Tribo-Corrosion and Wear Resistance of Biomedical Materials: A Comprehensive Review
Bibliographic record
Abstract
This comprehensive review paper provides an in-depth analysis of the tribo-corrosion behaviour and wear resistance of biomedical materials, focusing on their application in orthopaedic and dental implant settings. Various materials such as titanium alloys, stainless steel, CoCrMo alloy, UHMWPE, and Ti-based alloys are examined for their mechanical, tribological, and corrosion properties. The impact of surface modifications, coatings, and manufacturing techniques on the performance of these materials is thoroughly explored. Experimental investigations and characterization techniques including SEM analysis, X-ray diffraction, nanoindentation, and electrochemical impedance spectroscopy are utilized to assess tribo-corrosion behaviour, wear resistance, and mechanical properties. The significance of specific parameters such as coating thickness, temperature, sliding speed, and load in determining the performance of biomedical materials is highlighted. The review emphasizes the need for continued research and development to enhance the tribological properties of biomedical metallic materials, with promising implications for orthopaedic implant longevity and human health. Keywords: Tribo-corrosion, Wear Resistance, Biomedical Materials, coatings, Pin-on Disc
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".