Probing Mechanical Properties of Water–Crude Oil Interfaces and Colloidal Interactions of Petroleum Emulsions Using Atomic Force Microscopy
Bibliographic record
Abstract
Atomic force microscopy (AFM) is frequently used to elucidate complex interactions in emulsion systems. However, comparing results obtained with “model” planar surfaces to curved emulsion interfaces often proves unreliable, because droplet curvature can affect adsorption and arrangement of surface-active species, while droplet deformation affects the net interaction force. In the current study, AFM was used to study the interactions between a colloidal probe and water droplet. Force magnitude and water droplet deformation were measured in toluene solutions of asphaltene or bitumen at different concentrations and varying droplet aging time. Interfacial stiffening and an increase in particle–droplet adhesion force were observed upon droplet aging in bitumen solution. As reported in our previous study ( Kuznicki, N. P., Harbottle, D., Masliyah, J., and Xu, Z. Dynamic Interactions between a Silica Sphere and Deformable Interfaces in Organic Solvents Studied by Atomic Force Microscopy. Langmuir 2016, 32 (38), 9797−9806), a viscoelasticity parameter should be included in the high-force Stokes–Reynolds–Young–Laplace (SRYL) equations to account for the interfacial stiffening and non-Laplacian response of the water droplet at longer aging times. However, following the addition of a biodegradable demulsifier, ethyl cellulose (EC), an immediate reduction in both the particle–droplet adhesion force and the rigidity of the water droplet occurred. Following EC addition, the interface reverted back to a Laplacian response and droplet deformation was once again accurately predicted by the classical SRYL model. These changes in both droplet deformation and particle–droplet adhesion, tracked by AFM, imply a rapid asphaltene/bitumen film displacement by EC molecules. The colloidal probe technique provides a convenient way to quantify forces at deformable oil/water interfaces and characterize the in situ effectiveness of competing surface-active species.
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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".