Exchange bias magnetism in films of NiFe/(Ni,Fe)O nanocrystallite dispersions
Bibliographic record
Abstract
Ni3Fe/(Ni,Fe)O thin films having a nanocrystallite dispersion morphology were prepared by a reactive ion beam-assisted deposition technique. The crystallite sizes of these dispersion-based films were observed to decrease from 8.4 ± 0.3 nm to 3.4 ± 0.3 nm as the deposition flow-rate increased from 2.78% to 7.89% O2/Ar. Thin film composition was determined using selective area electron diffraction images and Multislice simulations. Through a detailed analysis of high resolution transmission electron microscopy images, the nanocrystallites were determined to be Ni3Fe (a ferromagnet), NiO, and FeO (both antiferromagnets). It was determined that the interfacial molar Ni3Fe ratio in the nanocrystallite dispersions increased slightly at first, then decreased as the oxygen content was increased; at 7.89% O2/Ar, the interfacial molar ratio was essentially zero (only NiO and FeO remained). For nanocrystallite dispersion films grown with O2/Ar flow-rate greater than 7.89%, no interfacial (intermixed) Ni3Fe phase was detected, which resulted in no measurable exchange bias. Comparing the exchange bias field between the nanocrystallite dispersion films at 5 K, we observed a decrease in the magnitude of the exchange bias field as the nanocrystallite size decreased. The exchange bias coupling for all samples measured set in at essentially the same temperature (i.e., the exchange bias blocking temperature). Since the ferromagnetic/anti-ferromagnetic (FM/AFM) contact area in the nanocrystallite dispersion films increased as the nanocrystallite size decreased, the increase in the magnitude of the exchange bias could be attributed to larger regions of defects (vacancies and bond distortions) which occupied a significant portion of the FM/AFM interfaces in the nanocrystallite dispersion films.
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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".