Critical evaluation and thermodynamic optimization of the iron-rare-earth systems
Bibliographic record
Abstract
Rare-Earth elements by virtue of its typical magnetic, electronic and chemical properties are gaining importance in power, electronics, telecommunications and sustainable green technology related industries. The Magnets from RE-alloys are more powerful than conventional magnets which have more longevity and high temperature workability. The dis-equilibrium in the Rare-Earth element supply and demand has increased the importance of recycling and extraction of REE's from used permanent Magnets. However, lack of the thermodynamic data on RE alloys has made it difficult to design an effective extraction and recycling process. In this regard, Computational Thermodynamic calculations can serve as a cost effective and less time consuming tool to design a waste magnet recycling process. The most common RE permanent magnet is Nd magnet (Nd2Fe14B). Various elements such as Dy, Tb, Pr, Cu, Co, Ni, etc. are also added to increase its magnetic and mechanical properties. In order to perform reliable thermodynamic calculations for the RE recycling process, accurate thermodynamic database for RE and related alloys are required. The thermodynamic database can be developed using the so-called CALPHAD method. The database development based on the CALPHAD method is essentially the critical evaluation and optimization of all available thermodynamic and phase diagram data. As a results, one set of self-consistent thermodynamic functions for all phases in the given system can be obtained, which can reproduce all reliable thermodynamic and phase diagram data. The database containing the optimized Gibbs energy functions can be used to calculate complex chemical reactions for any high temperature processes. Typically a Gibbs energy minimization routine, such as in FactSage software, can be used to obtain the accurate thermodynamic equilibrium in multicomponent systems. As part of a large thermodynamic database development for permanent magnet recycling and Mg alloy design, all thermodynamic and phase diagram data in the literature for the fourteen Fe-RE binary systems: Fe-La, Fe-Ce, Fe-Pr, Fe-Nd, Fe-Sm, Fe-Gd, Fe-Tb, Fe-Dy, Fe-Ho, Fe-Er, Fe-Tm, Fe-Lu, Fe-Sc and Fe-Y are critically evaluated and optimized to obtain thermodynamic model parameters. The model parameters can be used to calculate phase diagrams and Gibbs energies of all phases as functions of temperature and composition. This database can be incorporated with the present thermodynamic database in FactSage software to perform complex chemical reactions and phase diagram calculations for RE magnet recycling process.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".