Extrusion bioprinting from a fluid mechanics perspective
Bibliographic record
Abstract
Bioprinting is an emerging technology for fabricating intricate and diverse structures that closely mimic natural tissues and organs for such applications as tissue engineering, drug delivery, and cancer research as well. Among the various bioprinting techniques, extrusion-based bioprinting stands out due to its capability to apply a wide range of biomaterials and living cells and its controllability over printed structures. In bioprinting, the bioink stored in a syringe is forced to flow through the nozzle connected to the syringe, and then to exit and deposit onto the printing stage to form three-dimensional (3D) structures. The bioprinting process involves the flow of bioink in both syringe and nozzle and then its flow or spreading on a printing stage. As a result, fluid mechanics plays a crucial role in extrusion bioprinting. Notably, the biomaterials used in bioprinting are typically non-Newtonian fluids, which have complex viscoelastic and thixotropic behaviors; and the influence of these behaviors on the bioprinting process has been drawn considerable attention by employing various methods, including the numerical simulations via computational fluid dynamics (CFD). This paper reviews the latest development in the fluid mechanics aspects of extrusion-based bioprinting to shed light on the challenges and key considerations involved. It covers the topics of extrusion bioprinting (including driving mechanisms, printability, cell viability), biomaterial rheology and its effect on bioprinting, multi-material bioprinting and numerical simulation of bioprinting. Key issues and challenges are also discussed along with the recommendations for future research.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".