Enhancement of biocompatibility of 316LVM stainless steel by cyclic potentiodynamic passivation
Bibliographic record
Abstract
Passivation of stainless steel implants is a common procedure used to increase their biocompatibility. The results presented in this work demonstrate that the electrochemical cyclic potentiodynamic polarization (CPP) of a biomedical grade 316LVM stainless steel surface is a very efficient passivation method that can be used to significantly improve the material's general corrosion resistance and thus its biocompatibility. The influence of a range of experimental parameters on the passivation/corrosion protection efficiency is discussed. The passive film formed on a 316LVM surface by using the CPP method offers a significantly higher general corrosion resistance than the naturally grown passive film. The corresponding relative corrosion protection efficiency measured in saline during a 2-month period was 97% +/- 1%, which demonstrates a very high stability of the CPP-formed passive film. Its high corrosion protection efficiency was confirmed also at temperatures and chloride concentrations well above normal physiological levels. It was also shown that the CPP is a significantly more effective passivation method than some other surface-treatment methods commonly used to passivate biomedical grade stainless steels. In addition, the CPP-passivated 316LVM surface showed an enhanced biocompatibility in terms of preosteoblast (MC3T3) cells attachment. An increased thickness of the CPP-formed passive film and its enrichment with Cr(VI) and oxygen was determined to be the origin of the material's increased general corrosion resistance, whereas the increased surface roughness and surface (Volta) potential were suggested to be the origin of the enhanced preosteoblast cells attachment.
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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 | 0.001 |
| 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".