Tailings Pond Surfactant Analogues: Effects on Toluene-Diluted Bitumen Drops in NaHCO<sub>3</sub>/K<sub>2</sub>CO<sub>3</sub> Solution. Part 2: Dilational Interfacial Viscoelasticity
Bibliographic record
Abstract
In this two-part work, we used dilational interfacial rheometry to study the interfaces associated with diluted bitumen in simulated pond water as a function of pH, for three different surfactants: two simple carboxylic acids, one with a straight chain and one having a ring in the tail, and the more complex sodium naphthenates. In part 1 ( 10.1021/ef400376v ), dynamic interfacial tension as a function of time was used to measure adsorption. In part 2, described in the present paper, interfacial dilational rheology was studied as a function of oscillation frequency for three different concentrations of the three surfactants adsorbed at the interface between buffer and toluene-diluted bitumen. The concentrations used were selected on the basis of results from part 1 ( 10.1021/ef400376v ). Our results show that interfacial viscoelasticity depends upon not only the tail complexity in the surfactants but also the surfactant concentration and buffer pH. The adsorbed naphthenates were synergistic with interfacially active materials indigenous to bitumen, resulting in high interfacial rigidity (reduced interfacial elasticity). The simpler carboxylic surfactants were less effective. The observed interactions provide insight into possible transport pathways for surfactants in pond water when bitumen is present.
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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 | 0.000 |
| 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) | 0.001 | 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".