Corrosion of Carbon Steel by a Thermophilic Sulfate-Reducing Consortium Enriched from Oilfield-Produced Waters
Bibliographic record
Abstract
Corrosion of metal infrastructure due to microbial activity has been widely reported in many sectors and has been frequently studied under mesophilic conditions (<50°C). However, less is known about this degradation process at thermophilic (>50°C) temperatures that characterize many oil- and gas-producing operations. We used a thermophilic sulfate-reducing consortium (TSRM) enriched from offshore-produced water fluids to determine microbial corrosion of mild carbon steel at 60°C in the presence or absence of an organic electron donor (lactate or volatile fatty acids) and in the presence of riboflavin, a redox mediator previously reported to enhance microbial corrosion by pure isolates. Incubations of the TSRM consortium showed the highest corrosion rate in the absence of an organic electron donor, suggesting that the carbon steel itself served as an electron donor. Higher corrosion rates corresponded to increased numbers of localized pits formed. Scanning electron micrographs showed microbial cells with elongated filaments incubations when Fe0 served as an electron donor, potentially contributing to the direct uptake of electrons from iron. The addition of 20 ppm riboflavin did not enhance corrosion rates by the mixed TSRM consortium under the tested conditions. Microbial community analysis showed the TSRM culture to contain diverse anaerobic taxa and substantially distinct planktonic and coupon surface-attached communities. Overall, this study showed that thermophilic microbial communities containing sulfate-reducers can contribute to the corrosion of metal infrastructure operated or maintained at higher temperatures even in the absence of organic substrates, provided sulfate is present.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".