Microbial Selenate Sorption and Reduction in Nutrient Limited Systems
Bibliographic record
Abstract
In this study, batch sorption experiments and X-ray adsorption spectroscopy (XAS) were utilized to investigate selenate sorption onto Shewanella putrefaciens 200R. Selenate sorption was studied as a function of pH (ranging from 3 to 7), ionic strength (ranging from 0.1 to 0.001 M), and initial selenate concentration (ranging from 10 to 5000 microM) in the absence of external electron donors. The results show that the extent of selenate sorption is strongly dependent on pH and ionic strength, with maximum sorption occurring at low pH (pH = 3) and low ionic strength (0.001 M NaCl) conditions. The strong dependence of Se sorption with ionic strength suggests the formation of outersphere complexes with the cell wall functional groups. Langmuir isotherm plots yielded log Kads values from 2.74 to 3.02. Desorption experiments demonstrated thatthe binding of selenate onto S. putrefaciens was not completely reversible. XANES analysis of the cells after sorption experiments revealed the presence of elemental selenium, indicating that S. putrefaciens has a capacity to reduce Se(VI) to Se(0) in the absence of external electron donors. We conclude that Se sorption onto S. putrefaciens cell walls is the result of the combination of outer-sphere complexation and cell surface reduction. This sorption process leads to a complex reservoir of bound Se which is not entirely reversible.
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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) | 0.000 | 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 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".