Mössbauer and magnetization studies of mechanically milled nanocrystalline Fe<sub>1–</sub><sub><i>x</i></sub>Al<sub><i>x</i></sub> alloys
Bibliographic record
Abstract
Changes in the magnetic behavior of Fe1–xAlx (x = 0.3, 0.4, 0.5, 0.6) powders during mechanical alloying have been studied. The ball milling process leads to formation of solid state reaction assisted by severe plastic deformation because of which crystallite size is reduced and as a result of which interesting magnetic properties are developed. The evolution of magnetic order in high-energy ball-milled Fe–Al solid solution is investigated using 57Fe Mössbauer spectroscopy and vibrating sample magnetometer. Mössbauer spectra and the hyperfine field distributions of all the samples show the presence of both magnetic and paramagnetic components in the samples. The corresponding bulk magnetization studies also show that the Al rich samples are also ferromagnetic, which can be attributed to the presence of disordered Fe-rich phases due to the non-equilibrium process of alloying. In Fe-rich samples, the formation of an off stoichiometric Fe3Al phase is favored while in the case of Al-rich samples both Al-rich phases and clustering of Fe and Al atoms are present. The systematic variation in the magnetic properties has been qualitatively correlated with the evolution of microstructure, reduction in grain size (obtained using transmission electron microscopy) and enhanced intergranular exchange coupling.
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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".