Magnetic force fields of isolated small nanoparticle clusters
Bibliographic record
Abstract
The usage of magnetic nanoparticles (NPs) in applications necessitates a precise mastering of their properties at the single nanoparticle level. There has been a lot of progress in the understanding of the magnetic properties of NPs, but incomparably less when interparticle interactions govern the overall magnetic response. Here, we present a quantitative investigation of magnetic fields generated by small clusters of NPs assembled on a dielectric non-magnetic surface. Structures ranging from individual NPs to fifth-fold particulate clusters are investigated in their magnetization saturation state by magnetic force microscopy and numerical calculations. It is found that the magnetic stray field does not increase proportionally with the number of NPs in the cluster. Both measured and calculated magnetic force fields underline the great importance of the exact spatial arrangement of NPs, shedding light on the magnetic force field distribution of particulate clusters, which is relevant for the quantitative evaluation of their magnetization and perceptibly for many applications.
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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) | 0.001 | 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 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".