Hydrogel refers to three-dimensional polymer network structures with high water content. They are acknowledged for their ability to emulate the properties of biological tissues given their outstanding mechanical and biocompatible characteristics, but also for their customizability – that is to say, hydrogels can be tailored to suit not only specific applications, but also processing methods. To take full advantage of such desirable features, our team uses additive manufacturing (generically referred to as 3D-printing) as the manufacturing method to add structural customizability on top the mentioned mechanical adjustability. Here we focus on two of the most representative methods of 3D printing, namely, direct ink writing (DIW) and digital light processing (DLP).
Despite their potentials, hydrogel research and 3D printing have faced certain limitations in their respective fields, and merging the two together has yet to throw us new challenges.
The following are key factors we are focusing on to implement our goal in 3D-printing artificial tissues using multifunctional hydrogels. First, various synthetic variables are being controlled, and nano- to macro-scale processes are being developed to bear the wide range of high mechanical properties found in human tissues. Here we tackle both the possible physical modifications (e.g. network structure, chain length) and chemical interactions. Furthermore, one of our main research topics is to be able to engineer even the curvature of stress-strain graphs, and in particular design hydrogels with a strain-stiffening property, which is a mechanical feature biological tissues are often characterized with.
Secondly, we aim to secure the printability of various materials by engineering precursor properties and developing the adequate processing methods parallelly. Despite the many unidentified correlations between variables, we characterize and modulate the rheological properties of materials for printability. The two 3D printing technologies require different hydrogel precursor properties, and we apply DIW or DLP technologies where appropriate.
Our vision is to find the absolute maximum performance of various organic small molecules, thus breaking the current performance barrier. Secondly, machine learning will help determine the complex inter-parameter relationships, which will help researchers understand the in-depth physics of fluid dynamics and crystallization mechanism.
Finally, the unidentified correlations between material properties (e.g. mechanical, rheological) and parameters (e.g. processes, printer type) are significant obstacles that require both manpower and time to explore. We overcome this by introducing machine learning (ML) to analyze and predict correlations. ML algorithms can analyze large experimental datasets to identify patterns and correlations that are not apparent to the naked eye. This enables us to predict the behavior of hydrogels under different conditions, optimize their properties for specific applications, and accelerate the discovery of new materials.
As a final outcome, we have the potential to revolutionize hydrogel research by through accelerated discovery and optimization of new materials and applying 3D-printing for various complex and custom applications.