Unraveling the complexity of surface antibacterial effects: A multifaceted evaluation of electrodeposited nanospikes
Bibliographic record
Abstract
The intricate interaction between bacteria and surfaces, coupled with the complexity of biofilm formation, poses significant challenges in understanding and controlling microbial behaviour. To combat pathogen contamination and proliferation, bioinspired nanostructures as antimicrobial surfaces have gained prominence over the past decade. However, conflicting theoretical studies and experimental results have made it difficult to draw general conclusions about antibacterial mechanisms. To address these challenges, we have adopted an approach that provides access to deeper insights to characterize the performance of surfaces, by taking into account different modes of antibacterial action. To demonstrate the applicability of this strategy, nanostructured materials mimicking dragonfly wing, were evaluated. A gold electrodeposition process was optimized to create large-area, uniform nanospike arrays, ranging in height from 70 to 570 nm. Large sample area (12 cm²) was critical for ensuring statistically significant results in the analysis of antibacterial effects. Model surfaces with ∼150 nm high spikes were tested, demonstrating significant efficacy in reducing bacterial proliferation for Escherichia coli and Staphylococcus epidermidis . Moreover, striking differences between strains were observed in biofouling (surface detachment and release of bacteria). These findings underscore the relevance of such a protocol in precisely characterizing bacterial interactions with antimicrobial materials. By providing a standardized characterization method, this study aims to facilitate a deeper understanding of antimicrobial surface interactions with bacteria and contributes to the development of more effective antibacterial surfaces.
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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 | 0.000 |
| 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 | 0.001 | 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 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".