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    Biomaterials

    (C-85) Structure-Property Predictive Model for Solvent-Cast 3D-Printed Polymeric Biomaterials

    Friday, October 13, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row C - Poster # 85

      Presenting Author(s)

    • Santiago Lazarte (he/him/his)

      Undergraduate Research Assistant
      Florida State University
      Tallahassee, Florida, United States

      • Co-Author(s)

    • JT

      John Tolbert

      Graduate Student
      Lehigh University, United States

      • Last Author(s)

    • BK

      Brandon Krick

      Associate Professor
      Florida State University, United States

      • Primary Investigator(s)

    • LC

      Lesley Chow

      Associate professor
      Lehigh University, United States

    Introduction::

    Biomaterial scaffolds are designed to support tissue growth and repair damaged tissue. Three-dimensional (3D) printing has gained popularity over the years as a method to produce and design scaffolds for tissue engineering. These techniques provide easier control over multiple properties of the printed structures at the macroscale (i.e., complex architectures, material properties) and microscale (i.e., pore size, biochemical properties) [1]. The properties of 3D-printed scaffolds can therefore be tuned to influence cell response, such as human mesenchymal stromal cells (hMSC) differentiation.


    In many load-bearing tissue regeneration applications, including osteochondral (bone-cartilage) tissues, the mechanical properties of the scaffold must be locally tuned to perform a physiological function while new tissue is forming. This work explores how scaffold architecture and polymer molecular weight affect mechanical behavior and its relationship to a mechanical model. Here, a model based on a structure made of Euler-Bernoulli beams was developed to explain and predict the mechanical behavior of 3D-printed scaffolds. This approach enables us to design scaffold properties to match and support the physiological function of native tissue and improve desired cellular responses.



    Materials and Methods::

    Ink preparation – Inks were prepared by blending 80 kDa and 25 kDa polycaprolactone (PCL) at different molecular weight ratios (80:25 kDa): 100:00, 90:10, and 80:20. The PCL blends were  dissolved in 1,1,1,3,3,3,-Hexafluoro-2-propanol (HFIP) at a total polymer concentration of 370 mg/mL for 48 hours with agitation and rested for 24 hours before printing. Printing – Inks were solvent-cast 3D printed using a 3-axis EV Series Automated Dispensing System. Scaffolds were printed with an offset orthogonal pattern with 260 µm programmed filament spacing. Print pressure and print speed were adapted to ensure consistent scaffold architectures and filament diameters across all groups. Mechanical characterization – A customized microindenter was used to perform a series of quasistatic compression experiments on the scaffolds to measure effective compressive modulus. Model – A mechanical model for the scaffolds was developed where filaments were analyzed as Euler-Bernoulli beams as springs in series and parallel.



    Results, Conclusions, and Discussions:: Scaffolds were successfully 3D printed and mechanically characterized. The measured compressive modulus between the 100:00 and 90:10 scaffolds was not significant (Figure 1A). However, we observed a significant decrease in the compressive modulus of 80:20 scaffolds compared to 100:00 and 90:10 groups. Interestingly, the measured compressive moduli did not match our predicted compressive moduli. Differences between our model and measured compressive moduli can be attributed to morphology variations in the 3D-printed filaments due to wetting. This discrepancy led us to modify our model to account for wetting of the filaments during printing (Figure 1B). The corrected model more accurately predicted the modulus for the 90:10 and 100:00 blend. Despite having small variations, the model is a great tool to plan and target specific mechanical properties by controlling key parameters during SCP. Ongoing and future work will investigate taking more direct and representative measurements of key elements in the printed structures that play an important role in the model. The base model also allows us to predict and print scaffolds with a compressive modulus in a range from 104 Pa to 107 Pa.

    Acknowledgements (Optional): : This work was generously supported by the National Science Foundation (NSF) through a Faculty Early Career Development (CAREER) awards (DMR 1944914 to LWC; CMMI 2027029 to BAK).

    References (Optional): :

    [1]      [1] G. H. Wu and S. H. Hsu, “Review: Polymeric-based 3D printing for tissue engineering,” J Med Biol Eng, vol. 35, no. 3, pp. 285–292, Jun. 2015, doi: 10.1007/s40846-015-0038-3.