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 1: Dynamic Interfacial Tension
Bibliographic record
Abstract
The interfacial properties and stability of the bitumen as films over free water or as droplets in process water require attention to aid the design of bitumen recovery methods that foster environmental sustainability. This paper addresses the nature and consequences of surfactant interactions with toluene-diluted bitumen (AOSB) in simulated tailings water. The interactions were monitored as functions of time ( t ) through measurements of dynamic interfacial tension σ ( t ) . We compared the effects of adsorption of two simple surfactants, hexanoic acid (C 5 H 11 COOH or HAA), and the other having 3-cyclopentylpropionic acid (C 5 H 9 CH 2 CH 2 COOH or CPPA), with complex sodium naphthenates (SNs). The surfactants were adsorbed at the toluene/water interface as a control, and the Gibbs surface excess, the area per molecule, and critical breakpoint concentrations were determined. These data were used to select both surfactant concentrations for adsorption at the diluted bitumen/water interface and pH. σ ( t ) versus ( t ) was measured for the AOSB/water interface at pH near that of the surfactant solutions. Next, σ ( t ) versus ( t ) was measured for an AOSB/water system at three concentrations of each surfactant: trace, saturated, and near the critical breakpoint concentration (CMC). Depending upon the concentration and pH, each surfactant affected the interfaces differently depending upon concentration and pH. SN was the most effective for lowering σ ( t ) versus ( t ) synergistically at all concentrations. At low surfactant concentrations, HAA and CPPA inhibited the interfacial activity of AOSB/water unlike SN. At saturated concentrations, HAA and CPPA did not affect the interfacial activity of AOSB/water, while SN produced a synergistic effect. At CMC concentrations, HAA was more effective than CPPA but SN was exceptional in enhancing surface activity. Even if SN is removed from bitumen and is quite soluble in water, it re-adsorbs effectively with significantly reduced interfacial tensions.
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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".