Optimizing the Polyethylene Oxide and Polypropylene Oxide Contents in Diethylenetriamine-Based Surfactants for Destabilization of a Water-in-Oil Emulsion
Bibliographic record
Abstract
In this work, we used polyoxyalkylenated diethylenetriamine (DETA) demulsifier with various polyethylene oxide (PEO) and polypropylene oxide (PPO) contents to destabilize a stable water-in-oil emulsion. The demulsifiers were characterized by the relative solubility number (RSN). The efficiency of emulsion destabilization was measured by the degree of separation of the oil and water phases. It was found that the destabilization of an emulsion is closely correlated with the PO and EO numbers. When the PO number in a molecule is much greater than the EO number, the surfactant gives a very low oil resolution rate, requires high dosages, and produces a stable middle phase. When a surfactant contains more EO than PO, it gives a high oil resolution rate at a low dosage, but it is easily overdosed, and some surfactants of this type (high molecular weight) also produce a stable middle emulsion phase. When the PO and EO numbers in the surfactant are close to equal, the surfactant breaks the emulsion rapidly at a very low dosage, does not show overdosing at very high dosage, and does not produce a stable middle phase. Therefore, surfactants with balanced PO and EO numbers give optimal performance.
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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 | 0.001 |
| 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 | 0.001 |
| 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".