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Computational Modeling and Experimental Characterization of Extrusion Printing into Suspension Baths.

Margaret E PrendergastJason A Burdick
Published in: Advanced healthcare materials (2021)
The extrusion printing of inks into suspension baths is an exciting tool, as it allows the printing of diverse and soft hydrogel inks into 3D space without the need for layer-by-layer fabrication. However, this printing process is complex and there have been limited studies to experimentally and computationally characterize the process. In this work, hydrogel inks (i.e., gelatin methacrylamide (GelMA)), suspension baths (i.e., agarose, Carbopol), and the printing process are examined via rheological, computational, and experimental analyses. Rheological data on various hydrogel inks and suspension baths is utilized to develop computational printing simulations based on Carreau constitutive viscosity models of the printing of inks within suspension baths. These results are then compared to experimental outcomes using custom print designs where features such as needle translation speed, defined in this work as print speed, are varied and printed filament resolution is quantified. Results are then used to identify print parameters for the printing of a GelMA ink into a unique guest-host hyaluronic acid suspension bath. This work emphasizes the importance of key rheological properties and print parameters for suspension bath printing and provides a computational model and experimental tools that can be used to inform the selection of print settings.
Keyphrases
  • hyaluronic acid
  • drug delivery
  • type diabetes
  • machine learning
  • metabolic syndrome
  • molecular dynamics
  • electronic health record
  • artificial intelligence
  • wound healing
  • deep learning
  • ultrasound guided