A Highly Sensitive Amperometric Sensor for Monitoring Sulfide and Microbiologically Influenced Corrosion
Bibliographic record
Abstract
Abstract Microbiologically influenced corrosion (MIC) has been considered a significant factor contributing to oil and gas pipeline failures. This type of corrosion results from the activities of microorganisms in the biofilms formed on metal surfaces. The in-situ monitoring of MIC is very challenging as it requires a combination of microbiological, surface analytical and electrochemical methods. Sulfate-reducing bacteria (SRB) are considered a predominant cause of MIC and they reduce sulfate to sulfide through anaerobic respiration. Thus the microbial corrosion can be monitored through the detection of biogenic sulfide resulting from the SRB activities. In this paper, an amperometric sensor was constructed for on-line detection of sulfide. Single-walled carbon nanotubes (SWCNTs) functionalized with a conducting polymer poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) were used to facilitate signal transduction. The SWCNT-PEDOT-PSS modified glassy carbon electrode (GCE) sensor exhibited a large linear detection range, short response time, and high sensitivity for detection of sulfide, through direct oxidation of the sulfide without the assistance of any enzyme and mediator. The results paved the way for the development of on-line biosensors for fast and reliable monitoring of MIC related to SRB activities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".