Interaction and Adsorption of Hydrophobically Modified Polyacrylamide on Silica and Asphaltene Surfaces
Bibliographic record
Abstract
The adsorption of polymers affects the cost and oil recovery in oil reservoir exploitation and the flocculation effect in the treatment of oil sand tailings. The adhesion and adsorption of a hydrophobically modified polyacrylamide (HMPAM), i.e., P(AM-NaAA-C16DMAAC), on silica and asphaltene were investigated using surface force measurements, thermodynamic analysis and quartz crystal microbalance with dissipation (QCM-D) measurement. Our study indicates that HMPAM polymer has strong interaction with both silica and asphaltene. The adhesion force of HMPAM on silica was stronger than that on asphaltene surface. Consistently, the adsorption of HMPAM was also greater on silica surface, with a more rigid layer formed on the surface. For HMPAM/silica system, the attractive interaction and the strong adhesion are mainly driven by the hydrogen bonding and electrostatic interaction. For HMPAM/asphaltene system, it is mainly due to hydrophobic interaction between the long hydrocarbon chains of HMPAM and asphaltene. Furthermore, continuous adsorption of HMPAM was detected and multiple layers formed on both silica and asphaltene surfaces, which can be attributed to the hydrophobic chains of HMPAM polymers. This work has illustrated the interaction mechanism of HMPAM polymer on hydrophilic silica and hydrophobic asphaltene surfaces, which provide insight into the industrial applications of hydrophobically modified polymer.
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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".