Magnetic properties and the tunneling magnetoresistance effect in Co−MgF2 granular films
Bibliographic record
Abstract
Co − MgF 2 granular films were deposited on glass substrates by rf co-sputtering at room temperature (RT). The influence of the Co volume fraction, fv, of these granular films on the tunneling magnetoresistance (TMR) and magnetic properties was studied systematically. In a magnetic field of 1.2 T, the TMR value at RT initially increases gradually with decreasing fv, reaches its maximum value of −8% for fv=0.38, and then decreases. The corresponding magnetization curves indicate a change from ferromagnetism to superparamagnetism. A minimum in the coercivity, Hc, (11 Oe) is obtained in the Co50(MgF2)50 granular film which also has a large zero field resistivity. This magnetically soft granular film may consequently be a good candidate for high frequency applications. These variations of the TMR and magnetic properties can be ascribed to gradual changes in the film microstructure with decreasing fv, from interconnected metallic Co grains to nano-scaled Co particles dispersed in a crystallized insulating MgF2 matrix. The zero field cooled (ZFC) and field cooled (FC) curves for the samples were obtained in the temperature range 5–300 K in various magnetic fields. The peak in the ZFC curve shifts gradually towards lower temperature with increasing applied magnetic field and, with increasing fv, the peak temperature decreases more quickly with increasing field. The latter indicates that the magnetic interactions between grains become stronger, consistent with model predictions.
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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".