Theoretical and experimental investigation of <scp> CO <sub>2</sub> </scp> solubility in nanofluids containing <scp>NaP</scp> zeolite nanocrystals and [C <sub>12</sub> mim][Cl] ionic liquid
Bibliographic record
Abstract
Abstract Today, CO 2 separation is very important, both as an environmental issue and also in various industries. In this study, the water‐based nanofluid of NaP zeolite nanocrystals and 1‐dodecyl‐3‐methylimidazolium chloride ([C 12 mim][Cl]) ionic liquid were mixed and tested experimentally for CO 2 absorption in an isothermal high pressure cell equipped with magnetic stirring. Zeolite nanocrystals were synthesized via the hydrothermal approach and characterized. A series of experiments were performed at different conditions to investigate the impact of various parameters, including nanoparticle type, nanoparticle concentration, stabilizer concentration, and the vessel's initial pressure, on CO 2 solubility. It was found that 0.02 wt.% of zeolite nanoparticles, 0.4 wt.% of [C 12 mim][Cl] ionic liquid, and 0.05 wt.% of sodium dodecyl benzene sulphonate (SDBS) in nanofluids result in higher absorption of CO 2 compared to other concentrations. Furthermore, CO 2 absorption was increased by increasing ionic liquid and surfactant concentration up to a certain value near critical micelle concentration, but after that the CO 2 absorption was decreased. The overall CO 2 absorption enhancement at 20 bar for 0.02 wt.% zeolite and ZnO water‐based nanofluids with 0.4% [C 12 mim][Cl] ionic liquid and 0.02 wt.% SDBS were 26.9%, 21.5%, 21.2%, and 17% in comparison to pure water, respectively. In an absorption process using nanofluids, besides the influence of the mentioned parameters, the micro‐convection caused by Brownian motion and the grazing effect of nanoparticles should be noted. Considering the micro‐convection and grazing effects, a theoretical model should take into account the Brownian motion and grazing effects on the mass transfer rate in nanofluids to investigate the absorption enhancement by nano‐particles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".