Shrinkable Hydrogel-Based Magnetic Microrobots for Interventions in the Vascular Network
Bibliographic record
Abstract
We previously showed that microrobots containing ferromagnetic or superparamagnetic material can be propelled in the vascular network while being tracked for navigation control purposes using magnetic gradients generated by a clinical magnetic resonance imaging (MRI) scanner. Here, we show that it is possible to synthesize such microrobots to allow them to change size in response to heat while maintaining the same gradient-based propulsion and MRI-based tracking characteristics of the previous versions. These microrobots are made of magnetic nanoparticles (MNPs) encapsulated in thermo-sensitive hydrogels (poly(N-isopropylacrylamide)). This configuration allows them to shrink in response to temperature elevation caused by the embedded MNPs when exposed to an AC magnetic field. In this paper, spherical PNIPA–MNP microrobots were synthesized and propelled using magnetic gradients of 400 mT/m inside a clinical MRI scanner. The same MRI scanner was used for imaging and tracking of the microrobots before the same microrobots were heated by an AC magnetic field of 4 kA/m at 160 kHz, resulting in a 25% volume reduction of the microrobots. These results suggest the possibility of implementing advanced polymorphic microrobots to accomplish complex tasks in the human body.
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 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".