Influence of quenching rate on the microstructure and magnetic properties of melt-spun L10-FePt∕Fe2B nanocomposite magnets
Bibliographic record
Abstract
The quenching rate, which is dependent on the surface velocity (Vs) of Cu wheel during melt spinning, has significant influence on the formation of nanocomposite structure in the Fe52Pt32B18 melt-spun ribbons. The L10-FePt∕Fe2B hard magnetic nanocomposite structure was formed at Vs=20–37m∕s, while the soft magnetic fcc-FePt+amorphous phases were formed at Vs=40–50m∕s. The ribbons melt spun at Vs=37m∕s exhibit in-plane coercivity (Hci)=760kA∕m, remanence (Br)=0.71T, and energy product (BH)max=93.4kJ∕m3. The Br=0.74–0.77T, Hci=681–718kA∕m, and (BH)max=101–108kJ∕m3 were obtained for the ribbons melt spun at Vs=50m∕s and annealed at 748–773K for 900s. The improvement in hard magnetic properties is due to the formation of more finer and homogeneous nanocomposite structure, which results in the enhancement in exchange coupling among the nanosized hard L10-FePt and soft Fe2B magnetic phases.
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 | 0.001 |
| 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".