Synthesis of bio-functionalized three-dimensional titania nanofibrous 3 using femtosecond laser ablation
Bibliographic record
Abstract
The primary objective of current tissue regeneration research is to synthesize nano-based platforms that 24 can induce guided, controlled, and rapid healing. Titanium nanotubes have been extensively considered 25 as a new biomaterial for biosensors, implants, cell growth, tissue engineering, and drug delivery systems. 26 However, cell adhesion to nanotubes is poor due to their chemical inertness, as well as the one-dimen- 27 sional structure, and surface modification is required to enhance nanotube–cell interaction. While there 28 have been a considerable number of studies on growing titanium nanotubes, synthesizing a three-dimen- 29 sional (3-D) nano-architecture which can act as a growth support platform for bone and stem cells has 30 not been reported so far. Therefore, we present a novel technique to synthesize and grow 3-D titania 31 interwoven nanofibrous structures on a titanium substrate using femtosecond laser irradiation under 32 ambient conditions. This surface architecture incorporate the functions of 3-D nano-scaled topography 33 and modified chemical properties to improve osseointegration while at the same time leaving space to 34 deliver other functional agents. The results indicate that laser pulse repetition can control the density 35 and pore size of engineered nanofibrous structures. In vitro experiments reveal that the titania nanofi- 36 brous architecture possesses excellent bioactivity and can induce rapid, uniform, and controllable 37 bone-like apatite precipitation once immersed in simulated body fluid (SBF). This approach to synthesiz- 38 ing 3-D titania nanofibrous structures suggests considerable promise for the promotion of Ti interfacial 39 properties to develop new functional biomaterials for various biomedical applications.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".