Bioactive Interlinked Extracellular Matrix–Like Silicon Nano-Network Fabricated by Femtosecond Laser Synthesis
Bibliographic record
Abstract
Nanostructured silicon has proven to be a promising candidate in tissue engineering. However, recent research on fabrication of silicon scaffolds has been limited to expensive, complex, and time-consuming lithographic techniques that require the addition of caustic chemicals. Moreover, these techniques generate structures that do not truly mimic the extracellular matrix (ECM). Therefore, we introduce a novel, interlinked, silicon nano-network fabricated by MHz ultrafast laser synthesis. We demonstrate that ultrafast laser synthesis is simple, rapid, free of any chemical additions, and can be carried out under ambient conditions. Variation in laser parameters resulted in an alteration in the pore size and density of the silicon fibrous network. Microscopic analysis revealed a highly charged silicon network with elevated adhesion forces. In vitro bioactivity tests indicate the precipitation of bone-like apatite in just 3 days. Cell proliferation studies on the silicon nano-network present a 300% increase in comparison to its bulk counterpart. Scanning electron microscopy analysis shows healthy migration and attachment of cells on the silicon nano-network. This study points to a correlation between elevated cell proliferation and the ECM-like structure of the silicon nano-network. This ECM-like silicon nano-network suggests significant potential not only in tissue engineering and regeneration but also in other biomedical applications such as biosensor detection.
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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".