Projects per year
Hydrogels comprised of alginate and gelatin have demonstrated potential as biomaterials in three dimensional (3D) bioprinting applications. However, as with all hydrogel-based biomaterials used in extrusion-based bioprinting, many parameters influence their performance and there is limited data characterising the behaviour of alginate-gelatin (Alg-Gel) hydrogels. Here we investigated nine Alg-Gel blends by varying the individual constituent concentrations. We tested samples for printability and print accuracy, compressive behaviour and change over time, and viability of encapsulated mesenchymal stem cells in bioprinted constructs. Printability tests showed a decrease in strand width with increasing concentrations of Alg-Gel. However due to the increased viscosity associated with the higher Alg-Gel concentrations, the minimum width was found to be 0.32 mm before blends became too viscous to print. Similarly, printing accuracy was increased in higher concentrations, exceeding 90% in some blends. Mechanical properties were assessed through uniaxial compression testing and it was found that increasing concentrations of both Alg and Gel resulted in higher compressive modulus. We also deemed 15 min crosslinking in calcium chloride to be sufficient. From our data, we propose a blend of 7%Alg-8%Gel that yields high printability, mechanical strength and stiffness, and cell viability. However, we found the compressive behaviour of Alg-Gel to reduce rapidly over time and especially when incubated at 37 °C. Here we have reported relevant data on Alg-Gel hydrogels for bioprinting. We tested for biomaterial properties and show that these hydrogels have many desirable characteristics that are highly tunable. Though further work is needed before practical use in vivo can be achieved.
|Number of pages||8|
|Journal||Journal of The Mechanical Behavior of Biomedical Materials|
|Publication status||Published - 1 Mar 2018|
1/01/15 → 31/12/18
Engineering better clinical outcomes: Improving abdominal aortic aneurysm risk assessment through patient-specific computational modelling
1/01/14 → 30/09/18