Preparation of Tannic Acid/Hyaluronic Acid Coating to Improve the Corrosion Resistance of Implant Material Based on AZ31B Magnesium Alloy
Bibliographic record
Abstract
Magnesium (Mg) has good biocompatibility, making it suitable as an implant material. However, Mg has a high corrosion rate because of the reaction between magnesium implants and fluids in the human body. To lower the corrosion rate of magnesium alloys, it is necessary to perform a coating process using tannic acid (TA) and hyaluronic acid (HYA), as we have done in this study. TA, an active ingredient, is relatively inexpensive, easy to find, and can effectively reduce the degradation rate. SEM characterization showed that the TA–HYA layer was formed by chelation between the Mg and TA surfaces. Furthermore, adding HYA to the coating covered the cracks caused by the TA layer and increased the hydrophilic properties. In vitro corrosion tests using Tafel polarization showed that the TA–HYA coating reduced the corrosion rate of the magnesium alloy from 7.379 mm/year to 0.204 mm/year. The immersion test in the SBF solution showed that the TA–HYA layer could bind Mg2+, which is beneficial for new bone growth.
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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) | 0.001 | 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".