Synthesis of <scp>TMP</scp> esters as a biolubricant from canola oil via a two‐step transesterification–transesterification process
Bibliographic record
Abstract
Abstract Trimethylolpropane esters were synthesized from canola oil by a two‐step transesterification–transesterification process. In the first step, canola oil is reacted with ethanol to produce canola oil ethyl ester, while in the second step, canola oil trimethylolpropane ester was obtained by reacting the canola oil ethyl ester and trimethylolpropane using K 2 CO 3 as a catalyst. In the first step, under the optimal reaction conditions (70°C, 2 h, ethanol‐to‐canola oil molar ratio of 10:1, and 0.25 wt.% catalyst loading), canola oil ethyl ester was successfully synthesized with a yield of 95.7 wt.%. In the second step, the final product with the maximum total concentration of canola oil trimethylolpropane ester (82%) and the maximum conversion of canola oil ethyl ester (85%) was obtained at 130°C for 3 h with a canola oil ethyl ester to trimethylolpropane molar ratio of 3.1:1, and 1 wt.% loading of K 2 CO 3 catalyst. The produced polyol esters can be a promising biolubricant with excellent lubricant characteristics, quenching performance, biodegradability, and rheological/tribological properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
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".