How surfactant-decorated nanoparticles contribute to thermodynamic miscibility
Bibliographic record
Abstract
Abstract In this study, mineral oil–water fluid miscibility without and with the addition of surfactant-decorated nanoparticles is experimentally and theoretically studied. First, three series of interfacial tension (IFT) tests are conducted using a spinning drop tensiometer (SDT) with the addition of hexadecyltrimethylammonium bromide (CTAB) surfactant-decorated SiO 2 nanoparticles at different concentrations. Second, a new comprehensive thermodynamic model is developed to describe the fluid miscibility without and with the addition of these surfactant-decorated nanoparticles, which is also applied theoretically to reveal how the surfactant-decorated nanoparticles contribute to the thermodynamic miscibility state. The thermodynamic model developed is proven to be accurate and physically meaningful by comparing its calculated free energy of mixing with the experimental results and examples from the literature. A series of optimum conditions for the improvement of fluid miscibility by the addition of such surfactant-decorated nanoparticles are determined: a lower temperature, a higher pressure, more wetting conditions, a smaller nanoparticle radius ( r NP < 40 nm), a larger surfactant concentration, and a nanoparticle concentration in the range of 0.5–0.6 wt.%. It should be noted that a higher nanoparticle concentration is required with the addition of more CTAB surfactants in order to reach the most miscible state. Moreover, the effect of surfactant concentration on the miscibility development is found to be independent of the nanoparticle radius, whereas the optimum nanoparticle concentration is reduced with increasing particle size.
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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 | 0.001 |
| 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".