The role of acetogens in microbially influenced corrosion of steel
Bibliographic record
Abstract
Microbially influenced corrosion (MIC) of iron (Fe(0)) by sulfate-reducing bacteria (SRB) has been studied extensively. Through a mechanism, that is still poorly understood, electrons or hydrogen (H2) molecules are removed from the metal surface and used as electron donor for sulfate reduction. The resulting ferrous ions precipitate in part with the sulfide produced, forming characteristic black iron sulfide. Hydrogenotrophic methanogens can also contribute to MIC. Incubation of pipeline water samples, containing bicarbonate and some sulfate, in serum bottles with steel coupons and a headspace of 10% (vol/vol) CO2 and 90% N2, indicated formation of acetate and methane. Incubation of these samples in serum bottles, containing medium with coupons and bicarbonate but no sulfate, also indicated that formation of acetate preceded the formation of methane. Microbial community analyses of these enrichments indicated the presence of Acetobacterium, as well as of hydrogenotrophic and acetotrophic methanogens. The formation of acetate by homoacetogens, such as Acetobacterium woodii from H2 (or Fe(0)) and CO2, is potentially important, because acetate is a required carbon source for many SRB growing with H2 and sulfate. A consortium of the SRB Desulfovibrio vulgaris Hildenborough and A. woodii was able to grow in defined medium with H2, CO2, and sulfate, because A. woodii provides the acetate, needed by D. vulgaris under these conditions. Likewise, general corrosion rates of metal coupons incubated with D. vulgaris in the presence of acetate or in the presence of A. woodii were higher than in the absence of acetate or A. woodii, respectively. An extended MIC model capturing these results is presented.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".