The idiosyncratic self-cleaning cycle of bacteria on regularly arrayed mechano-bactericidal nanostructures
Bibliographic record
Abstract
Nanostructured mechano-bactericidal surfaces represent a promising technology to prevent the incidence of microbial contamination on a variety of surfaces and to avoid bacterial infection, particularly with antibiotic resistant strains. In this work, a regular array of silicon nanopillars of 380 nm height and 35 nm diameter was used to study the release of bacterial cell debris off the surface, following inactivation of the cell due to nanostructure-induced rupture. It was confirmed that substantial bactericidal activity was achieved against Gram-negative Pseudomonas aeruginosa (85% non-viable cells) and only modest antibacterial activity towards Staphylococcus aureus (8% non-viable cells), as estimated by measuring the proportions of viable and non-viable cells via fluorescence imaging. In situ time-lapse AFM scans of the bacteria-nanopillar interface confirmed the removal rate of the dead P. aeruginosa cells from the surface to be approximately 19 minutes per cell, and approximately 11 minutes per cell for dead S. aureus cells. These results highlight that the killing and dead cell detachment cycle for bacteria on these substrata are dependant on the bacterial species and the surface architecture studied and will vary when these two parameters are altered. The outcomes of this work will enhance the current understanding of antibacterial nanostructures, and impact upon the development and implementation of next-generation implants and medical devices.
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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".