Nanopatterning of steel by one-step anodization for anti-adhesion of bacteria
Bibliographic record
Abstract
Surface nanopatterning of metals has been an effective technique for improved performance and functionalization. However, it is of great challenge to fabricate nanostructure on carbon steels despite their extensive use and urgent needs to maintain the performance reliability and durability. Here, we report a one-step anodization technique to nanopattern a carbon steel in 50 wt.% NaOH solution for highly effective anti-adhesion by sulphate reducing bacteria (SRB), i.e., Desulfovibrio desulfuricans subsp. desulfuricans (Beijerinck) Kluyver and van Niel. We characterize the morphology, structure, composition, and surface roughness of the nanostructured film formed on the steel as a function of anodizing potential. We quantify the surface hydrophobicity by contact angle measurements, and the SRB adhesion by fluorescent analysis. The optimal anodization potential of 2.0 V is determined for the best performance of anti-adhesion of SRB to the steel, resulting in a 23.5 times of reduction of SRB adhesion compared to bare steel. We discuss the mechanisms for the film formation on the steel during anodization, and the high-performance anti-adhesion of bacteria to nanopatterned steels. Our technique is simple, cost-effective and environment-friendly, providing a promising alternative for industry-scale surface nanopatterning of carbon steels for effective controlling of bacterial adhesion.
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".