Branched Biobased Diesters with Exceptional Low Temperature and Flow Properties for Use in Lubricant Formulations
Bibliographic record
Abstract
A vegetable oil based diester (1,6-hexyldioleate) was branched with propanoic acid (C3) using a green synthetic approach involving solvent- and catalyst-free epoxide ring opening followed by in situ normal esterification. A total of three branched ester derivatives possessing varied numbers of internal protruding branched ester and hydroxyl groups were obtained. All of the pure branched derivatives were comprised of mixtures of positional and/or stereoisomers. Differential scanning calorimetry showed that, regardless of the compositional inhomogeneity of each branched derivative, crystallization was suppressed completely in all of the branched compounds and they all demonstrated glass transitions below −65 °C. This unique thermal behavior is attributed to the internal protruding branched moieties and hydroxyl groups which dramatically slowed mass transfer. The viscosity of the branched compounds was 1 order of magnitude larger than that of the starting diester due to the increased resistance to flow associated with increased branching and hydrogen bonding introduced by the OH groups. Overall, these branched diesters demonstrated superior low temperature and flow properties comparable to existing nonsustainable commercial lubricants and analogous biobased materials, making them suitable alternatives for use in lubricant formulations, particularly in high performance industrial gear and bearing biolubricants.
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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".