Navigation of superparamagnetic particles with a pre-magnetized high-temperature superconducting bulk in a weak background field
Bibliographic record
Abstract
Abstract Recent research has shown significant progress towards a full body magnetic drug delivery (MDD) system for use in targeted cancer treatments. Although many different MDD systems have been proposed to generate strong remote forces capable of rapidly changing directions at distances greater than 10 cm, current state-of-the-art technologies lack in force strength and/or degrees of freedom. Knowing that high temperature superconducting (HTS) bulks can achieve trapped fields an order of magnitude larger than ferromagnets, this work aims at numerically and experimentally evaluating the forces that can be produced by HTS bulks in a uniform magnetic field. We first use Hall probe measurements and finite element simulations to determine the magnetic field generated by an HTS pellet and show that both results are in good agreement. Using a combination of simulations and experiments, we then show that for a 14 × 6 mm YBa2Cu3O 7 − x pellet magnetized at 2 T, remote forces on superparamagnetic microparticles are maximized at background fields of ∼50 mT. In addition, the direction of the magnetic forces can be flipped by changing the direction of the applied field relative to the HTS’s magnetization. The HTS bulk was successfully used to navigate magnetic microparticles in a glass bifurcation mimicking the hepatic artery of the human liver. Finally, we show by simulation that a large HTS pellet magnetized at 5 T in a field of ∼250 mT can generate stronger forces with more degrees of freedom than the strongest forces achievable in current MDD technologies.
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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".