Magnetic field assisted programming of particle shapes and patterns
Bibliographic record
Abstract
Anisotropic particles have generated an enormous amount of research interest due to their applications for drug delivery, electronic displays and as micromotors. However, up till now, there is no single protocol capable of generating particles of "patchy" composition with a variety of well-defined and predictable shapes. To address this, in this submission we dispersed magnetic nanoparticles (MNPs) in a non-magnetic fluid containing monomer and crosslinker. This solution was added to the surface of Teflon, which was submerged in the solvent 2,2,4-trimethylpentane. Under these conditions a round, stable droplet was formed on the Teflon. Upon exposure to a permanent magnet, the MNPs self-assembled into clusters with a variety shapes and sizes. The shape and size of the clusters depended on the magnetic field strength, which we controlled by systematically varying the distance between the magnet and the droplet. Interestingly, the shape of the liquid droplet was also influenced by the magnetic field. Upon polymerization, the MNP patterns and the droplet shape was preserved. We also show that very complex MNP patterns and particle shapes could be generated by controlling the distance between the drop and both a magnet above and below the droplet. In this case, the resulting patterns depended on whether the magnets were attracting or repelling each other, which was capable of changing the field lines that the MNPs align with. Overall, this approach is capable of generating particles with predictable MNP patterns and particle shapes without the use of any templates or complex synthetic steps. Furthermore, by using a sprayer (or similar approaches, e.g., ink jet printing) this technique can be easily scaled up to produce many complex anisotropic particles in a short amount of time.
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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 | 0.000 | 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 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".