Class II (three-layer system) phenomenological model based on limiting current density and dynamic chelation chemistry for separation of rare earth elements using electrodialysis
Bibliographic record
Abstract
This paper presents an in-depth investigation into the optimization of rare earth element (REE) separation through electrodialysis, leveraging a newly developed Class II phenomenological model. This study explores the pivotal roles of the HEDTA/Nd molar ratio and pH of feed solution on enhancing the separation efficiency of neodymium (Nd) and praseodymium (Pr) from lanthanum (La) and cerium (Ce). By integrating expanded Nernst-Planck equations and the concept of limiting current density, the model offers a sophisticated understanding of ion transport dynamics and the impacts of concentration polarization. Experimental validation confirms the model's predictive accuracy, demonstrating its practical applicability for industrial-scale operations. The research delineates how operational parameters such as chelating agent concentration and pH critically influence the purity and yield of separated REEs. The dynamic nature of chelation chemistry is also examined, highlighting its evolution during the electrodialysis process and its effect on the system's overall performance. Key findings illustrate that lower HEDTA/Nd molar ratios significantly enhance the purity of Nd + Pr by minimizing the chelation of La and Ce, thus facilitating their migration to the concentrate compartment. Conversely, higher ratios maximize yield by retaining more Nd + Pr in the feed compartment. This dual approach allows for optimized separation based on specific industrial requirements. The outcomes of this study not only advance the field of REE separation but also provide a framework for further research into more efficient and sustainable extraction methods. The developed model and its validation represent a step forward in the practical application of electrodialysis in REE processing, offering substantial benefits for the critical materials sector.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".