Tuning the Topography of Non‐Wetting Surfaces to Reduce Short‐Term Microbial Contamination Within Hospitals
Bibliographic record
Abstract
Abstract Microbial contamination of hospital surfaces is a major contributor to infectious disease transmission. This work demonstrates that superhydrophobic (Cassie‐Baxter) micro post topographies can significantly reduce cell attachment compared to flat controls. For ordered micro post arrays (post diameters 0.3 to 150 µm), the attachment of four pathogens ( Pseudomonas aeruginosa , Staphylococcus aureus , Escherichia coli , and Candida albicans ) from discrete contaminant droplets upon short‐term contact (15 s to 30 min) are assessed. There is a 3‐4‐log decrease in microbial attachment when reducing the micro posts diameters from 150 to 0.3 µm for all strains, with large posts (>20 µm) exhibiting similar attachment rates to flat controls. The critical, maximum feature size to prevent attachment can be tuned depending on the ratio of the cell size to post diameter. Two potential mechanisms are discussed for this size effect. First, application of the random sequential adsorption model shows that this relative post/cell size effect may be due to a reduced probability of attachment, which is theorized to be the dominant mechanism. Alternatively, a physical model is suggested for bacterial cell “pull‐off” due to surface tension forces during droplet dewetting. This work may be important for the design of non‐wetting antimicrobial surfaces within healthcare environments.
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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".