Development of bio‐lubricants from <scp> <i>Madhuca longifolia</i> </scp> and <scp> <i>Ricinus communis</i> </scp> oils via 3‐step chemical modification process for enhanced properties
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Though numerous studies on the tribological performance of edible and non‐edible oils have been carried out, the tribological performance of edible and non‐edible oils under extreme conditions is not completely discussed. Very limited work has been done to examine the performance of lubricants produced by transesterification, epoxidation, and oxirane ring opening (ORO) reaction steps with non‐edible oils using various alcohols. Castor oil‐based lubricant (COL) showed better lubrication properties than mahua oil‐based lubricant (MOL). For the ORO step, the reaction with octanol gives a better quality of lubricant than that with butanol. However, improvement in the quality and rheological properties was found for all the samples of COL and MOL in comparison to mineral base oil. The lubricant formed by the reaction of castor epoxide with octanol has shown 30% improvement, and castor epoxide with butanol has shown 20% improvement. The lubricant formed by the reaction of mahua epoxide with octanol has shown 30% improvement, and mahua epoxide with butanol has shown 15% improvement. This work shows the dependence of the properties of bio‐based lubricant on the ORO reaction with different alcohols, and this can be used to enhance the lubricant performance in terms of various rheological properties like viscosity index, density, and so forth. As the non‐edible oils have lower viscosity index, giving them a disadvantage in comparison to mineral oil, this research increases the viscosity index of the non‐edible oils and gives them better lubrication properties.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it