Kinetics Analysis of a Salt-Tolerant Perchlorate-Reducing Bacterium: Effects of Sodium, Magnesium, and Nitrate
Bibliographic record
Abstract
Salt-tolerant perchlorate-reducing bacteria can be used to regenerate ion-exchange brines or resins exhausted with perchlorate. A salt-tolerant perchlorate-reducing Marinobacter vinifirmus strain P4B1 was recently purified. This study determined the effects of Na(+) and Mg(2+) concentrations on the perchlorate reduction rate of P4B1. The results showed that strain P4B1 could utilize perchlorate and grow in the presence of 1.8% to 10.2% NaCl. Lower NaCl concentrations allowed faster perchlorate reduction. The addition of Mg(2+) to the culture showed significant effects on perchlorate reduction when perchlorate was the sole electron acceptor. A molar Mg(2+)/Na(+) ratio of ∼0.11 optimized perchlorate degradation and cell growth. When perchlorate and nitrate were both present, nitrate reduction did not start significantly until perchlorate was below 100 mg/L. Tests with washed cell suspensions indicated that strain P4B1 had both perchlorate and nitrate reduction enzymes. When the culture was exposed to both perchlorate and nitrate, the nitrate reduction enzyme activity was low. The maximum specific substrate utilization rate (Vm) and the half saturation coefficient (KS) for P4B1 (30 g/L NaCl) determined in this study were 0.049 ± 0.003 mg ClO4(-)/mg VSS-h and 18 ± 4 mg ClO4(-)/L, respectively.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".