Modeling of the Flow Rate in the Dispensing-Based Process for Fabricating Tissue Scaffolds
Bibliographic record
Abstract
Made from biomaterials, tissue scaffolds are three-dimensional (3D) constructs with highly interconnected pore networks for facilitating cell growth and flow transport of nutrients and metabolic waste. To fabricate the scaffolds with complex structures, dispensing-based rapid prototyping technique has been employed recently. In such a fabrication process, the flow rate of biomaterial dispensed is of importance since it directly contributes to the pore size and porosity of the scaffold fabricated. However, the modeling of the flow rate has proven to be a challenging task due to its complexity. This paper presents the development of a model for the flow rate in the scaffold fabrication process based on the fundamentals of fluid mechanics. To verify the effectiveness of the developed model, experiments were carried out, in which the chitosan solution (2% w/v) in acetic acid was used for dispensing under different applied pressures (50kPa, 100kPa, 150kPa, 200kPa, and 250kPa) and needle heater temperatures (25°C, 35°C, 50°C, and 65°C). The measured flow rates were used to identify the flow behavior of the solution and were compared to the predictions from the developed model to illustrate the model effectiveness. Based on the developed model, simulations were carried out to identify the effects of the needle size and the flow behavior on the flow rate in the scaffold fabrication process. The developed model was also applied to determine the dispensing conditions for fabricating 3D scaffolds from a 50% chitosan-hydroxyapatite colloidal gel. As an example, a scaffold fabricated with a well-controlled internal structure of diameters of 610±43μm and pore sizes of 850±75μm in the horizontal plane and of 430±50μm in the vertical direction is presented in this paper to illustrate the promise of the developed model as applied to the 3D scaffold fabrication.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".