Adsorption of Mixtures of Toxic Metal Ions Using Non-Viable Cells of <i>Saccharomyces Cerevisiae</i>
Bibliographic record
Abstract
The use of waste biomaterial for the adsorption of heavy metal ions is an economically appealing alternative to conventional metal ion removal methods. In the present work, S. cerevisiae biomass has been shown to be capable of the simultaneous removal of more than 98% of Pb(II) ions, 60% of Zn(II) ions and up to 55% of Cu(II) ions from aqueous solutions in the 10–50 mg/ℓ concentration range. Model equations describing the removal efficiency of each metal ion were determined using Response Surface Methodology (RSM) with respect to operating conditions such as pH, initial metal ion concentration and biomass dosage. Characterization of the metal ion–biomass interactions responsible for biosorption was studied employing zeta potential measurements, BET, FT-IR and EDX techniques; these indicated that the uptake of metal ions by non-living yeast was a surface adsorption phenomenon. The results proved the involvement of an ion-exchange mechanism between the adsorbing metal ions and the cell walls. In the presence of the complete range of metal ions studied, yeast cells were more selective towards Pb(II) ions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".