Biotic Ligand Model Does Not Predict the Bioavailability of Rare Earth Elements in the Presence of Organic Ligands
Bibliographic record
Abstract
Due to their distinct physicochemical properties, rare earth elements (REEs) are critical to high-tech and clean-energy industries; however, their bioavailability is still largely unexplored. In this paper, the bioavailability of several REEs has been carefully examined for the freshwater alga, Chlamydomonas reinhardtii. In the presence of organic ligands (L), the biouptake of REEs was much higher than that predicted by the biotic ligand model (BLM). Enhancement of the biouptake flux was observed for six ligands (metal = thulium) and six REEs (ligand = citric acid), indicating that this could be a common feature for these metals. In order to explore the mechanism for the enhanced uptake, Tm internalization was carefully evaluated. The Tm internalization flux (Jint) followed first-order (Michaelis-Menten) kinetics with a calculated maximum internalization flux (Jmax) of (1.1 ± 0.08) × 10(-14) mol · cm(-2) · s(-1) and an affinity constant for the reaction of the metal with the transport sites (KTm-R) of 10(7.1) M(-1). In the presence of citric acid, malic acid, or NTA, the Jint for Tm was more than 1 order of magnitude higher than that predicted by the BLM when algae were exposed to a constant 10(-9) M Tm(3+). The bioavailability of the metal complexes could not be explained by a piggyback internalization (through an anion channel) or the contribution of labile complexes. The enhanced biouptake was attributed to the formation of a ternary Tm complex {L-Tm-R} at the metal transport site. In the natural environment where organic ligands are ubiquitous, classic models are unlikely to predict the bioavailability of REEs to aquatic organisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".