Rheological and viscoelastic properties of collagens and their role in bioprinting by micro-extrusion
Bibliographic record
Abstract
This article aims to understand the rheology of collagen networks and their role in various stages of a bioprinting process while building tissue-like constructs. The science of rheology, which deals with the deformation and flow of matter, has grown considerably from its earlier focus on polymer melts and solutions and their processing methods to hydrogels with new processing procedures, such as bioprinting. The main objective of this paper is to discuss the impact of the rheology of collagen hydrogels on micro-extrusion and layer-stacking stages of bioprinting. Generally, the rheological characterization of hydrogels, including collagens by dynamic measurements under small deformations, is considered sufficient to evaluate their bioprinting performance. However, we brought out the importance of other rheological properties of collagen networks, such as steady-state shear flow conditions and large amplitude oscillator shear. While the dynamic measurements under small deformations help characterize the crosslinking and gel formations of the collagen, the steady shear flow measurements are better tools for investigating filament micro-extrusion and layer-stacking stages of a bioprinting process. We brought the role of other non-Newtonian material functions, such as first normal stress difference and extensional viscosity in addition to shear viscosity, for the first time. Extensional viscosity and the viscoelasticity manifested through normal-stress differences are significant in capillary (needle) flow. We also suggested caution to use dynamic viscosity vs. oscillation frequency under small deformations in place of steady shear viscosity vs. shear rate measurement. In addition, we brought out the importance of the large amplitude oscillatory shear test to investigate the collagen networks under large deformations. Finally, we discussed the role of crosslinking and flow conditions on cell viability. Those discussions are focused on collagen networks; nevertheless, they are valid on the bioprinting of other hydrogels.
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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 | 0.000 |
| 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) | 0.001 | 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".