Estimation of Chemical and Mechanical Factors Affecting Tribocorrosion, Volume Loss, and Metal Release
Bibliographic record
Abstract
Tribocorrosion is the synergistic action of mechanical wear and chemical corrosion. The repassivation (reformation of the surface oxide) plays a significant role in the extent of volume loss and metal release caused by metal oxidation. However, a too rapid repassivation can also increase the hardness of the interface resulting in larger mechanical wear rates, and consequently volume loss. Chemical complexation can hinder the repassivation and therefore increase the overall metal release and metal oxidation rates. The solution chemistry largely affects the precipitation rates of wear debris (including nanoparticles of corrosion products) and the lubrication at the interface, which primarily affects the mechanical wear and volume loss. By polarizing the sample during a tribocorrosion test, mechanical wear and chemical wear can be distinguished using the assumption that mechanical wear prevails under cathodic polarization and that Faraday’s law can be used to estimate the chemical wear fraction under anodic polarization. Here, we present a number of examples and factors aimed to improve the interpretation and usage of tribocorrosion tests. We combined multiple techniques with electrochemical/mechanical tribocorrosion tests of several alloys (Ti6Al4V, stainless steel 316L, and Co28Cr6Mo, both wrought and additively manufactured); laser scanning confocal microscopy to calculate the volume loss, scanning electron microscopy to image the wear track, inductively coupled plasma mass spectrometry to measure the amount of metals in solution, and X-ray photoelectron spectroscopy (XPS) inside and outside of the wear track to compare the surface oxide composition in the anodic and cathodic sites. A study on wrought 316L in salt and cassava flour found that cassava was able to hinder repassivation, increase the metal release, and lubricate the interface resulting in less volume loss, while still showing a similar specific wear rate than the salt solution reference. Wrought 316L exposed to phosphate-buffered saline (PBS), PBS with bovine serum albumin (BSA), and PBS, BSA, and hydrogen peroxide, provided several insights. It showed that the presence of BSA strongly reduced the mechanical wear, while increasing the metal release through complexation. When hydrogen peroxide was added, it rapidly increased the repassivation after a rupture of the surface oxide, which, however, did not result in lower tribocorrosion due to mixed mechanical/oxidative wear modus, which vastly increased the volume loss. The manufacturing method played a role as well, as shown for wrought versus additively manufactured (laser powder bed fusion) Ti6Al4V alloy. The additively manufactured titanium alloy was harder due to a finer grain structure, which resulted in lower tribocorrosion rates. Spot analysis of the surface composition inside and outside the wear track of the CoCrMo alloy revealed acidification of the wear track, as evidenced by higher amounts of oxidized Mo, in solutions without proteins, and a buffering effect of the proteins (no enrichment of oxidized Mo) in their presence. Distinguishing mechanical from chemical wear requires accurate potential selection and control, accurate current increase estimates, and a knowledge on the number of electrons expected in the corresponding metal oxidation, which can be tricky for some alloys. Figure 1
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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 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".