Photocatalytic anti-bioadhesion and bacterial deactivation on nanostructured iron oxide films
Bibliographic record
Abstract
Bacterial adhesion and biofilm formation on metals are a primary mechanism causing integrity degradation and failure of engineering structures. Conventional anti-bioadhesion methods usually impact the sustainability of environments and ecosystems. In this work, nanostructured iron oxide films were fabricated by electrochemical anodization of steel in a concentrated alkaline solution. The morphology, surface roughness, composition and photoelectrochemical properties of the nano-films were characterized, and the anti-adhesion properties of the films to Pseudomonas aeruginosa bacteria were investigated. A photoelectrochemical based model was developed to explain mechanistically the photocatalytic anti-bioadhesion and bacterial deactivation of the iron oxide nano-films. Results demonstrate that the nanostructured iron oxide films enable effective anti-adhesion of the bacteria to the filmed steel. The photocatalytic activity of the nano-films further deactivates the bacteria remaining on the specimen surface under visible light illumination. In particular, the nano-film formed by 10 min of anodization features the largest surface roughness, the highest photocatalytic activity, and the best performance for anti-bioadhesion and bacterial deactivation. Compared to bare steel, a 99.9% anti-bioadhesion performance is achieved. The nanostructured iron oxide films can deactivate the bacteria remaining on the film surface due to oxidative holes and reactive oxygen species generated during photo illumination.
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) | 0.000 | 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".