Influence of Proteins and Building Direction on the Corrosion and Tribocorrosion of CoCrMo Fabricated by Laser Powder Bed Fusion
Bibliographic record
Abstract
Cobalt–chromium–molybdenum (CoCrMo) alloys are common wear-exposed biomedical alloys and are manufactured in multiple ways, increasingly using additive manufacturing processes such as laser powder bed fusion (LPBF). Here, we investigate the effect of proteins and the manufacturing process (wrought vs LPBF) and building orientation (LPBF- XY and XZ ) on the corrosion, metal release, tribocorrosion, and surface oxide composition by means of electrochemical, mechanical, microscopic, diffractive, and spectroscopic methods. The study was conducted at pH 7.3 in 5 g/L NaCl and 5 mM 2-( N -morpholino) ethanesulfonic acid (MES) buffer, which was found to be necessary to avoid metal phosphate and metal–protein aggregate precipitation. The effect of 10 g/L bovine serum albumin (BSA) and 2.5 g/L fibrinogen (Fbn) was studied. BSA and Fbn strongly enhanced the release of Co, Cr, and Mo and slightly enhanced the corrosion (still in the passive domain) for all CoCrMo alloys and most for LPBF- XZ, followed by LPBF- XY and the wrought CoCrMo. BSA and Fbn, most pronounced when combined, significantly decreased the coefficient of friction due to lubrication, the wear track width and severity of the wear mechanism, and the tribocorrosion for all alloys, with no clear effect of the manufacturing type. The wear track area was significantly more oxidized than the area outside of the wear track. In the reference solution without proteins, a strong Mo oxidation in the wear track surface oxide was indicative of a pH decrease and cell separation of the anodic and cathodic areas. This effect was absent in the presence of the proteins.
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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) | 0.000 | 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".