Surface Charge Heterogeneities and Shear-Induced Coalescence of Bitumen Droplets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The coalescence of emulsified liquid drops is critically important to many separations processes. It is, for example, central to the extraction of bitumen (a form of extra heavy crude oil) from the Alberta oil sands. This research concerns the colloidal interactions between bitumen droplets in water, and how these interactions affect the coalescence of bitumen droplets under static (or at least quasi-static) and dynamic conditions. Experiments utilizing fine capillary pipettes were designed to create controlled oblique shear contacts involving a small oil drop (approx. 250 micron in diameter) sliding along the surface of a much larger droplet (effectively a flat surface). Procedures were developed to allow direct quantification of the probabilities of coalescence between the two oil drops. The experimental parameters include: zeta potential of the bitumen drops (through manipulation of solution pH and calcium ion concentration in the electrolyte), distance of shear contact, and shear speed. These parameters were varied to observe their effects on the probabilities of drop coalescence. Contrary to traditional DLVO theory, it was demonstrated that the coalescence of bitumen droplets was stochastic, and that the process can be characterized by only a probability. This probability of coalescence was shown to dramatically increase with larger contact areas and/or longer distances of shear (i.e. sliding) contact. Regarding the dynamics of contact, our results suggested that the probability of coalescence was slightly lowered with higher shear speeds. Experimental evidence from this study has pointed to significant departures from the classical DLVO theory, which predicts deterministic and contact-area-independent coalescence behaviors. In addition, a theoretical model was developed to explain our observations. The proposed model adopts a statistical approach, combining familiar results of the DLVO theory (which is deterministic in nature) with surface charge heterogeneities (SCH) introduced as the stochastic element. It was demonstrated that our proposed SCH model was remarkably successful in predicting both the trend and the magnitude of the observed coalescence probabilities under quasi-static shearing conditions. It was also shown that the aqueous phase pH not only affected the average zeta potential of the bitumen drops (something that is already known), but also modified the degree of surface charge heterogeneity (in the context of our SCH model). More precisely, an increase in pH of the aqueous solution led to two effects: (i) reduction in zeta potential uniformity (i.e. a larger standard deviation for the local ), and (ii) reduction in the size of the ‘domains’ over which local zeta potentials were effectively uniform. These two effects contributed to a more random distribution of zeta potentials on the bitumen droplet surfaces. Ultimately, it is the randomness of zeta potential, together with its average value (measured, for example, by electrophoresis), that determine the probability of bitumen droplet coalescence. Finally, the validity of our theoretical model was verified by directly mapping surface charge heterogeneities at the bitumen-water interface using atomic force microscopy (AFM). Local zeta potentials were obtained from force curves quantified at different locations, and the results clearly indicated that charges on the bitumen-water interface were heterogeneously distributed. In addition, it was found that the average zeta potential and the important charge heterogeneity parameter (namely, the local domain size), as determined by direct AFM measurements, were comparable to those obtained based on the SCH model (i.e. from fitting the SCH model to observed coalescence probabilities).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it