Plenty of research has been completed regarding the different materials we use to create biomedical parts. Many innovative bioinks – biomaterials loaded with cells to 3D print biological structures – have been developed for 3D bioprinting purposes, from materials like stem cells, gelatin hydrogels, and even sugarcane waste. 3D bioprinting itself is changing the field of medicine as we know it, because we can now fabricate patient-specific human tissues in a laboratory setting.
However, this technology only works if researchers and doctors have good bioinks on hand…and how do we know the materials are good? It’s expensive, difficult, and can take a long time to evaluate if these bioinks are 3D printable. That’s why many many researchers, like a team from the Wallenberg Wood Science Center (WWSC) in Sweden are starting to rely more and more on computer simulations to optimize these biomaterials.
Kajsa Markstedt, a PhD student of chemistry and chemical engineering and biopolymer technology at WWSC, and her colleagues recently partnered up with Johan Göhl’s Computational Engineering and Design team at the Fraunhofer Chalmers Centre (FCC) to test out a process for using a computational fluid dynamics tool to model the way bioinks are dispensed.
“As well as allowing us to evaluate the printability of a bioink, simulations could also help us choose the printing technique that should be employed depending on the target tissue. Such techniques vary depending on the viscosity and nature of the ink being printed, and include ink-jet printing, laser-induced forward transfer, microvalve- and extrusion-based bioprinting,” said Markstedt.
“To model how a bioink is dispensed, we used its mass flow rate and density as input in our calculations. These parameters are the ones most commonly evaluated in experiments when printing designs such as lines, grids or cylinders.”
The team published a paper, titled “Simulations of 3D bioprinting: predicting bioprintability of nanofibrillar inks,” in the Biofabrication journal; co-authors include Göhl, Markstedt, Andreas Mark, Karl Håkansson, Paul Gatenholm, and Fredrik Edelvik.
The abstract reads, “To fulfill the multiple requirements of a bioink, a wide range of materials and bioink composition are being developed and evaluated with regard to cell viability, mechanical performance and printability. It is essential that the printability and printing fidelity is not neglected since failure in printing the targeted architecture may be catastrophic for the survival of the cells and consequently the function of the printed tissue. However, experimental evaluation of bioinks printability is time-consuming and must be kept at a minimum, especially when 3D bioprinting with cells that are valuable and costly. This paper demonstrates how experimental evaluation could be complemented with computer based simulations to evaluate newly developed bioinks. Here, a computational fluid dynamics simulation tool was used to study the influence of different printing parameters and evaluate the predictability of the printing process. Based on data from oscillation frequency measurements of the evaluated bioinks, a full stress rheology model was used, where the viscoelastic behaviour of the material was captured.”
According to Markstedt, 3D printability of a bioink is most often determined by the ratio of line width to the diameter of a 3D printer’s nozzle, the curvature of 3D printed lines, and how many layers can be printed before structure collapse. The FCC scientists also used a dynamic contact-angle model, which uses surface tension and a contact angle as input, to the bioinks’ wettability on a substracte.
“In our simulations, we also used the printing path of a grid structure as input,” Markstedt said.
The full rheology model was based on the material’s viscoelastic behavior and the ink-oscillation frequency data obtained in the team’s experiments. For cellulose nanofibril (CNF) bioinks with different rheological properties, simulations produced outcomes that were similar to experimental results in lab evaluations. Additionally, the researchers could use the computer model the follow the real-time 3D printing process and study the behavior of various inks during dispensing.
Markstedt said, “In experimental evaluations, we often only have the properties of the final, printed grid structure to go on. This is a time-consuming way to develop new bioinks or to optimize printing parameters for a specific ink. It is also expensive since the prepared bioink containing cells is precious.”
It’s also important to test the biostructure soon after it’s 3D printed, because the cells are still viable at that point; this limits how long evaluations can last.
“This often leads to many bioinks being printed at printing parameters that have not been optimized for a specific bioink composition. The result is that the right architecture is not produced, which can be catastrophic because the printed tissue does not function properly,” said Markstedt. “For example, the printed line may be too thin causing the structure to break, or too thick, which prevents nutrients and oxygen reaching all the cells in the bioink.”
The researchers are fairly certain that their new simulation tool will be able to provide them with far more feedback during 3D printing, like how viscoelastic- and shear stresses are distributed in the ink, while still surmounting all of these issues.
Markstedt said, “This provides a better understanding of why certain printer settings and bioinks work better than others. For example, it allows us to isolate individual parameters, such as printing speed, printer nozzle height, ink flow rate and printing path to study how they influence printing.”
The team will now work on modeling bioink flow inside nozzle geometries that are pre-defined.
“This addition to the model will allow us to observe what effect shear stresses from the nozzle have on the printing process. This will help us to determine how different printing pressures and nozzle shapes affect the bioprintability of a bioink,” explained Göhl.
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[Source: Physics World / Images: Göhl et. al.]