Name
Technical Session XI - MSEC-155173
Date & Time
Thursday, June 26, 2025, 10:30 AM - 10:55 AM
Description
The field of tissue engineering has advanced significantly with the rise of extrusion-based bioprinting, a technique that uses shear forces to generate filaments for intricate tissue structures. The printability and stability of bioprinted constructs depend on the bioink's rheological properties, especially viscosity, which varies with shear rate for non-Newtonian bioinks. Given that the shear rate at the nozzle tip and the resulting viscosity can change significantly during extrusion, it is essential to assess how bioink composition impacts this important behavior. This study employed a comprehensive rheological analysis on a novel bioink, referred to as ALGEC, which is composed of ALginate, GElatin, Cellulose derivative. ALGEC bioinks were prepared with varying concentrations of alginate (2-5.25%), gelatin (2-5.25%), and TEMPO-mediated nanofibrillated cellulose (0.5-1%) were tested across shear rates from 0.1 to 100 s⁻¹. An 8% total solid content was maintained, and 169 rheological measurements were split for model training (80%) and validation (20%). A predictive model was developed based on a polynomial fit (PF) utilizing experimental data to estimate viscosity of ALGEC based on its components and shear rate. The predictive model achieved a coefficient of determination (R²) of 0.95 and a mean absolute error (MAE) of 0.28, underscoring its potential as a tool for optimizing bioink formulations tailored for extrusion-based bioprinting. These findings contribute to advancing tissue engineering by enhancing our understanding of bioink printability, enabling the fabrication of more reliable and structurally sound tissue constructs.
Location Name
Gardenia
Full Address
Hyatt Regency
220 N Main St
Greenville, SC 29601
United States
Session Type
Technical Session
Paper #
MSEC-155173
Author List
Rokeya Sarah, Riley Rohauer, Kory Schimmelpfennig, Shah Limon, Christopher Lewis, Md Ahasan Habib
Paper Title
Development of a Predictive Model to Optimize Bioink Formulations Tailored for Extrusion-Based Bioprinting
Session Chair
Congrui Jin, Qiming Wang