Improvement of <scp>CO<sub>2</sub></scp> absorption by <scp>Fe<sub>3</sub>O<sub>4</sub></scp>/water nanofluid falling liquid film in presence of the magnetic field
Bibliographic record
Abstract
Abstract Carbon dioxide is considered as the main greenhouse gas responsible for climate change. Absorption of CO2 by liquid phase has gained attention as a means of mitigating environmental challenges. In this study, the effect of Fe3O4 nanoparticles on the CO2‐water mass transfer coefficient was experimentally evaluated in the presence and absence of a magnetic field. The absorption experiments were carried out in a falling liquid film absorber system in laminar and turbulent flow regimes. Fe3O4/water nanofluid was used in 0.001‐0.05 vol% concentrations. The results show that adding Fe3O4 nanoparticles to water increases the mass transfer coefficient (MTC), and that it increases with an increase in nanofluid concentration. For a concentration of 0.05 vol% nanofluid, the MTC was improved 111% and 13.7% in the turbulent and laminar flow regimes, respectively. The mass transfer coefficient of CO2 in water and effective mass transfer coefficient in nanofluid were increased up to 10% and 29% in the presence of a parallel alternative magnetic field, respectively.
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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) | 0.001 | 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".