Effect of needle geometry on flow rate and cell damage in the dispensing‐based biofabrication process
Bibliographic record
Abstract
Biodispensing techniques have been widely applied in biofabrication processes to deliver cell suspensions and biomaterials to create cell-seeded constructs. Under identical operating conditions,two types of dispensing needles—tapered and cylindrical—can result in different flow rates of material and different cell damage percent induced by the mechanical forces. In this work, mathematical models of both flow rate and cell damage percent in biodispensing systems using tapered and cylindrical needles, respectively, were developed, and experiments were carried out to verify the effectiveness of the developed models. Both simulations and experiments show tapered needles produce much higher flow rates under the same pressure conditions than cylindrical needles. Use of a lower pressure in a tapered needle can therefore achieve the same flow rate as that in a cylindrical needle. At equivalent flow rates, cell damage in a tapered needle is lower than that in a cylindrical one. Both Schwann cells and 3T3 fibroblasts, which have been widely used in tissue engineering, were used to validate the cell damage models. Application of the developed models to specify the influence of process parameters, including needle geometry and air pressure, on the flow rate and cell damage percent represents a significant advance for biofabrication processes.The models can be used to optimize process parameters to preserve cell viability and achieve the desired cell distribution in dispensing-based biofabrication.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".