Electrochemical removal of anodic aluminium oxide templates for the production of phase-pure cuprous oxide nanorods for antimicrobial surfaces
Bibliographic record
Abstract
Antimicrobial surfaces are ones that incapacitate or kill pathogens landing on them, which could allow for self-sanitising surfaces for hospitals or implants, ensuring healthier stays and procedures. Cuprous compounds such as Cu2O are especially effective at incapacitating both viruses and bacteria, and nanorod arrays have been shown to prevent the adhesion of pathogens and mechanically deform bacteria to the point that their cell walls rupture. A Cu2O nanorod array should therefore allow for the exploitation of both of these effects. In the present work, an electrochemical method is introduced, where Cu2O nanorods formed in a substrate-supported anodic aluminium oxide (AAO) template are held at a stable electrochemical potential throughout the removal of the AAO template. This avoids the partial reduction of the nanorods from Cu2O to Cu that was observed during chemical removal of the template, which was attributed to the presence of residual aluminium from the template fabrication process that reacts with the etchant and lowers the electrochemical potential of the nanorods to a value that favours reduction. Using the electrochemical removal method, the reliable production of phase-pure, free-standing, crystalline Cu2O nanorod arrays on ITO/glass substrates is demonstrated. This simple method is compatible with nanorod arrays of any size.
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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".