Fe-Metalloid Metallic Glasses with High Magnetic Flux Density and High Glass-Forming Ability
Bibliographic record
Abstract
Fe-based bulk metallic glasses with good soft magnetic softness, high strength and relatively low material cost should have greatest potential for wide variety of applications among many kinds of bulk metallic glasses (BMGs). However, the glass-forming metal elements such as Al, Ga, Nb, Mo and so forth in the Fe-based BMGs significantly decrease saturation magnetization (Js) which is a essential property as soft magnetic materials. Since the coexistence of high Js and high glass-forming ability (GFA) has been earnestly desired from academia to industry, however, has been left unrealized over many years. Here, we present a Fe76Si9B10P5 bulk glassy alloy exhibiting with unusual combination of high Js of 1.51 T comparable to the Fe-Si-B amorphous alloy ribbons with thickness of about 25 μm in now practical use, because of not-containing the glass-forming metal elements, and high GFA leading to a rod with a diameter of 2.5 mm. This alloy composed of familiar and low-priced elements also has extremely low coercivity which should enable ultra-high efficient transformers, therefore, has a great advantage for engineering and industry, and thus significantly improves energy saving, conservation of earth resources and environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".