Effect of Ethanol Concentration on AOT Clustering in Supercritical CO <sub>2</sub> : A Molecular Dynamics Study
Bibliographic record
Abstract
CO 2 foam flooding has emerged as a promising enhanced oil recovery (EOR) strategy for shale/tight reservoirs, but the intrinsically low solubility of surfactants in supercritical CO 2 (scCO 2 ) remains a major barrier to practical application. While experiments have shown that ethanol can improve surfactant solubility, the molecular mechanisms remain unclear. Here, molecular dynamics simulations were performed to investigate the effect of ethanol concentration (5–20 wt %) on the aggregation of AOT in scCO 2 at reservoir-relevant conditions (333 K, 200 bar). Across all systems, AOT assembled into clusters within 600 ns, but increasing the ethanol concentration produced smaller and more dispersed clusters, indicating enhanced solubilization. Structural analyses identified two key drivers of this behavior: (i) stronger solvation of Na + ions by ethanol and (ii) increased hydrogen bonding between ethanol and AOT headgroups, both of which suppress AOT–AOT association. Potential of mean force calculations further revealed that interactions between clusters become progressively less favorable with increasing ethanol content, confirming ethanol’s dispersive role. These molecular-level insights establish the mechanistic basis by which ethanol improves surfactant solubility in scCO 2, providing theoretical guidance for optimizing surfactant- co -solvent formulations to enhance foam stability and injectivity in low-permeability reservoirs.
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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".