Tribocorrosion behaviour of nanostructured titanium substrates processed by high-pressure torsion
Bibliographic record
Abstract
Aseptic loosening induced by wear particles from artificial bearing materials is one of the main causes of malfunctioning in total hip replacements. With the increase in young and active patients, complications in revision surgeries and immense health care costs, there is considerable interest in wear-resistant materials that can endure longer in the harsh and corrosive body environment. Here, the tribological behaviour of nanostructured titanium substrates processed by high-pressure torsion (HPT) is investigated and compared with the coarse-grained samples. The high resolution transmission electron microscopy reveals that a nanostructured sample has a grain size of 5-10 nm compared to that of ∼ 10 µm and ∼ 50 µm for untreated and annealed substrates, respectively. Dry and wet wear tests were performed using a linear reciprocating ball-on-flat tribometer. Nanostructured samples show the best dry wear resistance and the lowest wear rate in the electrolyte. There was significantly lower plastic deformation and no change in preferred orientation of nanostructured samples attributable to the wear process. Electrochemical impedance spectroscopy (EIS) shows lower corrosion resistance for nanostructured samples. However, under the action of both wear and corrosion the nanostructured samples show superior performance and that makes them an attractive candidate for applications in which wear and corrosion act simultaneously.
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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) | 0.002 | 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".