In-Situ Epoxidation of Waste Cooking Oil and Its Methyl Esters for Lubricant Applications: Characterization and Rheology
Bibliographic record
Abstract
In the present investigation, in-situ epoxidation of waste cooking oil and its methyl esters was prepared, and the rheological behavior was analyzed for biolubricant applications. Rheological properties of the prepared epoxides were measured at a temperature of 25–100 °C, at a shear rate ranging from 5 to 300 s−1. As viscosity is one of the critical parameters for potential biolubricant applications, in the present study, the power-law model was used to investigate the flow behavior of the epoxides. The viscosity of epoxidized waste cooking oil and its methyl ester epoxides showed Newtonian flow behavior in the studied temperature range. Different shear rates (5–100, 5–300, 100–300 s−1) were studied to determine the shear rate dependency of the epoxidized waste cooking oil and its methyl ester epoxides at different temperatures. From the average viscosity values, it was shown that the epoxides show identical results at all shear rates. The dynamic viscosities of the epoxidized waste cooking oil and its methyl ester epoxides were found to be dependent on fatty acid chain length, unsaturation, and temperature. Detailed physicochemical characterization for epoxide waste cooking oil (EWCO) and epoxide waste cooking oil methyl esters (EWCOME) were carried out to evaluate the properties for suitable biolubricant applications using standard American Society for Testing and Materials (ASTM) and American Oil Chemists’ Society (AOCS) methods. Based on the viscosity for EWCO (278.9 mm2/s) and EWCOME (12.15 mm2/s) and viscosity index for EWCO (164.94) and EWCOME (151.97) of the prepared epoxides, they could complement the standard ISO vegetable grade (VG) lubricants in the market.
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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".