Influence of oxidative nanopatterning and anodization on the fatigue resistance of commercially pure titanium and Ti–6Al–4V
Bibliographic record
Abstract
With an increasingly aging population, a significant challenge in implantology is the creation of biomaterials that actively promote tissue integration and offer excellent mechanical properties. Engineered surfaces with micro- and nanoscale topographies have shown great potential to control and direct biomaterial-host tissue interactions. Two simple yet efficient chemical treatments, oxidative nanopatterning and anodization, have demonstrated the ability to confer exciting new bioactive capacities to commercially pure titanium and Ti-6Al-4V alloy. However, the resulting nanoporous and nanotubular surfaces require careful assessment in regard to potential adverse effects on the fatigue resistance, a factor which may ultimately cause premature failure of biomedical implants. In this work, we have investigated the impact of oxidative nanopatterning and anodization on the fatigue resistance of commercially pure titanium and Ti-6Al-4V. Quantitative (e.g., S-N curves) and qualitative analyses were carried out to precisely characterize the fatigue response of treated metals and compare it to that of polished controls. Scanning electron microscopy (SEM) imaging revealed the effects of cyclic loading on the fracture surface and on the structural integrity of chemically grown nanostructured oxides. Results from this study reinforce the importance of mechanical considerations in the development and optimization of micro- and nanoscale surface treatments for metallic biomedical implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".