Comparative Studies on Tribocorrosion Behaviour of Plasma-Sprayed and Detonation Gun Coatings of Al2O3-13 %TiO2 on Biomedical Alloy Ti-13Nb-13Zr and Gum Metal
Bibliographic record
Abstract
The tribocorrosion behavior of titanium-based alloys is of significant interest as bio-implant materials. Bare alloys may not offer enough resistance to tribocorrosion, so coatings could be used to improve their performance. An important biomedical alloy, Ti-13Nb-13Zr, and a newly developed β titanium alloy called “Gum metal” (Ti-23 %Ni-0.7 %Ta-2 %Zr-1 %O2) were used as substrates in the current work. Both were coated with conventional and nano-ceramic materials of Al2O3-TiO2. Bilayered coatings of ZrO2+Al2O3-13 %TiO2 were also applied to the Ti-13Nb-13Zr using plasma spray. The coatings on Ti-13Nb-13Zr were applied using plasma spray, whereas that on the Gum metal was applied by a detonation gun (D-Gun). Surface morphology was characterized using a scanning electron microscope (SEM). Tribocorrosion experiments were performed in salt water using a linear reciprocating ball-on-plate tribometer with an aluminum ball as the slider. The nano particles are embedded in the fully melted splats and offered better crack propagation resistance. The high velocity of the D-Gun process resulted in a higher volume fraction of the embedded nano particles and produced substantial improvement in wear resistance relative to the air-plasma-sprayed coating. The conventional coating, with its higher porosity, exhibited a high corrosion rate compared to nano coating. The D-Gun coating, with its lower porosity, had a higher corrosion resistance than the plasma-sprayed coating, but bilayered plasma-sprayed coating showed even higher corrosion resistance, owing to its dense microstructure. Open-circuit potential measurements before and during tribocorrosion showed that the bilayered plasma-sprayed coating had better tribocorrosion resistance than the other coatings. Electrochemical impedance spectroscopy indicated stable impedance values for the bilayered plasma-sprayed coating before and after tribocorrosion.
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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) | 0.001 | 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".