Surface Potential Measurement of Bacteria Using Kelvin Probe Force Microscopy
Bibliographic record
Abstract
Surface potential is a commonly overlooked physical characteristic that plays a dominant role in the adhesion of microorganisms to substrate surfaces. Kelvin probe force microscopy (KPFM) is a module of atomic force microscopy (AFM) that measures the contact potential difference between surfaces at the nano-scale. The combination of KPFM with AFM allows for the simultaneous generation of surface potential and topographical maps of biological samples such as bacterial cells. Here, we employ KPFM to examine the effects of surface potential on microbial adhesion to medically relevant surfaces such as stainless steel and gold. Surface potential maps revealed differences in surface potential for microbial membranes on different material substrates. A step-height graph was generated to show the difference in surface potential at a boundary area between the substrate surface and microorganisms. Changes in cellular membrane surface potential have been linked with changes in cellular metabolism and motility. Therefore, KPFM represents a powerful tool that can be utilized to examine the changes of microbial membrane surface potential upon adhesion to various substrate surfaces. In this study, we demonstrate the procedure to characterize the surface potential of individual methicillin-resistant Staphylococcus aureus USA100 cells on stainless steel and gold using KPFM.
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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 | 0.001 | 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".