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 (kLa) is an important parameter in the design of various reactors and bioreactors. In the present work, the influence of α‐Fe2O3 nanofluid on the enhancement of kLa 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 kLa is observed to fall under two regimes: the dispersion regime and emulsion regime. The kLa enhancement factor is found to be higher in TiO2 nanofluid than in α‐Fe2O3 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 kLa enhancement. A dimensionless multiple regression analysis (MRA) model was developed to predict kLa in the nanoparticle loading range of 0.003–0.019 (v/v%), relating the Sherwood number with oscillating Reynolds number (1200 ≤ Reo ≤ 20,000), gas flow Reynolds number (0.135 ≤ Reg ≤0.370), Schmidt number (1300 ≤ Sc ≤2700), and Brownian Reynolds number (2.81 × 10−4 ≤ ReB ≤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 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) | 0.000 | 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 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".