CO<sub>2</sub> foam properties and the stabilizing mechanism of sodium bis(2-ethylhexyl)sulfosuccinate and hydrophobic nanoparticle mixtures
Bibliographic record
Abstract
In this work, we have prepared CO2-in-water foam by mixing partially hydrophobic SiO2 nanoparticles and sodium bis(2-ethylhexyl)sulfosuccinate (AOT) and studied its properties. The observation of the appearance of the foam revealed that, with the continuous addition of AOT, the phase behavior of the SiO2 nanoparticle and the AOT mixed system transformed from that of a two-phase system of aggregated nanoparticles into that of a uniform dispersed phase. Both foaming ability and foam stability were optimized when the nanoparticles and the AOT were mixed in a proportion of 1 : 5. On the basis of our findings from measurements of the dispersion properties, including measurements of the adsorption isotherm of the surfactant on the nanoparticles, zeta potentials, interfacial tension and the three-phase contact angle, we concluded that the synergistic interactions between the SiO2 nanoparticles and the AOT led to the adsorption of nanoparticles around the bubble surface and the formation of a spatial network structure of nanoparticles in the film, thereby enhancing the mechanical strength of the bubble and improving the resistance to outside disturbances, deformation and drainage. Laser scanning confocal microscopy (LCSM) analysis of the same foams further confirmed the existence of a "viscoelastic shell" wrapped around and protecting the bubble.
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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) | 0.000 | 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 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".