Acetic Acid Etching of Mg-xGd Alloys
Bibliographic record
Abstract
Mg-xGd alloys show potential to be used for degradable implants. As rare earth containing alloys, they are also of special interest for wrought products. All applications from medical to engineering uses require a low and controlled degradation or corrosion rate without pitting. Impurities from fabrication or machining, like Fe inclusions, encourage pitting, which inhibits uniform material degradation. This work investigates a suitable etching method to remove surface contamination and to understand the influence of etching on surface morphology. Acetic acid (HAc) etching as chemical surface treatment has been used to remove contamination from the surface. Extruded Mg-xGd (x = 2, 5 and 10) discs were etched with 250 g/L HAc solution in a volume of 5 mL or 10 mL for different times. The microstructure in the near surface region was characterized. Surface characterization was done by SEM, EDS, interferometry, and ToF-SIMS (time-of-flight secondary ion mass spectrometry) analysis. Different etching kinetics were observed due to microstructure and the volume of etching solution. Gd rich particles and higher etching temperatures due to smaller etchant volumes promote the formation of pits. Removal of 2–9 µm of material from the surface was sufficient to remove surface Fe contamination and to result in a plain surface morphology.
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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".