Probing Bitumen Liberation by a Quartz Crystal Microbalance with Dissipation
Bibliographic record
Abstract
The detachment of bitumen from sand grains in oil sands processing is known as bitumen liberation. In this study, a quartz crystal microbalance with dissipation (QCM-D) was applied to study the bitumen liberation process under various process conditions. Bitumen was coated on the surface of silica sensors to simulate the oil sands ore. By recording the change of frequency and dissipation of the coated sensor, QCM-D allows for a real-time quantitative analysis of the bitumen detachment process. The effects of solid wettability, solution pH, and operation temperature on bitumen liberation were investigated using QCM-D. The effects of different solution pH values and temperatures on bitumen liberation were conducted with untreated hydrophilic silica sensors. It was found that the degree of bitumen liberation (DBL) was improved from 32 to 98% when the solution pH was increased from 7.8 to 11, indicating the importance of solution pH in the water-based bitumen extraction process. An increasing temperature enhanced not only the degree of bitumen liberation but also the rate of bitumen detachment. The DBL from a hydrophobic silica surface was about 1.2% at pH 11.5 and 22 °C, which is much lower than the hydrophilic silica surface. QCM-D is a powerful tool in studying the bitumen liberation from both hydrophobic and hydrophilic surfaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".