Detection of Biofilm Forming Microbes Using Electrochemical Methods
Bibliographic record
Abstract
Abstract Microbiologically Influenced Corrosion (MIC) and biofouling are major challenges to operators who manage water systems in the oil & gas and other sectors. Key to these threats is the formation and accumulation of biofilms in piping systems due to the agglomeration of both biotic components (e.g. microorganisms such as bacteria, archea and extracellular polymeric substances) and abiotic materials (e.g. inorganic solids). These biofilms adhere to inner pipe wall surfaces, and evolve over time depending on surrounding environmental conditions. Manual detection of biofilm formation and bio-fouling often occurs during routine inspection, or during an unexpected system shut down due to a corrosion failure or flow blockage. In this study, a novel in-situ method to detect the presence of biofilm forming bacteria in a fluid system has been proposed based on capacitance measurements using the Electrochemical Impedance Spectroscopy (EIS) technique. Two probe types were used to measure the growth of bacteria: 1) a parallel plate system to measure total bacteria concentration (planktonic and sessile), and 2) a small-scale Interdigitated Electrode (IDE) system to measure the formation of biofilms (sessile bacteria). In both series of tests, Pseudomonas.putida was used as the model bacteria due to its ability to grow rapidly as a biofilm. Upon introduction of the microorganism to the system, EIS patterns were correlated to the observed bacterial concentration which was determined using plating methods to quantify microbial counts over time. Surface areas and gap sizes of the various probes were also evaluated to determine their effect on measured sensitivities. Preliminary tests show that the EIS technique is capable of detecting and quantifying the bacterial counts (i.e. initiation and growth of biofilms). Furthermore, utilizing a model circuit, the effective capacitance at different concentrations of bacteria was determined. This “proof of concept” study demonstrates the potential for a viable, real-time detection method for systems susceptible to bio-fouling and/or MIC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".