Autobigrafía del margen: el poema en prosa en Seamus Heaney y Geoffrey Hill
Bibliographic record
Abstract
Nanopillar-textured surfaces are of growing interest because of their ability to kill bacteria through physical damage without relying on antimicrobial chemicals. Although research on antibacterial nanopillars has progressed significantly in recent years, the effect of nanopillar hydrophobicity on bactericidal activity remains elusive. In this study, we investigated the mechano-bactericidal efficacy of etched silicon nanopillars against <i>Pseudomonas aeruginosa</i> at nanopillar hydrophobicities from superhydrophilic to superhydrophobic. Assessing cell viability and bacterial morphology in immersed wet conditions, we observed negligible bactericidal activity; however, air/liquid interface displacement during water evaporation established a bactericidal effect that strongly depends on substrate hydrophobicity. Specifically, bactericidal activity was highest on superhydrophilic surfaces but abated with increasing hydrophobicity, diminishing at substrate contact angles larger than 90°. Calculation of the surface tension and Laplace pressure forces during water evaporation for each substrate subsequently highlighted that the total capillary force, as an external driving force responsible for bacterial deformation, is significantly weaker on hydrophobic substrates. These findings suggest that superhydrophilic nanopillared surfaces are a superior choice for mechano-bactericidal activity, whereas superhydrophobic surfaces, although not bactericidal, may have antibiofouling properties through their self-cleaning effect. These findings provide new insights into the design and application of nanopillared surfaces as functional antibacterial materials.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".