Corrosion study of iron‐cobalt alloys for MRI‐based propulsion embedded in untethered microdevices operating in the vascular network
Bibliographic record
Abstract
Our group have shown in an experiment performed in the carotid artery of a living swine that magnetic gradients generated by a clinical magnetic resonance imaging (MRI) system could propel and navigate untethered medical microdevices and micro-nanorobots in the human vasculature. The main problem with these devices is that the metal necessary for magnetic propulsion may corrode and induce cytotoxic effects. The challenge, then, is to find an alloy with low corrosion yet providing an adequate magnetization level for propulsion in often stringent physiological conditions. Because of their high magnetization, we studied the corrosion behavior of two iron-cobalt alloys, Permendur (49% Fe, 49% Co, 2% V) and Vacoflux 17 (81% Fe, 17% Co, 2% Cr), in physiological solution by potentiodynamic polarization assay, surface analysis, and corrosion electrolyte analysis. Both alloys exhibited low corrosion parameters such as a corrosion potential (E(corr)) of -0.57 V/SCE and E(corr) of -0.42 V/SCE for Vacoflux 17. The surface of Permendur samples was homogenously degraded. Vacoflux 17 surface was impaired by cracks and crevices. Both alloys had a stoichiometric dissolution in the electrolyte, and they released enough cobalt to induce cytotoxic effects. This study concluded that Fe-Co alloys could be used preferably in medical microdevices if they were coated so as not to come in contact with physiological solutions.
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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) | 0.000 | 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 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".