Superparamagnetic versus blocked states in aggregates of Fe<sub>3−x</sub>O<sub>4</sub> nanoparticles studied by MFM
Bibliographic record
Abstract
Magnetic domain configurations in two samples containing small aggregates of Fe(3-x)O4 nanoparticles of about 11 and 49 nm in size, respectively, were characterized by magnetic force microscopy (MFM). Two distinct magnetic behaviors were observed depending on the particle size. The aggregates constituted of nanoparticles of about 11 nm in size showed a uniform dark contrast on MFM images, reflecting the predominant superparamagnetic character of these particles and arising from the coherent rotation of the spins within the aggregate as the latter align along the tip stray-field. By applying a variable in-plane field, it is possible to induce magnetic polarization yielding an increasing dark/bright contrast as the strength of the applied field overcomes the stray-field of the tip, although this polarization completely disappears as the remanent state is recovered when the magnetic field is switched off. On the contrary, for aggregates of NPs of about 49 nm in size, dark/bright contrast associated with the existence of magnetic domains and magnetic polarization prevails in MFM images all along the magnetic cycle due to the blocking state of the magnetization of these larger particles, even in the absence of an applied field. All in all, we unambiguously demonstrate the capabilities of magnetic force microscopy to distinguish between blocked and superparamagnetic states in the aggregates of magnetic nanoparticles. Micromagnetic simulations strongly support the conclusions stated from the MFM experiments.
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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) | 0.000 | 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 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".