Are nano-colloids controlling rare earth elements mobility or is it the opposite? Insight from A4F-UV-QQQ-ICP-MS
Bibliographic record
Abstract
Rare earth element (REE) mobility in the environment is expected to be controlled by colloids. Recent research has detailed the structure of iron-organic colloids (Fe-OM colloids), which include both large colloids and smaller nano-colloids. To assess how these nano-colloids affect REE mobility, their interactions with REE and calcium (Ca) were investigated at pH 4 and 6. Using Asymmetric Flow Field Flow Fractionation (A4F) combined with UV and Triple Quadrupole Inductively Coupled Plasma Mass Spectrometry (QQQ-ICP-MS), Fe-OM nano-colloids were separated from bulk Fe-OM colloids and their REE and Ca content were analyzed. Without REE and Ca, nano-colloids had an average diameter of approximately 25 nm. Their structure is pH-dependent, with aggregation increasing as pH decreases. At high REE loadings (REE/Fe ≥ 0.05), REE induced a size increase of nano-colloids, regardless of pH. Heavy REE (HREE), with their high affinity for organic matter, formed strong complexes with Fe-OM colloids, resulting in large aggregates. In contrast, light REE (LREE), which bind less strongly to organic molecules, were associated with the smallest nano-colloids. Low REE loading did not cause noticeable fractionation. Calcium further enhanced the aggregation process at both pH levels by neutralizing the charges on nano-colloids. These findings indicate that REE can act as aggregating agent controlling their own mobility, and regulating colloid transfer. • A4F-UV-QQQ-ICP-MS shows REE fractionation within nano-colloids. • Heavy REE act as effective aggregating agents for nano-colloids. • Light REE form weak bridges, binding to the smallest, least robust nano-colloids. • REE loading significantly enlarges the aggregation of nano-colloids. • REE ability to induce colloidal aggregation controls their environmental mobility.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.080 | 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; both teacher heads agree on what is shown here.
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".