Monitoring the behaviour of anionic polymer‐anionic surfactant stabilized foam in the absence and presence of oil: Bulk and bubble‐scale experimental analyses
Bibliographic record
Abstract
Abstract The present study aims at monitoring the bulk and bubble‐scale behaviour of anionic polyacrylamide‐sodium dodecyl sulphate stabilized foam in the absence and presence of oil. Dynamic stability tests provided results indicating that polymer increases the foam dynamic stability and decreases the drainage. Oil slows down the drainage rate of polymer‐surfactant foam. In the absence of oil, foam is drained gradually/smoothly whereas remarkable fluctuations are evident in drainage graphs when oil is present. The Hele‐Shaw cell was employed to conduct bubble‐scale as well as statistical analyses on how foam texture is influenced by a polymer‐surfactant system and hydrocarbon. Bubble‐scale analyses, taken right after foam generation in the absence of oil, revealed that foam bubble sizes and their standard deviation increase by polymer concentration. The coefficient of variation of foam bubble sizes drops with polymer concentration in the absence/presence of oil, meaning the growth of foam texture uniformity. Oil increases the bubble size diversity in the foam texture. Disproportionation/Ostwald ripening is hindered by increasing the foam bubble distribution uniformity by adding polymer to the foaming solution. At polymer concentrations higher than 13 × 10−4 g/g, a polymer‐surfactant mixture generates foam in the presence of oil as stable as foam in the absence of oil, while at the polymer concentrations lower than 7 × 10−4 g/g, bubbles are highly unstable when oil is present. Results of this study help to gain a better understanding about the extent to which polymer could enhance the foam stability in bulk/bubble‐scale, which might be applicable for enhanced oil recovery operations.
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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".