Stabilization of Carbon Dioxide Foam by a Low Concentration of Cetyltrimethylammonium Bromide-Grafted Nano-Faujasite Zeolite. Part 2: Modeling
Bibliographic record
Abstract
There is a dire need for a descriptive model that adequately evaluates the foam stabilizing and destabilizing processes in the presence of nanoparticles. In this study, we are using the population balance model (PBM) to investigate the effects of implementing sole cetyltrimethylammonium bromide (CTAB) as well as CTAB grafted or physically mixed with faujasite (FAU) nanoparticles on the evolution of CO 2 foam bubbles and liquid hold-up. In this method, the sizes of the generated CO 2 foam bubbles of all foaming systems were monitored experimentally and converted into a log-normal population of bubble sizes at every time scale. Then, the obtained population bubble sizes were incorporated into the PBM to obtain approximate solutions and empirical forms describing the coalescence and ripening kernels of foam bubbles with respect to liquid hold-up. Our modeling results indicated that the primary CO 2 foam stabilization mechanism was different based on the applied concentration levels of CTAB and its interaction with FAU nanoparticles. Below the critical micelle concentration (CMC), the virgin surfactant and physically mixed surfactant/nanoparticles showed a high coalescence rate in contrast to the CTAB-grafted FAU nanoparticles. For instance, our findings indicated that the CTAB-grafted FAU nanofluids at a concentration of 100 ppm had a 1000 times lower bubble coalescence rate than the physical mixing of 100 ppm of CTAB with 500 ppm of FAU nanoparticles. Hence, grafting the surfactant on the surface of nanoparticles before application in CO 2 foaming can significantly reduce the breakage of foam bubbles at a surfactant concentration below the CMC. Above the CMC, on the other hand, the PBM indicated that reducing the CO 2 bubbles coarsening was the major CO 2 foam bubble stabilization mechanism. As a broader impact, our modeling results and developed empirical forms of foam coalescence and ripening functions are helpful in accurately predicting the real phenomena that are responsible for foam stabilization at a wide range of conditions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".