Biosorption selectivity of rare earth elements onto Euglena mutabilis suspensions and biofilms and the effect of divalent metal ions
Bibliographic record
Abstract
The increasing demand for electronics has led to a desire to recover rare earth elements (REEs) from non-conventional sources, including mining and liquid waste effluents. Biosorption could be a promising method for adsorbing REEs onto microalgae, but biomass immobilization and light delivery challenges remain. It was recently shown that REEs biosorb 160% more on algal biofilms than suspended biomass due to the extracellular polymeric substance (EPS) matrix that grows abundantly in biofilms. In this work, we present findings on biosorption selectivity for different REEs in sulfate solutions. The maximum adsorption capacities of Euglena mutabilis suspensions and biofilms were determined for a mixed REE sulfate solution at an equimolar initial concentration range of 0.1–1 mol/L of each REE ion. The highest adsorption capacities for the suspension are for Sm and Eu which are 57% and 46% higher, respectively, compared to the average REE adsorption capacity. The biofilms also preferentially adsorb Sm, Eu, Yb and Lu at 0.035, 0.033, 0.033, and 0.031 mmol/g, respectively. The impact of dissolved divalent ions of Ca, Mg, and Fe on REE adsorption was also assessed. When Ca and Mg are added in equimolar amounts to 0.1–1 mmol/L solutions of equimolar La, Eu, and Yb sulfate, the amount of REEs adsorbed onto suspensions increases by 30% while when Fe is added, it decreases by 10%. No change is observed in biofilms except when Fe is added resulting in a reduction of the adsorption capacity by 40%. A possible explanation for the role of Fe is attributed to the formation of stronger bonds at the binding sites compared to Ca and Mg. Mechanism of rare earth biosorption onto algal cells and extracellular polymeric substances within an algal biofilm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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".