Bibliographic record
Abstract
Trace addition of Cu is an effective method to improve the coercivity of sintered NdFeB magnets via improving the microstructure. The efficiency of Cu doping depends on the distribution of Cu in the multi-phase microstructure of the NdFeB magnet. To understand and control the Cu redistribution, the site preference of Fe substitution by Cu in Nd2Fe14B (2:14:1) and their substitution energies have been calculated by a first-principles density functional method. The total energy calculations show that all the substitution energies of Fe by Cu in 2:14:1 are positive, indicating Cu tends to avoid entering 2:14:1 phase. In particular, the substitution energy of Cu at the 16k1site (Fe) has a value of 55 meV/Cu per unit cell, implying the substitution of Fe (16k1site) by Cu in 2:14:1 could occur at high temperature (above 650 K). It is expected that a very small amount of Cu (1.5 at.% or so) will dissolve in 2:14:1 during induction-melting sintering process (above 1600 K) while depleting from the 2:14:1 grains to the grain boundary region during the post-sinter annealing process. The redistribution of Cu in Nd-rich phase will lower its melting point and promote the homogeneous distribution of Nd-rich phase along the grain boundary of 2:14:1 phase, enhancing the coecivity in sintered NdFeB.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".