Albumin Protein Impact on Early-Stage <i>In Vitro</i> Biodegradation of Magnesium Alloy (WE43)
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mg and its alloys are promising biodegradable materials for orthopedic implants and cardiovascular stents. The first interactions of protein molecules with Mg alloy surfaces have a substantial impact on their biocompatibility and biodegradation. We investigate the early-stage electrochemical, chemical, morphological, and electrical surface potential changes of alloy WE43 in either 154 mM NaCl or Hanks’ simulated physiological solutions in the absence or presence of bovine serum albumin (BSA) protein. WE43 had the lowest electrochemical current noise (ECN) fluctuations, the highest noise resistance ( Z n = 1774 Ω·cm 2 ), and the highest total impedance (| Z | = 332 Ω·cm 2 ) when immersed for 30 min in Hanks’ solution. The highest ECN, lowest Z n (1430 Ω·cm 2 ), and |Z| (49 Ω·cm 2 ) were observed in the NaCl solution. In the solutions containing BSA, a unique dual-mode biodegradation was observed. Adding BSA to a NaCl solution increased | Z | from 49 to 97 Ω·cm 2 and decreased the ECN signal of the alloy, i.e., the BSA inhibited corrosion. On the other hand, the presence of BSA in Hanks’ solution increased the rate of biodegradation by decreasing both Z n and | Z | while increasing ECN. Finally, using scanning Kelvin probe force microscopy (SKPFM), we observed an adsorbed nanolayer of BSA with aggregated and fibrillar morphology only in Hanks’ solution, where the electrical surface potential was 52 mV lower than that of the Mg oxide layer.
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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".