High‐Coercivity Nd <sub>2</sub> Fe <sub>14</sub> B/α‐Fe Nanocomposites With Ultrafine Nanocrystalline Structure via Zr‐Induced Synchronous Precipitation
Bibliographic record
Abstract
ABSTRACT Nanocomposite permanent magnets with reduced rare‐earth content represent a promising class of materials for next‐generation high‐performance applications. However, asynchronous precipitation of soft and hard magnetic phases often results in grain size mismatch and limited coercivity. In this study, zirconium is utilized to modulate the eutectic reaction temperature among the soft magnetic, hard magnetic, and boron‐rich phases, aligning it with the solidification point of the hard phase. This thermal alignment enables synchronous precipitation, leading to the formation of ultrafine dual‐phase nanocomposites with an average grain size of approximately 20 nm and a 75.8% improvement in coercivity. Furthermore, zirconium addition induces the formation of a ferromagnetic ZrFe 2 three‐dimensional network that encapsulates both soft and hard magnetic grains, significantly enhancing intergranular exchange coupling and magnetization uniformity. The synergistic effects of grain refinement and phase compatibility result in the concurrent enhancement of coercivity and energy product, while substantially lowering rare‐earth consumption. These findings offer a practical strategy for grain size synchronization and phase integration in multiphase nanocomposites.
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) | 0.000 | 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".