Steering of aggregating magnetic microparticles using propulsion gradients coils in an MRI Scanner
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Upgraded gradient coils can effectively enhance the MRI steering of magnetic microparticles in a branching channel. Applications of this method include MRI targeting of magnetic embolization agents for oncologic therapy. A magnetic suspension of Fe(3)O(4) magnetic particles was injected inside a y-shaped microfluidic channel. Magnetic gradients of 0, 50, 100, 200, and 400 mT/m were applied to the magnetic particles perpendicularly to the flow by a custom-built gradient coil inside a 1.5-T MRI scanner. Measurement of the steering ratio was performed both by video analyses and quantification of the mass of the particles collected at each outlet of the microfluidic channel, using atomic absorption spectroscopy. Magnetic particles steering ratios of 0.99 and 0.75 were reached with 400 mT/m gradient amplitude and measured by video analyses and atomic absorption spectroscopy, respectively. Experimental data shows that the steering ratio increases with higher magnetic gradients. Moreover, theory suggests that larger particles (or aggregates), higher magnetizations, and lower flows can also be used to improve the steering ratio. The technological limitation of the approach is that an MRI gradient amplitude increase to a few hundred milliteslas per meter is needed. A simple analytical method based on magnetophoretic velocity predictions and geometric considerations is proposed for steering ratio calculation.
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.
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 | 0.001 |
| 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 it