Factorial Design of Experiment Method to Characterize Bioprinting Process Parameters to Obtain the Targeted Scaffold Porosity.

Due to its inbuilt ability to release biocompatible materials encapsulating living cells in a predefined location, 3D bioprinting is a promising technique for regenerating patient-specific tissues and organs. Among various 3D bioprinting techniques, extrusion-based 3D bioprinting ensures a higher percentage of cell release, ensuring suitable external and internal scaffold architectures. Scaffold architecture is mainly defined by filament geometry and width.

Constraint based Bayesian optimization of bioink precursor: a machine learning framework.

Current research practice for optimizing bioink involves exhaustive experimentation with multi-material composition for determining the printability, shape fidelity and biocompatibility. Predicting bioink properties can be beneficial to the research community but is a challenging task due to the non-Newtonian behavior in complex composition. Existing models such as Cross model become inadequate for predicting the viscosity for heterogeneous composition of bioinks.