Magnetic force microscopy
Bibliographic record
Abstract
Magnetic force microscopy (MFM) is an atomic force microscopy (AFM) based technique in which an AFM tip with a magnetic coating is used to probe local magnetic fields with the typical AFM spatial resolution, thus allowing one to acquire images reflecting the local magnetic properties of the samples at the nanoscale. Being a well established tool for the characterization of magnetic recording media, superconductors and magnetic nanomaterials, MFM is finding constantly increasing application in the study of magnetic properties of materials and systems of biological and biomedical interest. After reviewing these latter applications, three case studies are presented in which MFM is used to characterize: (i) magnetoferritin synthesized using apoferritin as molecular reactor; (ii) magnetic nanoparticles loaded niosomes to be used as nanocarriers for drug delivery; (iii) leukemic cells labeled using folic acid-coated core-shell superparamagnetic nanoparticles in order to exploit the presence of folate receptors on the cell membrane surface. In these examples, MFM data are quantitatively analyzed evidencing the limits of the simple analytical models currently used. Provided that suitable models are used to simulate the MFM response, MFM can be used to evaluate the magnetic momentum of the core of magnetoferritin, the iron entrapment efficiency in single vesicles, or the uptake of magnetic nanoparticles into cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".