Fabrication of Magnetic Ferrofluid Microrobot for Tissue Mechanical Measurement
Bibliographic record
Abstract
Ferrofluids are a uniform mixture of magnetic nanoparticles and a carrier liquid, such as water or oil. They have become increasingly popular in the medical field due to their biocompatibility, manipulability, and their wide range of possible medical applications, one of them being mechanical measurement in human tissues. Our goal is to fabricate a bio-compatible ferrofluid droplet to perform these mechanical measurements. To fabricate the ferrofluid, magnetite nanoparticles (Fe3O4) were chosen along with vegetable oil as the carrier liquid due to their biocompatibility. The magnetic nanoparticles were also coated with oleic acid to serve as a lubricant layer to prevent them from agglomerating. We first coated the magnetic particles (Nanoshel) with oleic acid (Sigma). The magnetic particles were added to distilled water that was heated up to 60oC and mechanically stirred at 500 rpm. For each gram of magnetic nanoparticles used, an equal amount of oleic acid was added into the mixture and stirred for 15 minutes. The coated particles were then rinsed with distilled water followed by ethyl alcohol and dried. To fabricate the ferrofluid, we mixed the vegetable oil with the nanoparticles overnight to ensure uniform mixing with weight rations including 30%wt, 40%wt, and 50%wt, to find the optimal ratio for magnetic control and stability. To validate the technology using ferrofluid to perform tissue mechanical measurement, a droplet of ferrofluid was injected into the agar gel mimicking the stiffness of tissue and then actuated with a magnet. The ferrofluid showed observable deformation under a microscope after magnetic actuation. Our next steps will include repeating this process under a controlled gradient magnetic field while using a camera to track its deformation and movement and from there the hope is to perform on human tissue.
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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 | 0.002 | 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 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".