Sulfate-Reducing Bacteria Lower Sulfur-Mediated Pitting Corrosion under Conditions of Oxygen Ingress
Bibliographic record
Abstract
The effect of oxygen ingress into sour water containing dissolved sulfide on the production of sulfur and polysulfide (S-PS) and associated iron corrosion was investigated. Biotic (active SRB present), abiotic (autoclaved SRB present), and chemical (no bacteria present) conditions were compared. Under biotic conditions formation of S-PS was only seen at a high ratio of oxygen to sulfide (R(OS)) of 1 to 2.4. General corrosion rates increased 10-fold to 0.10 mm/yr under these conditions. Under abiotic and chemical conditions S-PS formation increased over the entire range of R(OS) with general corrosion rates reaching 0.06 mm/yr. Although general corrosion rates were thus highest under biotic conditions, biotically corroded coupons showed much less pitting corrosion. Maximum pit depth increased to 40-80 μm with increasing R(OS) for coupons incubated for 1 month under abiotic or chemical conditions but not for biotically incubated coupons (10 μm). This appeared to be related to the properties and size of the sulfur formed, which was hydrophobic and in excess of 10 μm under chemical or abiotic conditions and hydrophilic and 0.5 to 1 μm under biotic conditions. Hence, perhaps contrary to expectation, SRB lowered pitting corrosion rates under conditions of oxygen ingress due to their ability to respire oxygen and produce a less aggressive form of sulfur. Microbial control, which is usually required in sour systems, may be counterproductive under these conditions.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".