A combination of Al diffusion and surface nanocrystallization of carbon steel for enhanced corrosion resistance
Bibliographic record
Abstract
Surface nanocrystallization is beneficial to the corrosion resistance of passive alloys, but generally has a negative effect on the corrosion behavior of non-passive alloys due to the enhanced surface reactivity. In this study, a combination of Al diffusion treatment and surface nanocrystallization was applied to carbon steel with the aim of exploring an alternative approach to improve the corrosion resistance of non-passive carbon steel. The surface nanocrystallization was achieved by sandblasting and subsequent recovery treatment. The former resulted in severe plastic deformation, while the latter turned high-density dislocation cells into nano-sized grains. The present study demonstrates that the combined Al diffusion and nanocrystallization generated a nanocrystalline Al-containing surface layer on the carbon steel with its surface grain diameter in the range of 10–300 nm. The corrosion resistance of the treated steel was evaluated. It is demonstrated that treated specimens possess increased resistance to corrosion with higher surface electron stability. Surface microstructure of the treated specimens was examined using SEM, AFM, and EDS in order to elucidate the mechanism responsible for the improved corrosion resistance.
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".