Changing the magnetic properties of microstructure by directing the self-assembly of superparamagnetic nanoparticles
Bibliographic record
Abstract
Magnetic nanoparticles (MNPs) in a liquid dispersion can be organized through controlled self-assembly by applying an external magnetic field that regulates inter-particle interactions. Thus, micro- and nanostructures of desired morphology and superlattice geometry that show emergent magnetic properties can be fabricated. We describe how superferromagnetism, which is a specific type of emergence, can be produced. Here, superparamagnetic nanoparticles that show no individual residual magnetization are organized into structures with substantial residual magnetization that behave as miniature permanent magnets. We investigate the emergence of superferromagnetism in an idealized system consisting of two MNPs, by considering the influence that interparticle magnetostatic interactions have on the dynamics of the magnetic moments. We use this model to illustrate the design principles for self-assembly in terms of the choice of material and MNP particle size. We simulate the dynamics of the interacting magnetic moments by applying the stochastic Landau-Lifshitz-Gilbert equation to verify our principles. The findings enable a method to pattern material magnetization with submicron resolution, a useful feature that has potential applications for magnetic recording and microfluidic particle traps. The analysis also yields useful empirical generalizations that could facilitate other theoretical developments.
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 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".