Extensional Rheological Measurements of Surfactant–Polymer Mixtures
Bibliographic record
Abstract
Abstract Polymer solutions flowing in the porous media during enhanced oil recovery (EOR) processes are subjected to both shear and extensional rheological deformation. However, the previous rheological studies conducted on a surfactant?polymer (SP) system or polymer systems were only shear-based. In this paper, the extensional rheological performance of hydrolyzed polyacrylamide (HPAM) in the presence of an anionic surfactant at various concentrations (0, 0.01, 0.05, 0.1, 0.2, and 0.3%) is studied with deionized water and 1% NaCl. Further, the extensional rheological behavior of HPAM in the presence of NaCl and CaCl2 is studied at varying ionic strengths (1?10%). A capillary break-up extensional rheometer is used for performing extensional rheological characterization. Results revealed that the extensional resistance of HPAM is enhanced in the presence of a surfactant. Particularly, around the critical micelle concentration value of the surfactant (0.1%), HPAM showed higher extensional resistance. Higher extensional resistance for the SP system is observed with deionized water when compared to 1% NaCl. HPAM showed improved performance at 1% NaCl salinity when compared to the higher concentration of NaCl salinity. However, the presence of even 1% of calcium ions is detrimental to the extensional properties of HPAM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".