Modeling of the Mechanical Behavior of 3D Bioplotted Scaffolds Considering the Penetration in Interlocked Strands
Bibliographic record
Abstract
Three-dimensional (3D) bioplotting has been widely used to print hydrogel scaffolds for tissue engineering applications. One issue involved in 3D bioplotting is to achieve the scaffold structure with the desired mechanical properties. To overcome this issue, various numerical methods have been developed to predict the mechanical properties of scaffolds, but limited by the imperfect representation of one key feature of scaffolds fabricated by 3D bioplotting, i.e., the penetration or fusion of strands in one layer into the previous layer. This paper presents our study on the development of a novel numerical model to predict the elastic modulus (one important index of mechanical properties) of 3D bioplotted scaffolds considering the aforementioned strand penetration. For this, the finite element method was used for the model development, while medium-viscosity alginate was selected for scaffold fabrication by the 3D bioplotting technique. The elastic modulus of the bioplotted scaffolds was characterized using mechanical testing and results were compared with those predicted from the developed model, demonstrating a strong congruity between them. Once validated, the developed model was also used to investigate the effect of other geometrical features on the mechanical behavior of bioplotted scaffolds. Our results show that the penetration, pore size, and number of printed layers have significant effects on the elastic modulus of bioplotted scaffolds; and also suggest that the developed model can be used as a powerful tool to modulate the mechanical behavior of bioplotted scaffolds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".