoxide blend with standard diesel fuel. Overall, outcomes of the physicochemical characterization data indicated that the prepared estolides can act as possible alternative bio-lubricant basestock for various low-temperature applications.
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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".