Predicting the distribution coefficient in the solvent extraction of rare earth elements
Bibliographic record
Abstract
Rare earth elements (REEs) comprise the 15 lanthanides, scandium, and yttrium, and are critical to many modern technologies. Solvent extraction is the most common method for REEs separation, with the distribution coefficient (log D ) influenced by factors such as extractant type, pH, temperature, and diluent properties. This study analyzes intercept differences of adjacent lanthanides in log D vs. pH plots using experimental data and proposes a new model to predict log D behavior based on thermodynamic principles. A gradual decrease in ionic radius across the lanthanide series, correlated with atomic number, was observed. Predictions from the model were found to be in reasonable agreement with available experimental data. The model considers ionic size trends and equilibrium behavior, providing a physically meaningful alternative to purely empirical methods. Additionally, a thermodynamic interpretation using Gibbs free energy was introduced to further validate the consistency between model predictions and equilibrium behavior. The model enables improved prediction of REEs behavior in solvent extraction systems without the need for extensive experimental calibration. In addition, the framework may assist in optimizing extraction process parameters for selective separation of target REEs.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".