Fibre Laser Treatment of Beta TNZT Titanium Alloys for Load-Bearing Implant Applications: Effects of Surface Physical and Chemical Features on Mesenchymal Stem Cell Response and Staphylococcus aureus Bacterial Attachment
Bibliographic record
Abstract
A mismatch in bone and implant elastic modulus can lead to aseptic loosening and ultimately implant failure. Selective elemental composition of titanium (Ti) alloys coupled with surface treatment can be used to improve osseointegration and reduce bacterial adhesion. The biocompatibility and antibacterial properties of Ti-35Nb-7Zr-6Ta (TNZT) using fibre laser surface treatment were assessed in this work, due to its excellent material properties (low Young’s modulus and non-toxicity) and the promising attributes of fibre laser treatment (very fast, non-contact, clean and only causes changes in surface without altering the bulk composition/microstructure). The TNZT surfaces in this study were treated in a high speed regime, specifically 100 and 200 mm/s, (or 6 and 12 m/min). Surface roughness and topography (WLI and SEM), chemical composition (SEM-EDX), microstructure (XRD) and chemistry (XPS) were investigated. The biocompatibility of the laser treated surfaces was evaluated using mesenchymal stem cells (MSCs) cultured in vitro at various time points to assess cell attachment (6, 24 and 48 h), proliferation (3, 7 and 14 days) and differentiation (7, 14 and 21 days). Antibacterial performance was also evaluated using Staphylococcus aureus (S. aureus) and Live/Dead staining. Sample groups included untreated base metal (BM), laser treated at 100 mm/s (LT100) and 200 mm/s (LT200). The results demonstrated that laser surface treatment creates a rougher (Ra value of BM is 199 nm, LT100 is 256 nm and LT200 is 232 nm), spiky surface (Rsk > 0 and Rku > 3) with homogenous elemental distribution and decreasing peak-to-peak distance between ripples (0.63 to 0.315 µm) as the scanning speed increases (p < 0.05), generating a surface with distinct micron and nano scale features. The improvement in cell spreading, formation of bone-like nodules (only seen on the laser treated samples) and subsequent four-fold reduction in bacterial attachment (p < 0.001) can be attributed to the features created through fibre laser treatment, making it an excellent choice for load bearing implant applications. Last but not least, the presence of TiN in the outermost surface oxide might also account for the improved biocompatibility and antibacterial performances of TNZT.
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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".