Silicon Carbide Nanoparticles as an Effective Bioadhesive to Bond Collagen Containing Composite Gel Layers for Tissue Engineering Applications
Bibliographic record
Abstract
Additive manufacturing via layer-by-layer adhesive bonding holds much promise for scalable manufacturing of tissue-like constructs, specifically scaffolds with integrated vascular networks for tissue engineering applications. However, there remains a lack of effective adhesives capable of composite layer fusion without affecting the integrity of patterned features. Here, the use of silicon carbide is introduced as an effective adhesive to achieve strong bonding (0.39 ± 0.03 kPa) between hybrid hydrogel films composed of alginate and collagen. The techniques have allowed us to fabricate multilayered, heterogeneous constructs with embedded high-resolution microchannels (150 µm-1 mm) that are precisely interspaced (500-600 µm). Hydrogel layers are effectively bonded with silicon carbide nanoparticles without blocking the hollow microchannels and high cell viability (90.61 ± 3.28%) is maintained within the scaffold. Nanosilica is also tested and found to cause clogging of smaller microchannels when used for interlayer bonding, but is successfully used to attach synthetic polymers (e.g., Tygon) to the hydrogels (32.5 ± 2.12 mN bond strength). This allows us to form inlet and outlet interconnections to the gel constructs. This ability to integrate hollow channel networks into bulk soft material structures for perfusion can be useful in 3D tissue engineering applications.
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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) | 0.000 | 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 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".