Magnetic-Dielectric Cantilevers for Atomic Force Microscopy
Bibliographic record
Abstract
Atomic force microscopy (AFM) is a technique that relies on detecting forces at the nanonewton scale. It involves using a cantilever with a tiny tip at one end. This tip interacts with the short- and long-range forces of material surfaces. These cantilevers are typically manufactured with Si or Si3N4 and synthesized using a lithography technique, which implies a high cost. On the other hand, through simple chemical methods, it is possible to synthesize a magneto-dielectric composite made up of artificial SiO2 opals infiltrated with superparamagnetic nanoparticles of Fe3O4. From these materials, it is possible to obtain tipless cantilevers that can be used in AFM analysis. Tipless cantilevers are an alternative tool in nanoscale exploration, offering a versatile approach to surface analysis. Unlike traditional AFM probes, tipless versions eliminate the challenges associated with tip wear, ensuring prolonged stability during measurements. This makes tipless AFM particularly valuable for imaging delicate or soft samples, as it prevents sample damage and provides precise measurements of topography and mechanical and electromechanical properties. This study presents the results of the characterization of known surfaces using magneto-dielectric cantilevers and commercial cantilevers based on Si. The characterization will be carried out through contact and non-contact topography measurements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".