Predicting the distribution coefficient in the solvent extraction of rare earth elements
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it