Measurements of Surfactant Adsorption on Sandstone in the Presence of Deep Eutectic Solvents
Bibliographic record
Abstract
How to reduce surfactant adsorption on a rock surface is a challenging task because surfactant adsorption would negatively affect the performance of surfactants in enhanced oil recovery. This study thoroughly investigates the effects of various deep eutectic solvent (DES) samples on surfactant adsorption. The surfactant adsorption amounts are quantitatively determined by measuring the variations in the surface tensions of surfactant solutions before and after the adsorption experiments. First, the surface tensions of various pure DES solution samples at different concentrations are measured. The results suggest that the prepared DES samples cannot alter the surface tension of water. Then, the surface tensions of various pure surfactant solutions with different concentrations are measured to build the relationship between surface tension and surfactant concentrations. Afterward, the adsorption experiments are conducted by mixing the crushed Berea sandstone samples with the pure surfactant solutions and the composite DES–surfactant solutions. Next, the surface tensions of the supernatants separated from the mixtures are measured. Correspondingly, the surfactant adsorption amounts are back-calculated from the built relationship between the surface tension and surfactant concentration. The results demonstrate that the DES samples prepared in this study can inhibit the adsorption of the three surfactants on the rock surface. Among all the tested DES samples, the DES sample of choline chloride (ChCl)/urea (1:2) has the most promising performance in inhibiting the adsorption of the surfactant of petroleum sulfonate on the rock surface. Finally, two dynamic adsorption experiments further prove that surfactant adsorption on sandstone particles can be inhibited by adding DES samples.
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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".