The Role of Cu in Sintered Nd-Fe-B Magnets: ab initio Study
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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 Nd <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Fe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sub> B (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 16k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> site (Fe) has a value of 55 meV/Cu per unit cell, implying the substitution of Fe (16k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> site) 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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