Using Mettler Dropping Point Data from Dilute Soybean Oil‐Triglyceride Mixtures to Estimate Thermodynamic Properties for Corresponding Pure Triglyceride
Bibliographic record
Abstract
Abstract The enthalpy of fusion and melting temperature for ten symmetrical and seven asymmetrical triglycerides (TAGs) was estimated using mettler dropping points (MDP) of five concentrations of TAGs dissolved in a complex mixed solvent (soybean oil) and a modified Clapeyron equation, an approach we refer to as LIST estimation. The ten estimates generated using the LIST method were compared for accuracy to values measured using differential scanning calorimetry and MDP of pure TAG samples and to estimates calculated using effective carbon number and the Triglyceride Property Calculator. We find that LIST estimates for stearic acid and palmitic acid‐based monoacid and symmetrical TAGs agree well with measured and calculated values using alternative methods. Conversely, LIST estimates for stearic acid and palmitic acid based asymmetrical TAGs diverge substantially from alternative estimates, suggesting that the LIST approach is inadequate in describing asymmetric TAGs using the assessed concentrations of TAG in soybean oil. Elaidic acid containing TAGs behaved uniquely, with LIST estimates for trielaidin not agreeing with alternative estimates yet LIST estimates for distearic–monoelaidic in both symmetric and asymmetric configurations agreeing well with alternative estimates. We conclude that the LIST approach of using MDP of a high melting pure TAGs dissolved in soybean oil can be, at minimum, a viable approach in estimating melting behavior properties of symmetric stearic and palmitic acid containing TAGs. Further investigation for the behavior of asymmetric stearic and palmitic acid containing TAGs in soybean oil is required. As such, using known enthalpies of symmetric stearic and palmitic acid containing TAGs, we should be able to estimate the solubility of a high melting pure TAG in soybean oil using MDP and Clapeyron's equation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".