Bioactivity Enhancement of Titanium Induced by Nd: Yag Laser Pulses
Bibliographic record
Abstract
PURPOSE: In this research, the effect of laser properties such as laser power and laser dwell time on the surface morphology and oxidizing of titanium have been investigated in order to enhance the bioactivity of laser textured titanium sheets. METHODS: The Ti samples were irradiated with nanosecond pulses to create the predetermined point patterns on the surface of sample sheets with specific laser parameters. Final bioactivity of the treated samples were evaluated through the use of simulated body fluid (SBF), followed by material characterization techniques such as X-ray diffraction (XRD) and energy dispersive (EDX). RESULTS: It was observed that by increasing the roughness of the titanium surface samples using a range of dwelling time, and with different powers, titania with higher levels of surface energy in micro/sub-micro scales are produced. The use of laser results in a one-step heat increase and the oxidation of titanium, which results in creation of titania with higher cell adhesion abilities. CONCLUSIONS: It was concluded that the variation of the surface roughness, surface morphology, and oxidation level of the material has a direct effect on the cell adhesion rate to the surface of the titanium. Upon completion of the analysis, it is concluded that using a higher power and a lower dwelling time results in better bioactivity improvement than using higher dwelling times and lower powers.
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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".