Donor‐dependent Extent of Uranium Reduction for Bioremediation of Contaminated Sediment Microcosms
Bibliographic record
Abstract
Bioremediation of uranium was investigated in microcosm experiments containing contaminated sediments from Oak Ridge, Tennessee to explore the importance of electron donor selection for uranium reduction rate and extent. In these experiments, all of the electron donors, including ethanol, glucose, methanol, and methanol with added humic acids, stimulated the reduction and immobilization of aqueous uranium by the indigenous microbial community. Uranium loss from solution began after the completion of nitrate reduction but essentially concurrent with sulfate reduction. When electron donor concentrations were normalized for their equivalent electron donor potential yield, the rates of uranium reduction were nearly equivalent for all treatments (0.55-0.95 micromol L(-1) d(-1)). Uranium reduction with methanol proceeded after a 15-d longer lag time relative to that of ethanol or glucose. Significant differences were not found with the inclusion of humic acids. The extent of U reduction in sediment slurries measured by XANES at various time periods after the start of the experiment increased in the order of ethanol (5-7% reduced at 77 and 153 d), glucose (49% reduced at 53 d), and methanol (93% reduced at 90 d). The microbial diversity of ethanol- and methanol-amended microcosms in their late stage of U reduction was analyzed with 16S rRNA gene amplification. Members of the Geobacteraceae were found in all microcosms as well as other potential uranium-reducing organisms, such as Clostridium and Desulfosporosinus. The effectiveness of methanol relative to ethanol at reducing aqueous and sediment-hosted uranium suggests that bioremediation strategies that encourage fermentative poising of the subsurface to a lower redox potential may be more effective for long-term uranium immobilization as compared with selecting an electron donor that is efficiently metabolized by known uranium-reducing microorganisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".