Imaging Large Iron-Oxide Nanoparticle Clusters by Field-Dependent Magnetic Force Microscopy
Bibliographic record
Abstract
Iron-oxide nanoparticles are intensively considered for high-performance biomedical applications, where simultaneous functionalities, such as magnetic state, large surface area for maximal protein/enzyme binding, high magnetization values to provide large signals, and good dispersion in liquid media, are usually required. In this context, the association of individual nanoparticles into large clusters is of particular interest. Here, we present a magnetic force microscopy (MFM) approach capable to image individual nanoparticulate clusters as large as 350 nm at room temperature and under variable magnetic fields. It is shown that an in situ removal of electrostatic interactions─particularly important for large particle sizes supported by dielectric substrates─in MFM experiments based on phase detection allows us to image the magnetic state of individual clusters. After taking into account the magnetization behavior of the microscope tip, the phase signal reveals a gradual and uniform rotation of the magnetization with the magnetic field and the absence of a hysteretic behavior for all investigated clusters.
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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".