Mechanistic insight on the combined effect of albumin and hydrogen peroxide on surface oxide composition and extent of metal release from Ti6Al4V
Bibliographic record
Abstract
Abstract The titanium–aluminium (6 wt%)–vanadium (4 wt%) (Ti6Al4V) alloy is widely used as an orthopedic and dental implant material due to its high corrosion resistance in such environments. The corrosion resistance is usually determined by means of electrochemical methods, which may not be able to detect other chemical surface reactions. Literature findings report a synergistic effect of the combination of the abundant protein albumin and hydrogen peroxide (H 2 O 2 ) on the extent of metal release and corrosion of Ti6Al4V. The objectives of this study were to gain further mechanistic insight on the interplay of H 2 O 2 and albumin on the metal release process of Ti6Al4V with special focus on (1) kinetics and (2) H 2 O 2 and albumin concentrations. This was accomplished mainly by metal release and surface oxide composition investigations, which confirmed the combined effect of H 2 O 2 and albumin on the metal release process, although not detectable by electrochemical open circuit potential measurements. A concentration of 30 m M H 2 O 2 induced substantial changes in the surface oxide characteristics, an oxide which became thicker and enriched in aluminum. Bovine serum albumin (BSA) seemed to be able to deplete this aluminum content from the outermost surface or at least to delay its surface enrichment. This effect increased with increased BSA concentration, and for time periods longer than 24 h. This study hence suggests that short‐term (accelerated) corrosion resistance measurements are not sufficient to predict potential health effects of Ti6Al4V alloys since also chemical dissolution mechanisms play a large role for metal release, possibly in a synergistic way. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 855–867, 2019.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".