Prospects of Magnetic Nanoparticles for Magnetic Field-Assisted Mixing of Fluids with Relevance to Chemical Engineering
Bibliographic record
Abstract
Utilization of efficient, safe and controllable alternative energization approaches towards green and sustainable processes is vigorously explored in the field of process intensification. In this contribution, magnetic fields are specifically discussed and possible mechanisms to exploit this form of energy excitation for fluid-phase mixing in confined spaces are introduced. Magnetic nanofluids are par excellence the most suitable media for transmission of magnetic energy into a target fluid. In addition, their benign nature makes them suitable candidates for biological applications in microfluidics. The interaction of magnetic fluids with magnetic fields, as governed by the equations of motion in ferrohydrodynamics, can generate different mechanisms for fluidic actuations. These mechanisms are mainly the result of the type of magnetic field enabled, e.g., non-uniform static, oscillating or rotating magnetic fields, their strength or the magnetization of polar fluids, in addition to the momentum exchange induced between the rotating magnetic nanoparticles and the carrier fluid in rotating magnetic fields. With an emphasis on applications in microfluidic devices, the review of recent advances in the present contribution shows how such a variety of magnetic fields can be taken advantage of to mix fluids. Mixing in electrically conducting fluids in the framework of magnetohydrodynamics, as another class of magnetic field-assisted mixing is also another subject of this review. This latter category benefits from the absence of magnetic nanoparticles but on the other hand requires complex structuring of mixing devices as imposed by indispensable and appropriate interactions between electric and magnetic fields. The reviewed research findings in this category show how the generation of complex fluid motions is attainable specifically in micron-sized conduits.
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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 | 0.000 | 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 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".