Antifouling nanoplatform for controlled attachment of <i>E. coli</i>
Bibliographic record
Abstract
Abstract Biofouling is the most common cause of bacterial contamination in implanted materials/devices resulting in severe inflammation, implant mobilization, and eventual failure. Since bacterial attachment represents the initial step toward biofouling, developing synthetic surfaces that prevent bacterial adhesion is of keen interest in biomaterials research. In this study, we develop antifouling nanoplatforms that effectively impede bacterial adhesion and the consequent biofilm formation. We synthesize the antifouling nanoplatform by introducing silicon (Si)/silica nanoassemblies to the surface through ultrafast ionization of Si substrates. We assess the effectiveness of these nanoplatforms in inhibiting Escherichia coli (E. coli) adhesion. The findings reveal a significant reduction in bacterial attachment on the nanoplatform compared to untreated silicon, with bacteria forming smaller colonies. By manipulating physicochemical characteristics such as nanoassembly size/concentration and nanovoid size, we further control bacterial attachment. These findings suggest the potential of our synthesized nanoplatform in developing biomedical implants/devices with improved antifouling properties.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".