Experimental investigation, modelling, and order of magnitude analysis of oxygen mass transfer in pulsed plate column with <scp>α‐Fe<sub>2</sub>O<sub>3</sub></scp> nanofluid
Bibliographic record
Abstract
Abstract Volumetric oxygen mass transfer coefficient (k L a) is an important parameter in the design of various reactors and bioreactors. In the present work, the influence of α‐Fe 2 O 3 nanofluid on the enhancement of k L a is studied in a pulsed plate column (PPC). An enhancement factor of greater than one showed that the nanofluid is favourable in enhancing the mass transfer rate. The effect of pulsing velocity on k L a is observed to fall under two regimes: the dispersion regime and emulsion regime. The k L a enhancement factor is found to be higher in TiO 2 nanofluid than in α‐Fe 2 O 3 nanofluid, indicating that the type of nanofluid influences the enhancement factor. The order of magnitude analysis showed that localized convection triggered by the Brownian motion of nanoparticles is the phenomenon responsible for k L a enhancement. A dimensionless multiple regression analysis (MRA) model was developed to predict k L a in the nanoparticle loading range of 0.003–0.019 (v/v%), relating the Sherwood number with oscillating Reynolds number (1200 ≤ Re o ≤ 20,000), gas flow Reynolds number (0.135 ≤ Re g ≤0.370), Schmidt number (1300 ≤ Sc ≤2700), and Brownian Reynolds number (2.81 × 10 −4 ≤ Re B ≤5 × 10 −4 ). The pseudo‐homogeneous model could accurately predict the enhancement until critical loading conditions.
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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 | 0.001 | 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".