Defining the physical properties of blends of acetyl‐triacylglycerols derived from transgenic oil seeds
Bibliographic record
Abstract
Abstract Acetyl‐TAG are unusually structured triacylglycerols (TAG) that possess an acetate group at their sn‐3 position. Acetyl‐TAG have different physical properties compared to regular TAG, including lower viscosity and improved cold temperature properties, making acetyl‐TAG useful for different applications, including as a diesel replacement. These unusual TAG molecules can be synthesized in the seeds of transgenic plants through the expression of diacylglycerol acetyltransferase (DAcT) enzymes isolated from different Euonymus species. In this study, the kinematic viscosity as well as the crystallization and melting behavior of blends of acetyl‐TAG and regular TAG were examined to define goals for acetyl‐TAG synthesis in transgenic plants. Even small amounts of regular TAG when blended with acetyl‐TAG had a disproportionate effect on the viscosity of mixture. This effect of regular TAG in increasing kinematic viscosity was more pronounced at lower temperatures. Under slow cooling conditions, the two different TAGs and their blends possessed two main crystallization events with different degree of separation of the thermal transition, and the lower crystallization temperature decreased with increasing amounts of acetyl‐TAG in the blend. At higher cooling rates, one broad and tailed crystallization peak was observed. Heating thermograms indicate similar polymorphic behavior of the blends and a general peak shift to lower transition range with increasing acetyl‐TAG compared to the two pure lipids. This information about the viscosity and thermal properties of blends of TAG and acetyl‐TAG will provide useful targets for engineering higher levels of acetyl‐TAG in transgenic seeds.
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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".