Magnetic force microscopy study of electron-beam-patterned soft permalloy particles: Technique and magnetization behavior
Bibliographic record
Abstract
Electron-beam-patterned submicron permalloy elements with different aspect ratios were studied by magnetic force microscopy ͑MFM͒.The MFM tip stray field can be used to control a particle's magnetic state.By suitably choosing the operating mode and tip coatings, the tip induced distortion of the magnetic structure of soft permalloy elements can be largely reduced.The particle switching field can be precisely obtained by operating MFM at remanence.Through studying the remanent magnetization behavior, it was revealed that for large aspect ratio elements (Ͼ4:1) magnetization reversal occurs directly from one single domain state to the reversed single domain state, while for medium aspect ratio elements (р4:1) the magnetization reversal occurs in a two-step process with two characteristic switching fields.Initially single domain particles switch into a low moment state ͑vortex state͒ at the first field H s , while at the higher field H a , the magnetic moments form a reversed single domain state.The forming of the low moment state is due to the fact that the vortex states can be trapped in the elements during magnetization reversal.This is consistent with micromagnetic simulations and can be directly demonstrated by controlled local MFM tip induced switching.The variations in the measured distribution of the switching fields for both large and small aspect ratio particle arrays are attributed to different reversal mechanisms as well as individual difference in size, thickness, and edge roughness.
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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) | 0.001 | 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".