Bacteria–Polymeric Membrane Interactions: Atomic Force Microscopy and XDLVO Predictions
Bibliographic record
Abstract
Atomic force microscopy (AFM) in conjunction with a bioprobe developed using a polydopamine wet adhesive was used to directly measure the adhesive force between bacteria and different polymeric membrane surfaces. Bacterial cells of Pseudomonas putida and Bacillus subtilis were immobilized onto the tip of a standard AFM cantilever, and force measurements made using the modified cantilever on various membranes. Interaction forces measured with the bacterial probe were compared, qualitatively, to predictions by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory with steric interactions included. The XDLVO theory predicted attractive interactions between low energy hydrophobic membranes with high energy hydrophilic bacterium (P. putida). It also predicted a shallow primary maximum with the most hydrophilic bacterium, B. subtilis . Discrepancies between predictions using the XDLVO theory and theory require involvement of factors such as bridging effects. Differences in interaction between P. putida and B. subtilis are attributed to acid-base interactions and steric interactions. P. putida is Gram negative with lipopolysaccharides present in the outer cell membrane. A variation in forces of adhesion for bacteria on polymeric membranes studied was interpreted in terms of hydrophilicity and interfacial surface potential calculated from physicochemical properties.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".