Engineering Green Lubricants II: Thermal Transition and Flow Properties of Vegetable Oil-Derived Diesters
Bibliographic record
Abstract
Six homologous series of linear aliphatic diesters were prepared from commonly available fatty acids (chain lengths 10–22 carbons) and diols (chain lengths, n, 2–10 carbons). The thermal transition and flow properties are presented as functions of their molecular structures, namely chain length, symmetry, end group interactions, and saturation. Predictive relationships between the total chain length of the diesters and their characteristic thermal transition temperatures were obtained. The thermal transition temperatures were affected by intramolecular steric repulsion of the ester groups at small diol chain lengths ( n ≤ 4) and by the odd–even effect associated with large diol chains ( n > 4), allowing for further refinement of the crystallization and melting prediction models. All of the diesters presented Newtonian flow behavior above their melting points, making them particularly suitable for use in lubricant formulations and other flow-dependent applications. The influence of mass on the viscosity was significantly greater than any other structural feature of the linear aliphatic molecules. Viscosity scaled predictably with total chain length, from ∼6 mPa·s for the smallest diester to ∼41 mPa·s for the largest diester at 40 °C. This range is significantly larger than that accessible to native vegetable oils (33–66 mPa·s at 40 °C), affording a vastly improved application range for biobased materials.
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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) | 0.000 | 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 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".