Effect of Biocides and Corrosion Inhibitors on SRB-mediated MIC under Flow Conditions
Bibliographic record
Abstract
Abstract Biocides and corrosion inhibitors can decrease corrosion in stagnant and flowing systems, like storage tanks and pipelines. We have used 1 ml syringe columns packed with 60 carbon steel beads (55 mg each), which were continuously injected with the effluent of an SRB continuous culture chemostat, to monitor corrosion under flow conditions. A constant flow rate of 0.5 ml/hr was maintained throughout. General corrosion rates (CRs) were determined after 45 days of flow by measuring the weight loss of acid-treated beads. Medium entering the chemostat contained sulfate (10 mM) and formate (20 mM) for the growth of SRB. Effluent of the chemostat with 5 mM sulfate, 5 mM sulfide and high numbers of SRB was then continuously injected into the syringe columns. CRs of beads in these columns were 0.1 mm/yr. Periodic biocide treatment (2 h of 300 ppm every 5 days at the same flow rate) decreased CRs on average by 60% for two of five biocides tested, indicating control of corrosion in the system. In contrast, a single exposure of the carbon steel beads to a water-dissolved corrosion inhibitor at the start of the experiment decreased CR by 50%, whereas single exposure to two diesel-dissolved corrosion inhibitors decreased CR by 90-98%.
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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 | 0.001 |
| 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".