A Simple Surface Treatment for Mg to Gain Enhanced Resistance to Corrosion and Corrosive Wear by Hammering Al Powder‐Covered Mg Substrate
Bibliographic record
Abstract
Abstract Mg is abundant in the earth crust and very attractive as a base metal of lightweight alloys for the transportation industry due to its high strength‐to‐weight ratio. However, Mg is prone to corrosion, which largely limits its widespread applications. Here, a very simple surface treatment method is demonstrated for enhanced resistance to corrosion and corrosive wear by hammering Al powder‐covered surface of Mg with subsequent recovery treatment. Such treated surface is examined with X‐ray diffraction technique for information on phases, nanocrystallization, thickness, and coverage of the Al layer. Structure of the Al layer and lattice imperfections are analyzed with transmission electron microscopy. Electrochemical behavior, corrosion, and corrosive wear of the treated surface are evaluated. It is shown that the treated surface is nanocrystalline with significantly increased resistance to corrosion and corrosive wear. The effect of recovery heat treatment on microstructure of the Al layer is particularly characterized and demonstrated to be essential for achieving high degrees of nanocrystallization and structure integrity through eliminating lattice defects generated during the hammering process, leading to a desired nanocrystalline structure with high stability and superior properties. Molecular dynamics simulation is also performed to gain an insight into the underlying mechanism.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".