Insight on Methane Foam Stability and Texture via Adsorption of Surfactants on Oppositely Charged Nanoparticles
Bibliographic record
Abstract
We report the phase behavior of a dispersion of alumina-coated silica nanoparticles in the presence of an anionic surfactant (sodium fatty alcohol polyoxyethylene ether sulfate), and then describe the influence of surfactant/nanoparticle concentration ratio on the stability of methane foam as a potential fluid for enhanced oil recovery application. The surface tension of the methane/aqueous phase interface, surface charge, and size of the particle aggregates and amount of surfactant adsorption were characterized as a function of surfactant/nanoparticle ratio. Five adsorption stages, which are described in terms of the extent and type of the surfactant coverage on the nanoparticle surface, explain the behavior of the solution at different surfactant/nanoparticle ratios. The static foam generation experiments were conducted to monitor the variation of the foam stability and texture over the defined adsorption stages. The surface tension trends illustrate that the affinity of nanoparticles for the gas-liquid interface is strongly affected by the adsorption extent of AES molecules on the particle surface. At high surfactant/nanoparticle ratio, the adsorbed surfactant bilayer causes a high hydrophilicity of the particles that significantly pushed the particles away from the gas-liquid interface. At the most hydrophobic state of the particles which occurred at the ratio of 0.2, the foam structure collapsed quickly. The most stable foam with fine texture was found at surfactant/nanoparticle ratio less than 0.008 at which the particles are partially covered with surfactants and have smaller aggregate size. The findings provide a better understanding of the interaction between oppositely charged nanoparticle/surfactant pairs and how that interaction affects foam stability. It is demonstrated that substitution of absolute concentration by surfactant/nanoparticle ratio can truly govern the foam stability and texture. The results can be beneficial to predict the foam behavior in its numerous applications and whether interactions will be synergistic, antagonistic, or neutral.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".