Mechano-bactericidal nanopillars require external forces to effectively kill bacteria
Bibliographic record
Abstract
Abstract Nanopillars are known to mechanically damage bacteria, suggesting a promising strategy for highly-effective anti-bacterial surfaces. However, the mechanisms underlying this phenomena remain unclear, which ultimately limits translational potential towards real-world applications. Using real-time and end-point analysis techniques, we demonstrate that in contrast to expectations, bacteria on multiple “mechano-bactericidal” surfaces remain viable, unless exposed to a moving air-liquid interface which caused considerable cell death. Reasoning that normal forces arising from surface tension may underlie mechano-bactericidal activity, we developed computational and experimental models to estimate, manipulate, and recreate the impact of these forces. Our experiments together demonstrate that nanopillar surfaces alone do not cause cell death, but require a critical level of external force to deform and rupture bacteria. These studies hence provide fundamental physical insight into the mechanisms by which nanopillar surfaces can serve as effective antibacterial strategies, and describe the use-conditions under which such nanotechnological approaches may provide practical value. Graphical Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".