Zinc biosorption by the purple non-sulfur bacterium <i>Rhodobacter capsulatus</i>
Bibliographic record
Abstract
This paper presents the first report providing information on the zinc (Zn) biosorption potentialities of the purple non-sulfur bacterium Rhodobacter capsulatus. The effects of various biological, physical, and chemical parameters on Zn biosorption were studied in both the wild-type strain B10 and a strain, RC220, lacking the endogenous plasmid. At an initial Zn concentration of 10 mg·L(-1), the Zn biosorption capacity at pH 7 for bacterial biomass grown in synthetic medium containing lactate as carbon source was 17 and 16 mg Zn·(g dry mass)(-1) for strains B10 and RC220, respectively. Equilibrium was achieved in a contact time of 30-120 min, depending on the initial Zn concentration. Zn sorption by live biomass was modelled, at equilibrium, according to the Redlich-Peterson and Langmuir isotherms, in the range of 1-600 mg Zn·L(-1). The wild-type strain showed a maximal Zn uptake capacity (Qm) of 164 ± 8 mg·(g dry mass)(-1) and an equilibrium constant (Kads) of 0.017 ± 0.00085 L·(mg Zn)(-1), compared with values of 73.9 mg·(g dry mass)(-1) and 0.361 L·mg(-1) for the strain lacking the endogenous plasmid. The Qm value observed for R. capsulatus B10 is one of the highest reported in the literature, suggesting that this strain may be useful for Zn bioremediation. The lower Qm value and higher equilibrium constant observed for strain RC220 suggest that the endogenous plasmid confers an enhanced biosorption capacity in this bacterium, although no genetic determinants for Zn resistance appear to be located on the plasmid, and possible explanations for this are discussed.
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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) | 0.001 | 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 | 0.000 |
| 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".