Effects of Surface Topography and Cellular Biomechanics on Nanopillar-Induced Bactericidal Activity
Bibliographic record
Abstract
Bacteria are mechanically resistant biological structures that can sustain physical stress. Experimental data, however, have shown that high-aspect-ratio nanopillars deform bacterial cells upon contact. If the deformation is sufficiently large, it lyses the bacterial cell wall, ultimately leading to cell death. This has prompted a novel strategy, known as mechano-bactericide technology, to fabricate antibacterial surfaces. Although adhesion forces were originally proposed as the driving force for mechano-bactericidal action, it has been recently shown that external forces, such as capillary forces arising from an air-water interface at bacterial surfaces, produce sufficient loads to rapidly kill bacteria on nanopillars. This discovery highlights the need to theoretically examine how bacteria respond to external loads and to ascertain the key factors. In this study, we developed a finite element model approximating bacteria as elastic shells filled with cytoplasmic fluid brought into contact with an individual nanopillar or nanopillar array. This model elucidates that bacterial killing caused by external forces on nanopillars is influenced by surface topography and cell biomechanical variables, including the density and arrangement of nanopillars, in addition to the cell wall thickness and elastic modulus. Considering that surface topography is an important design parameter, we performed experiments using nanopillar arrays with precisely controlled nanopillar diameters and spacing. Consistent with model predictions, these demonstrate that nanopillars with a larger spacing increase bacterial susceptibility to mechanical puncture. The results provide salient insights into mechano-bactericidal activity and identify key design parameters for implementing this technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".