Synergy of Sodium Nitroprusside and Nitrate in Inhibiting the Activity of Sulfate Reducing Bacteria in Oil-Containing Bioreactors
Bibliographic record
Abstract
Sodium nitroprusside (SNP) disrupts microbial biofilms through the release of nitric oxide (NO). The actions of SNP on bacteria have been mostly limited to the genera Pseudomona, Clostridium and Bacillus. There are no reports of its biocidal action on sulfate-reducing bacteria (SRB), which couple the reduction of sulfate to sulfide with the oxidation of organic electron donors. Here, we report the inhibition and kill of SRB by low SNP concentrations [0.05 mM (15 ppm)] depending on biomass concentration. Chemical reaction of SNP with sulfide did not compromise its efficacy. SNP was more effective than five biocides commonly used to control SRB. Souring, the SRB activity in oil reservoirs, is often controlled by injection of nitrate. Control of SRB-mediated souring in oil-containing bioreactors was inhibited by 4 mM (340 ppm) of sodium nitrate, but required only 0.05 mM (15 ppm) of SNP. Interestingly, nitrate and SNP were found to be highly synergistic with 0.003 mM (1 ppm) of SNP and 1 mM (85 ppm) of sodium nitrate being sufficient in inhibiting souring. Hence, using SNP as an additive may greatly increase the efficacy of nitrate injection in oil reservoirs.
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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 | 0.001 | 0.001 |
| 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".