Investigation of mixing performance in a semi‐active <scp>T</scp> ‐micromixer actuated by magnetic nanoparticles: Characterization via <scp>Villermaux–Dushman</scp> reaction
Bibliographic record
Abstract
Abstract A semi‐active T‐type micromixer is designed to intensify micromixing by actuating magnetic nanoparticles (MNPs). Five permanent magnets in a zig‐zag arrangement are located next to the mixing channel of the micromixer to apply the magnetic field to the fluid flow. Micromixing performance is considered in terms of the segregation index (X S ) by the Villermaux/Dushman reaction test. The effects of magnetic flux intensity (B = 380–500 mT), the concentration of MNPs (φ = 0.002–0.01 [w/v]), and flow rate ratios on X S and pressure drop are investigated. By increasing MNPs concentration from φ = 0.002–0.008 (w/v), X S decreased and the rise in φ up to 0.008 (w/v) has not been significant on X S . Maximum mixing efficiency (i.e., minimum X S = 0.0088) is achieved for B = 500 mT and φ = 0.01 (w/v). By applying the magnetic field, the mixing performance increased due to the motion of MNPs, but its negative effect is an increase in the pressure drop along the micromixer reactor. Generally, with the formation of MNPs barriers inside the mixing channel, the main fluid flows through these layers and creates the sinusoidal flow paths compared to no magnetic field conditions, and thus, a superior mixing efficiency could be attained.
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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".