Electrokinetic Control of Bacterial Deposition and Transport
Bibliographic record
Abstract
Microbial biofilms can cause severe problems in technical installations where they may give rise to microbially influenced corrosion and clogging of filters and membranes or even threaten human health, e.g. when they infest water treatment processes. There is, hence, high interest in methods to prevent microbial adhesion as the initial step of biofilm formation. In environmental technology it might be desired to enhance bacterial transport through porous matrices. This motivated us to test the hypothesis that the attractive interaction energy allowing cells to adhere can be counteracted and overcome by the shear force induced by electroosmotic flow (EOF, i.e. the water flow over surfaces exposed to a weak direct current (DC) electric field). Applying EOF of varying strengths we quantified the deposition of Pseudomonas fluorescens Lp6a in columns containing glass collectors and on a quartz crystal microbalance. We found that the presence of DC reduced the efficiency of initial adhesion and bacterial surface coverage by >85%. A model is presented which quantitatively explains the reduction of bacterial adhesion based on the extended Derjaguin, Landau, Verwey, and Overbeek (XDLVO) theory of colloid stability and the EOF-induced shear forces acting on a bacterium. We propose that DC fields may be used to electrokinetically regulate the interaction of bacteria with surfaces in order to delay initial adhesion and biofilm formation in technical installations or to enhance bacterial transport in environmental matrices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".