Insights into Increasing Selenate Reductase Enzyme Activity in the Presence of Nitrogen-Doped Graphite Electrodes for Selenium Effluent Treatment
Bibliographic record
Abstract
The weathering of selenium-rich rocks or anthropogenic activities such as mining or smelting can release selenium into the environment, posing a significant environmental risk. The increased monitoring and enforcement of selenium regulations have resulted in protocols to efficiently measure and treat selenium in water and effluent water. The principal aqueous forms of inorganic selenium are selenite (Se(IV)) and selenate (Se(VI)). Selenate, due to its oxy-anionic nature, high mobility, and lack of affinity to conventional adsorbents, is typically more difficult to treat and remove. Thus, it is proposed to remove selenate from water by first reducing it to selenite and then to insoluble elemental selenium, a form that has low toxicity. A naturally occurring selenate reductase enzyme from Thauera selenatis was previously shown to specifically reduce selenate to selenite. To exploit this functionality, recombinant enzyme technologies were used to produce a cell-free, enriched Thauera selenatis selenate reductase heterotrimeric enzyme complex (TsSer-αβγ). The addition of the recombinant enzyme complex to effluent water was found to successfully reduce the selenate. Interestingly, upon adding nitrogen-doped graphite electrodes to the reaction, the selenate-reducing activity significantly increased. Overall, these findings highlight a new, potentially sustainable solution to the reduction of selenate in water and effluent water.
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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".