Total Internal Reflection Fluorescence Microscopy To Investigate the Distribution of Residual Bitumen in Oil Sands Tailings
Bibliographic record
Abstract
A major waste byproduct of oil sands in situ extraction is oil sands tailings, which are a mixture of water, clay, and residual bitumen. These tailings represent a huge ecological footprint in the form of tailings ponds, which not only render large land areas unusable but also prevent reuse of water. The slow dewatering of the tailings ponds poses a major challenge to the industry. The presence of complex inorganic–organic bitumen–clay mixtures in these tailings contributes to this problem. Hence, understanding the nature of the bitumen–clay association and the effect of bitumen on clay particle–particle interactions is important for the development of more effective chemicals or processes to accelerate particle aggregation and sedimentation during dewatering. Previous studies that investigate these interactions used techniques that are sensitive only toward inorganic clay but not sensitive towards organic bitumen. Here, we use a high-resolution total internal reflection fluorescence (TIRF) microscopy to help identify the accurate location and distribution of bitumen in mature fine tailings (MFT) samples. We report the first adaptation of TIRF beyond cell biology for visualization of bitumen and its interaction with clay. The high signal-to-noise ratio of TIRF microscopy and a high contrast between the clay and residual bitumen provide images that reveal a wealth of information about the bitumen coverage on clay as well as clay–clay aggregates and how the bitumen positions itself within these aggregates. These images confirm the presence of hydrophobic fine clay agglomerates along with the hydrophilic clay particles in MFT. It is also observed that bitumen coats clay particles, bridges clay agglomerates, and is mostly absent as free bitumen in the bulk of the MFT sample. Our work paves the way for the use of nanophotonic tools in oil sands imaging and provides strategic suggestions for the development of better methods for clay sedimentation and bitumen recovery.
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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".