High Intensity Laser Induced Reverse Transfer: Solution for Enhancement of Biocompatibility of Transparent Biomaterials
Bibliographic record
Abstract
Bioactive glass is used extensively in biomedical applications due to its quality and effectiveness in tissue regeneration. Bioactive glasses are able to interact with biological systems and can be used in humans to improve tissue regeneration without any side effects. Bioactive glass is a category of glasses that maintain good contact with body organs and remain biocompatible for a long time after implementation. They have the potential to form a hydroxyapatite surface as a biocompatible layer after immersion in body fluid. In this research, glass biocompatibility was modified using a deposition method called the high intensity laser induced reverse transfer (HILIRT) method and they were utilized as enhanced-biocompatibility bioactive glass (EBBG) with a correspondent nanofibrous titanium (NFTi) coating. HILIRT is a simple ultrafast laser method for improving implants for biomedical applications and provides a good thin film of NFTi on the glass substrate that is compatible with human tissue. The proposed method is a non-chemical method in which NFTi samples with different porosities and biocompatibilities are synthesized at various laser parameters such as power and frequency. Physical properties and cell compatibility and adhesion of these NFTi before and after immersion in simulated body fluid (SBF) were compared. The results indicate that increasing laser intensity and frequency leads to more NFTi fabrication on the glass with no toxicity and better cell interaction and adhesion.
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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 | 0.001 | 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".