Linking the chemical speciation of cerium to its bioavailability in water for a freshwater alga
Bibliographic record
Abstract
Over the past decade, researchers have begun to use metals of the lanthanide family for numerous applications, including liquid crystal display (LCD) screens, optical fibers, and laser technology. Unfortunately, little is presently known about their bioavailability or the mechanisms by which they might cause toxicity. The present study focuses on cerium (Ce), one of the most widely used lanthanides, and on validating the biotic ligand model as a means to predict Ce bioaccumulation. Short-term exposures to Ce were performed using the unicellular alga Chlamydomonas reinhardtii, to better relate Ce bioavailability to its chemical speciation in solution. Maximum uptake fluxes (Jmax ) and affinity constants for the binding of Ce to the biological uptake sites (KS ) were established at pH 5.0 and pH 7.0. An apparent affinity constant of 1.8 × 10(7) M(-1) was observed at pH 5.0, with a larger value obtained at pH 7.0 (6 × 10(7) M(-1) ), albeit under conditions where equilibrium could not be confirmed. By evaluating Ce speciation using centrifugal ultrafiltration and single-particle inductively coupled plasma spectrometry, it could be concluded that very little (∼30%) Ce was truly dissolved at pH 7.0, with the majority of the metal being present in colloidal species. Speciation was also monitored by fluorescence to evaluate Ce complexation by natural organic matter (NOM). The presence of NOM decreased Ce bioaccumulation in line with free Ce concentrations. Finally, competition with calcium for the metal uptake sites was shown to result in a decrease in Ce uptake by C. reinhardtii.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".