3D Titania Nanofiber-Like Webs Induced by Plasma Ionization: A New Direction for Bioreactivity and Osteoinductivity Enhancement of Biomaterials
Bibliographic record
Abstract
In this study, we describe the formation method of web-like three-dimensional (3-D) titania nanofibrous structures coated on transparent substrate via a high intensity laser induced reverse transfer (HILIRT) process. First, we demonstrate the mechanism of ablation and deposition of Ti on the glass substrates using multiple picosecond laser pulses at ambient air in an explicit analytical form and compare the theoretical results with the experimental results of generated nanofibers. We then examine the performance of the developed glass samples coated by titania nanofibrous structures at varied laser pulse durations by electron microscopy and characterization methods. We follow this by exploring the response of human bone-derived mesenchymal stem cells (BMSCs) with the specimens, using a wide range of in-vitro analyses including MTS assay (colorimetric method for assessing cell metabolic activity), immunocytochemistry, mineralization, ion release examination, gene expression analysis, and protein adsorption and absorption analysis. Our results from the quantitative and qualitative analyses show a significant biocompatibility improvement in the laser treated samples compared to untreated substrates. By decreasing the pulse duration, more titania nanofibers with denser structures can be generated during the HILIRT technique. The findings also suggest that the density of nanostructures and concentration of coated nanofibers play critical roles in the bioreactivity properties of the treated samples, which results in early osteogenic differentiation of BMSCs.
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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".