Electrochemistry of Nanostructured Features on Steel Surface and the Applications
Bibliographic record
Abstract
Functionalization of pipeline steels through facile nano-techniques is valuable for industrial applications. In this research, the mechanistic aspects of steel corrosion at nanoscale has been studied in order to manipulate the development of corroded nanostructure on pipeline steel. High-performance nanocoatings capable of anti-bioadhesion and self-cleaning have been successfully developed on pipeline steels through facile electrochemical anodization methods. When the X100 pipeline steel is either corroded or passivated in aqueous environments, the development of nanostructures on the steel surface highly depends on the early-stage corrosion behavior, where the thermodynamics and kinetics are affected by the conditions such as surface finish, electrolyte concentration, and electrochemical potential. The nanostructure on steel substrate shows a quick-response to changes of the conditions, either caused by exposure to corrosive electrolytes, or electrochemical potential. The surface features are under a non-equilibrium state lasting from hundreds to thousands of seconds, during which the corrosion processes of the steel were successfully characterized by topographic in-situ mapping through electrochemical atomic force microscopy. It is demonstrated that the corroded nanostructure on pipeline steel can be controlled through manipulating conditions in order to achieve various functions. Nanostructured coating can be fabricated by anodization of pipeline steels in a concentrated alkaline solution. The nanostructure of the coatings can reduce the interactive force between microorganisms and the steel, resulting into anti-bioadhesion to sulfate-reducing bacteria (SRB) and P. aeruginosa. The photocatalytic property of iron oxides in the nanocoatings enables the release of toxic and oxidative reactive oxygen species (ROSs) under light illumination, enhancing the anti-bioadhesion performance up to 99.9 % compared to bare steel. Further treatment by dipping ZnAc solution and annealing allows the formation of ZnFe2O4 in the nanocoating, improving the electrochemical stability of the nanocoating in corrosive environments while maintaining a high performance in anti-bioadhesion and self-cleaning of residual bacteria (up to 99.3 % of total coverage) on the steel.
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".