Effect of selected biocides on microbiologically influenced corrosion caused by Desulfovibrio ferrophilus IS5
Bibliographic record
Abstract
The marine bacterial strain Desulfovibrio ferrophilus IS5, known for its lithotrophic growth ability to use metallic iron as a sole electron donor and for causing corrosion of steel, was used in the current study. Four commonly used biocides in the oil and gas industry, namely tetrakis(hydroxymethyl) phosphonium sulfate (THPS), glutaraldehyde (GLUT), benzalkonium chloride (BAC), and GLUT/BAC were selected to study their efficacy in controlling carbon steel corrosion in the presence of this strain. Incubations containing strain IS5 and low carbon steel coupons were prepared in the presence and absence of the four biocides, and these were monitored using both electrochemical methods (electrochemical impedance spectroscopy, linear polarization resistance and potentiodynamic polarization) and surface analyses (scanning electron microscopy, confocal measurements, optical microscopy, and profilometry) to assess the biofilm/metal interactions. When THPS, BAC, and GLUT/BAC treatments were applied, minimal corrosion was measured by all methods. In contrast, severe pitting was observed in the presence of 50 ppm GLUT, similar to what was observed when D. ferrophilus IS5 was incubated in the absence of biocide, suggesting that GLUT alone may not be effective in controlling MIC in marine environments. This study also showed that the use of non-destructive electrochemical methods is effective for screening for real time biocide selection and monitoring of the impact of chemicals post-dosage in oil and gas operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".