Influence of crosslinking on the mechanical behavior of 3D printed alginate scaffolds: Experimental and numerical approaches
Bibliographic record
Abstract
Tissue scaffolds fabricated by three-dimensional (3D) bioprinting are attracting considerable attention for tissue engineering applications. Because the mechanical properties of hydrogel scaffolds should match the damaged tissue, changing various parameters during 3D bioprinting has been studied to manipulate the mechanical behavior of the resulting scaffolds. Crosslinking scaffolds using a cation solution (such as CaCl2) is also important for regulating the mechanical properties, but has not been well documented in the literature. Here, the effect of varied crosslinking agent volume and crosslinking time on the mechanical behavior of 3D bioplotted alginate scaffolds was evaluated using both experimental and numerical methods. Compression tests were used to measure the elastic modulus of each scaffold, then a finite element model was developed and a power model used to predict scaffold mechanical behavior. Results showed that crosslinking time and volume of crosslinker both play a decisive role in modulating the mechanical properties of 3D bioplotted scaffolds. Because mechanical properties of scaffolds can affect cell response, the findings of this study can be implemented to modulate the elastic modulus of scaffolds according to the intended application.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".