Usage of Oleic and Stearic Acids in Mixed Anaerobic Culture for Sulfate Reduction While Inhibiting Methanogenesis
Bibliographic record
Abstract
In a mixed anaerobic biotreatment process, sulfate is converted into sulfide by sulfate-reducing bacteria (SRB). Sulfidogens and methanogens in a mixed anaerobic culture can compete for electrons. Diverting a larger fraction of electrons into the sulfate-reduction process can inhibit methanogens and increase overall anaerobic digestion process efficiency. The purpose of this study was to examine the efficiency of SRB in the sulfate-reduction process in presence of two 18 carbon long-chain fatty acids (LCFAs), namely oleic acid (OA), and stearic acid (SA), under mesophilic conditions. Sulfate reduction was examined using glucose in a mixed mesophilic anaerobic culture at 37°C and neutral pH in the presence OA and SA. The control experiments with LCFAs plus sulfate and sulfate alone showed no improvement in sulfate reduction due to the addition of LCFAs (OA and SA), with sulfate reductions of 10% and 9% over 7 days, respectively. In control experiments with glucose and sulfate, sulfate reduction was around 32% over the same period. In experiments with glucose and sulfate, increasing OA concentrations resulted in an increase in electron flux to sulfidogenesis, while negatively impacting glucose degradation rates. Maximum sulfate reduction of 73% over 7 days was observed at the highest OA concentration of 1,000 mg/L, which is about a threefold improvement over the control. Despite methanogenic inhibition and diversion of electron flow toward sulfate reduction in the presence of OA, no H2 was detected. In contrast to OA, no significant increase in sulfate reduction was observed with increasing concentration of SA, up to the maximum tested concentration of 1,000 mg/L. In general, the findings indicate that OA can be effective in redirecting more electrons and thus improving sulfate reduction during the anaerobic treatment process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".