Interfacial behavior and partitioning of partially neutralized naphthenic acids: experiments versus HLD-NAC predictions.
Bibliographic record
Abstract
Recent studies have shown that naphthenic acids (NAs) and sodium naphthenates dominate the interfacial activity of bitumen and the formation of bitumen emulsions. NAs are naturally occurring mixtures of carboxylic acids in bitumen, where their concentration can reach 4 wt%. Unlike other fatty acid-containing oils, alkaline neutralization of NAs in bitumen does not lead to ultralow interfacial tensions (IFT<0.1 mN/m). Ultralow IFTs are beneficial towards separating emulsions and could be beneficial in the separation of bitumen emulsions. Two possible reasons for the lack of ultralow IFTs with neutralized NAs were explored. One involved insufficient adsorption and neutralization of NA, and the other was the partition of naphthenic species in different phases. Dynamic IFT and pH studies suggest that adsorption and neutralization of NA proceed as predicted by the stoichiometry of the reaction. On the other hand, it was determined that a large fraction of the sodium naphthenates (NaNs) formed at the interface partitioned back into the oil. By forcing the participation of NaNs at the interface via their introduction through the aqueous phase, it is possible to create transient ultralow IFTs. The conditions (NA and NaN concentration and the salinity of the system) that led to ultralow IFTs were predicted by an HLD-NAC model previously validated for the neutralization of oleic acid.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".