Isolation and Characterization of Interfacial Materials in Bitumen Emulsions
Bibliographic record
Abstract
Stable water-in-diluted-bitumen emulsions are detrimental to the commercial production of Athabasca bitumen. Isolation and characterization of interfacial materials (IM) from these emulsions are vital in order to reveal the emulsion stabilization mechanism, which is not fully understood to date. Recently, a heavy water method to isolate IM has been developed for model systems containing less than 10% bitumen. In this study, the heavy water method was improved to isolate IM from water-in-naphtha-diluted-bitumen emulsions at a bitumen concentration typically used in the commercial operation (59 wt %). Modification of the method includes an additional naphtha cleaning process for the wet IM cake. The isolated IMs were characterized by a variety of techniques such as elemental analysis, density measurements, thermal gravimetric analysis, infrared spectroscopy, Langmuir trough, and atomic force microscopy. The results show that the IMs isolated from emulsions containing 5% and 59% bitumen, referred to IM5 and IM59 respectively, are quite different. IM59 on surface of water droplets is likely in a monolayer structure, while IM5 in a multilayer structure. Asphaltene-like species could be dominant in IM5. IM59 features a higher H/C ratio and contains more volatiles, more chloroform insoluble components, and clays as compared to IM5. Carboxylates, probably in the form of sodium naphthenates, were detected in IM59 only, which may play a role in monolayer formation in the emulsion from which IM59 was obtained. Combination of monolayer structure and clays is believed to cause the high stability of emulsions at the bitumen concentration of 59%.
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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 | 0.001 | 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".