Oxidative nanopatterning of titanium generates mesoporous surfaces with antimicrobial properties
Bibliographic record
Abstract
Mesoporous surfaces generated by oxidative nanopatterning have the capacity to selectively regulate cell behavior, but their impact on microorganisms has not yet been explored. The main objective of this study was to test the effects of such surfaces on the adherence of two common bacteria and one yeast strain that are responsible for nosocomial infections in clinical settings and biomedical applications. In addition, because surface characteristics are known to affect bacterial adhesion, we further characterized the physicochemical properties of the mesoporous surfaces. Focused ion beam (FIB) was used to generate ultrathin sections for elemental analysis by energy-dispersive X-ray spectroscopy (EDS), nanobeam electron diffraction (NBED), and high-angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) imaging. The adherence of Staphylococcus aureus, Escherichia coli and Candida albicans onto titanium disks with mesoporous and polished surfaces was compared. Disks with the two surfaces side-by-side were also used for direct visual comparison. Qualitative and quantitative results from this study indicate that bacterial adhesion is significantly hindered by the mesoporous surface. In addition, we provide evidence that it alters structural parameters of C. albicans that determine its invasiveness potential, suggesting that microorganisms can sense and respond to the mesoporous surface. Our findings demonstrate the efficiency of a simple chemical oxidative treatment in generating nanotextured surfaces with antimicrobial capacity with potential applications in the implant manufacturing industry and hospital setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".