Magnetically induced agitation in liquid‐liquid‐magnetic nanoparticle emulsions: Potential for process intensification
Bibliographic record
Abstract
Microscopic mixing using magnetic nanoparticles (MNP) unveils exciting ramifications for process intensification in chemical engineering. This study explores the use of oil‐in‐water MNP emulsions to achieve mixing in a nonmagnetic continuous phase tantamount to that occurring in equivalent dilute ferrofluid suspensions. To assess the technique, measurements of the torque exerted by ferrofluid emulsions and suspensions of equal magnetic content were performed in rotating, oscillating, and static magnetic fields. Results show that momentum transfer is fairly alike in amplitude and proportionality for the two types of systems of equal magnetic content under the three types of magnetic fields. This implies that momentum of spinning nanoparticles in the emulsions is transferable to the oil droplets containing them which, in return is then transferred to surrounding nonmagnetic liquid. The magnitude of the resulting mixing allows for the foresight of a versatile MNP mixing technology completely separated from the target phase being mixed. © 2013 American Institute of Chemical Engineers AIChE J , 60: 1176–1181, 2014
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".