Adsorption mechanism of neodymium onto a South American ion‐adsorption clay and its associated minerals (goethite and silicon dioxide)
Bibliographic record
Abstract
Abstract Ion‐adsorption clays, where rare earth elements (REEs) are adsorbed on surfaces due to weathering, elution, and adsorption processes, have been the primary sources of REEs, especially in China. Recently, South American ion‐adsorption clays, containing significant amounts of associated minerals such as goethite, silicon dioxide, and monazite, have gained attention for their complex REE occurrence and surface adsorption mechanisms. This study examines the performance and mechanisms of Nd3+ adsorption by South American ion‐adsorption clay. Adsorption kinetics and isotherms are investigated for the clay and its associated minerals (goethite and silicon dioxide) to clarify the REE adsorption mechanism. Additionally, attenuated total reflectance–Fourier transform infrared spectroscopy (ATR‐FTIR) is used to analyze the samples before and after adsorption, and zeta potential measurements are conducted to determine the point of zero charge (pHpzc). Results indicate that ion‐adsorption clay carries a negative charge and surface hydroxyl groups, leading to both physisorption and chemisorption of Nd3+, with an activation energy of 6.0 kJ/mol. The negative surface charge is attributed to kaolinite, while hydroxyl groups are provided by both clay and associated minerals. Nd3+ adsorption on goethite and silicon dioxide is homogeneous monolayer chemisorption, driven by surface hydroxyl groups. Silicon dioxide exhibits a lower activation energy (23.6 kJ/mol) compared with goethite (39.6 kJ/mol), likely due to its smaller pHpzc and larger negative zeta potential at pH 6. However, goethite showed a higher adsorption capacity due to its more abundant surface hydroxyl groups.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".