Comparison study on copper bioaccumulation by growing <scp><i>P</i></scp><i>ichia kudriavzevii</i> and <scp><i>S</i></scp><i>accharomyces cerevisiae</i>
Bibliographic record
Abstract
Bioaccumulation via growing microorganisms is a potential technique for treatment of heavy metal pollution. In this study, the Cu tolerance, bioaccumulation properties, and removal ability of growing Pichia kudriavzevii and Saccharomyces cerevisiae in complex environments were investigated comparatively. P. kudriavzevii displayed higher total Cu bioaccumulation capacity and Cu removal rate than S. cerevisiae at various bioaccumulation time and Cu concentrations. The Cu bioaccumulation in both yeasts was obviously improved at low pH and high concentrations of NaCl, which resulted in the increase of Cu removal rate. The maximum Cu removal rate of P. kudriavzevii respectively reached to 55.53% at 20 g/L NaCl and to 77.84% at pH 3, obviously higher than that in normal condition at pH 5 without NaCl addition (13.77%). High concentrations of Zn (0.05 − 0.5 mmol/L) significantly improved the Cu removal ability of P. kudriavzevii while the Cu removal rate was markedly inhibited with the addition of Cd (0.05 − 0.5 mmol/L). Compared with S. cerevisiae, the multi‐stress‐tolerant P. kudriavzevii possessed more powerful Cu tolerance and bioaccumulation ability at low pH, high concentrations of NaCl and with the addition of other heavy metal ions. It suggested that P. kudriavzevii could be used as a potential candidate for Cu removal in complex environments. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1353–1360, 2016
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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 | 0.000 | 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 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".