Heterogeneous Distribution of Adsorbed Bitumen on Fine Solids from Solvent-Based Extraction of Oil Sands Probed by AFM
Bibliographic record
Abstract
Mineral particles encountered in oil production and many chemical processes are generally adsorbed with organics (e.g., bitumen), playing an important role in determining their wettability and interaction behaviors. In this work, the surface properties of fine solids, collected from the solvent-based extraction of Athabasca oil sands using cyclohexane as the extraction solvent, have been systematically characterized by several complementary techniques. The fine solids were shown to be mainly composed of silica and aluminosilicate clays, analyzed using X-ray photoelectron spectroscopy and energy dispersive X-ray spectroscopy. The particle wettability was determined using the Washburn method. The mineral particle surfaces were further characterized by atomic force microscope (AFM) techniques. Various regimes on the particle surfaces with and without adsorbed bitumen were identified and distinguished using PeakForce quantitative nanomechanics AFM imaging of surface topography, adhesion, modulus, and deformation simultaneously in air. The results demonstrated that the adsorbed bitumen was heterogeneously distributed on the particle surfaces. Such surface heterogeneity was further confirmed by AFM force mapping using a hydrophobized AFM tip on fine solids in water. The force–separation profiles obtained on relatively hydrophilic mineral regimes could be well fitted by the classical Derjaguin–Landau–Verwey–Overbeek (DLVO) model, while additional hydrophobic interaction should be included in the modified DLVO model for the interactions on more hydrophobic domains (with adsorbed bitumen). This work provides a facile and useful methodology for characterizing the surface properties of fine solids in oil production, with implications for an improved understanding of the interaction mechanisms of particles suspended in bitumen products in oil production. The methodology can be readily extended to characterizing the surface properties of many other particles and substrates in a wide range of chemical processes and engineering applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".