Acetylation of Soybean Lecithin and Identification of Components for Solubility in Supercritical Carbon Dioxide
Bibliographic record
Abstract
There is a growing interest to develop environmentally friendly surfactants for utilization with supercritical carbon dioxide (scCO2), which is a "green" solvent with many industrial applications. The goal of the present work was to separate the commonly used soybean lecithin into a phospholipid-rich fraction, acetylate this fraction, and then test its solubility in scCO2 to gauge its suitability as a surfactant for potential scCO2-based applications. Soybean lecithin was first purified by fractionation using acetone and ethanol and then acetylated with acetic anhydride. The acetylated lecithin was further purified by fractionation with acetone to separate the acetylated fraction from the nonacetylated fraction. High-performance liquid chromatography and electrospray ionization mass spectrometry were utilized to characterize these fractions. The various acetylated phospholipid fractions were then tested for solubility in scCO2 under various pressures and temperatures using both a cloud-point and a Fourier transform infrared apparatus. Acetylation was found to increase the solubility of the phospholipids in scCO2, and N-acetylated phosphatidylethanolamine (NAc-PE) was found to be the most soluble component of the acetylated phospholipids.
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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".