Polymer-Coated Nanoparticles for Reversible Emulsification and Recovery of Heavy Oil
Bibliographic record
Abstract
Heavy crude oil has poor solubility and a high density, making recovery and transport much more difficult and expensive than for light crude oil. Emulsifiers can be used to produce low viscosity oil-in-water emulsions for recovery and transport, but subsequent demulsification can be challenging. Here, we develop and implement interfacially active, pH-responsive polymer-coated nanoparticles (PNPs) to reversibly stabilize, recover, and break oil/water emulsions through variation of solution pH. Silica particles with poly(2-(dimethylamino)ethyl methacrylate) (DMAEMA) chains covalently grafted to the surface are prepared although a reversible addition fragmentation chain transfer grafting-through technique. The resulting DMAEMA PNPs can stabilize emulsions of high viscosity Canadian heavy oil at PNP concentrations as low as 0.1 wt % and at neutral pH. The performance of the DMAEMA PNPs exceeds that of DMAEMA homopolymer additives, which we attribute to the larger size and irreversible adsorption of DMAEMA PNPs to the oil/water interface. After recovery, the emulsion can be destabilized by the addition of acid to reduce pH, resulting in separation and settling of the heavy oil from the aqueous phase. Recovery of more than 10 wt % of the crude heavy oil-in-place is achieved by flooding with aqueous solution of 0.1 wt % DMAEMA PNPs without any additional surfactant or chemical. This work demonstrates the applicability of PNPs as surface active materials for enhanced oil recovery processes and for heavy oil transport.
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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".