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Photo-tunable hydrogel mechanical heterogeneity informed by predictive transport kinetics model
Callie I Higgins1, Jason P Killgore, Frank W DelRio
1Applied Chemicals and Materials Division, Material Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA. callie.higgins@nist.gov.
Soft Matter
|March 24, 2020
Summary
This study introduces a predictive model to improve 3D bioprinting of complex tissues. The model compensates for material diffusion and swelling, enabling the printing of hydrogels with distinct mechanical properties for better tissue mimicry.
Area of Science:
- Biomaterials Science
- Tissue Engineering
- 3D Bioprinting
Background:
- Mimicking complex biological tissues requires understanding their 3D mechanical and chemical properties.
- 3D printing of hydrogels is challenging due to difficulties in achieving both mechanical robustness and biocompatibility, as well as maintaining structural fidelity.
Purpose of the Study:
- To develop a predictive transport and swelling model to address challenges in 3D hydrogel bioprinting.
- To enable the printing of spatially distinct hydrogel elastic moduli within a single structure.
Main Methods:
- A predictive transport and swelling model was developed and used for compensation during 3D printing.
- Experimental validation involved photopatterning distinct hydrogel elastic moduli using a single photo-tunable poly(ethylene glycol) (PEG) pre-polymer solution.
Main Results:
- The model successfully predicted and compensated for diffusion and swelling effects during hydrogel printing.
- Spatially distinct hydrogel elastic moduli were achieved using a single pre-polymer solution through sequential patterning and in-diffusion.
Conclusions:
- The developed model enhances the fidelity of 3D bioprinting, allowing for the creation of complex hydrogel structures with tunable mechanical properties.
- This approach facilitates the development of advanced biomaterials for mimicking intricate biological tissue properties.
