Experimental issues in magnetic force microscopy of nanoparticles
Bibliographic record
Abstract
The development of magnetic nanoparticles for biomedical applications requires a detailed characterization of their magnetic properties, with relation not only to their chemical structure, but also their morphology and size. Magnetic force microscopy (MFM), thanks to its nanometric lateral resolution and its capability to detect weak magnetic fields, appears as a powerful tool for the characterization of the magnetic properties of single nanoparticles, together with their morphological characteristics. Nevertheless, the application of MFM to the quantitative measurements of magnetic properties at the nanoscale is still an open issue because of a certain incongruence between experimental data and existing theoretical models of the tip-sample magnetic interactions. In this work, MFM data acquired on different magnetic nanoparticles in different experimental conditions (magnetized and not magnetized probes, out-of-field and in-field measurements) are analyzed, with the aim of individuating the possible phenomena affecting MFM measurements. These include topography-induced artifacts resulting from the tip-sample capacitive coupling, which we propose here for the first time. In case of measurements performed in presence of an external magnetic field, much more intense MFM signals were detected as it produces the saturation of the magnetization of the nanoparticles, which is not completely obtained by the sole stray field produced by the tip. Nevertheless, even in in-field measurements, the results evidenced the presence of significant electrostatic effects in MFM images, which, therefore, appear as an important factor to be taken into account for the quantitative interpretation of MFM data.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".