Determination of vapour pressures of <scp>FAME</scp> industrial mixtures by ebullioscopic and thermogravimetric experimental methods
Bibliographic record
Abstract
Abstract Vapour pressure (VP) is a parameter that characterizes, by principle, only pure compounds. Nevertheless, it can refer also to mixtures in order to characterize their volatility or to be inserted in technical documents. The measurement of VP for mixtures is strongly dependent on the possible variation of the composition, during experiments, due to the different volatility of the constituent compounds. It is possible to calculate the VP of the mixture starting from its composition, but different thermodynamic scenarios must be considered. Moreover, for industrial samples, possible effects due to the presence of impurities must be considered. In this work, two different experimental methods have been employed to determine VP of some acetates esters and two industrial mixtures of fatty acid methyl esters (FAME). The first method is a direct ebullioscopic method, while the second is an indirect thermogravimetric analysis (TGA). An error function was calculated to compare the experimental results of VPs obtained with the two methodologies with the theoretical ones. Ebullioscopic measures resulted suitable only for acetates esters, as FAME mixtures are characterized by VPs too low to be quantified with this technique. On the contrary, TGA methodology is more accurate for FAME than acetates. It allows the collection of a great number of VP values with a very fast analysis. This method is less accurate than others, but it can be useful for a fast screening of the FAME mixtures, also contaminated with light impurities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".