New Fe-B-P-Cu Nanocrystalline Soft Magnetic Alloys with High <i>J</i><sub>s</sub> Combined with Low Coercivity <i>H</i><sub>c</sub>
Bibliographic record
Abstract
Fe-Si-B Amorphous Alloys with Less than 80 at% Fe Are now in Practical Use due to their Excellent Magnetic Softness (Low Coercivity Hc) Combined with Rather High Saturation Magnetic Polarization (Js) which Basically Owing to the Lack of Intrinsic Magnetic Anisotropy and the High Fe Content, Respectively. In Order to Obtain High Js, High Fe Content Is Required. However, Alloys with High Fe Content Exceeding the Limit Usually Have the as-Quenched Structure Consisting of Coarse α-Fe Grains in the Amorphous Matrix, which Results in Inferior Magnetic Softness. We Have Developed a New Fe85.2B10P4Cu0.8 Nanocrystalline Soft Magnetic Alloy Ribbon (with 5 mm in Width and about 20 µm in Thickness) Made from Industrial Raw Materials in Air Atmosphere. The as-Quenched Structure of Fe85.2B10P4Cu0.8 Alloy Has Heterogeneous Amorphous Structure (a Large Amount of Extremely Small α-Fe Clusters in Addition to Amorphous Phase). Homogeneous Nanocrystalline Structure Composed of α-Fe Grains with a Size ~19 nm Was Realized by Crystallizing the Hetero-Amorphous Alloy. The Nanocrystalline Alloy Exhibit High Js ~ 1.83 T (Comparable to the Commercial Fe-3.5 Mass% Si Steel) and Extremely Low Hc ~ 6.0 A/m. Additionally the Alloy Has a Large Economical and Industrial Advantage of Lower Material Cost and Good Reproductivity, which Has a High Potential for the Power Applications.
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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) | 0.000 | 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".