Roles of pH and phosphate in rare earth element biosorption with living acidophilic microalgae
Bibliographic record
Abstract
Abstract The increasing demand for rare earth elements (REEs) has spurred interest in the development of recovery methods from aqueous waste streams. Acidophilic microalgae have gained attention for REE biosorption as they can withstand high concentrations of transition metals and do not require added organic carbon to grow, potentially allowing simultaneous sorption and self-replication of the sorbent. Here, we assessed the potential of Galdieria sulphuraria for REE biosorption under acidic, nutrient-replete conditions from solutions containing ≤ 15 ppm REEs. Sorption at pH 1.5–2.5 (the growth optimum of G. sulphuraria ) was poor but improved up to 24-fold at pH 5.0 in phosphate-free conditions. Metabolic activity had a negative impact on REE sorption, additionally challenging the feasibility of REE biosorption under ideal growth conditions for acidophiles. We further examined the possibility of REE biosorption in the presence of phosphate for biomass growth at elevated pH (pH ≥ 2.5) by assessing aqueous La concentrations in various culture media. Three days after adding La into the media, dissolved La concentrations were up to three orders of magnitude higher than solubility predictions due to supersaturation, though LaPO 4 precipitation occurred under all conditions when seed was added. We concluded that biosorption should occur separately from biomass growth to avoid REE phosphate precipitation. Furthermore, we demonstrated the importance of proper control experiments in biosorption studies to assess potential interactions between REEs and matrix ions such as phosphates. Key points • REE biosorption with G. sulphuraria increases significantly when raising pH to 5 • Phosphate for biosorbent growth has to be supplied separately from biosorption • Biosorption studies have to assess potential matrix effects on REE behavior
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 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) | 0.000 | 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 teacher head, 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".