ANISOTROPY OPTIMIZATION OF GIANT MAGNETOIMPEDANCE SENSORS
Bibliographic record
Abstract
As-cast melt extracted CoFeSiBNb wires 37.5 µm in average diameter prepared by MXT Inc. of Montréal have a predominantly helical anisotropy due to residual tensile and torsional stresses quenched in during preparation. When a supplementary tensile stress is applied along the wire, its easy axis of magnetization rotates towards the transverse (or circumferential) direction, since the magnetostriction of Co-rich wires is negative. Well defined, high amplitude and steep peaks of the magnetoimpedance are obtained when the magnetoelastic anisotropy of the wire reaches an optimum in magnitude and orientation. This optimum may be obtained by fine tuning the applied stress and can be permanently quenched in the material by current annealing the wire. A 50 to 100 mA DC current was used for this purpose. The circumferential DC magnetic field associated with this current also changes the wire anisotropy. After cooling, the wire quenches-in an optimum anisotropy, which maximizes the GMI effect of the wire. Magnetic field sensors with sensitivities as high as 1 kV/(T mm) and figures of merit of about 16 ΩT were obtained as a result of this optimization, for wires driven by AC currents of 1 mArms amplitude and 10 MHz frequency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".