[en] Research in bioprinting is booming due to its potential in addressing several manufacturing challenges in regenerative medicine. However, there are still many hurdles to overcome to guarantee cell survival and good printability. For the 3D extrusion-based bioprinting, cell viability is amongst one of the lowest of all the bioprinting techniques and is strongly influenced by various factors including the shear stress in the print nozzle. The goal of this study is to quantify, by means of in silico modeling, the mechanical environment experienced by the bioink during the printing process. Two ubiquitous nozzle shapes, conical and blunted, were considered, as well as three common hydrogels with material properties spanning from almost Newtonian to highly shear-thinning materials following the power-law behavior: Alginate-Gelatin, Alginate and PF127. Comprehensive in silico testing of all combinations of nozzle geometry variations and hydrogels was achieved by combining a design of experiments approach (DoE) with a computational fluid dynamics (CFD) of the printing process, analyzed through a machine learning approach named Gaussian Process. Available experimental results were used to validate the CFD model and justify the use of shear stress as a surrogate for cell survival in this study. The lower and middle nozzle radius, lower nozzle length and the material properties, alone and combined, were identified as the major influencing factors affecting shear stress, and therefore cell viability, during printing. These results were successfully compared with those of reported experiments testing viability for different nozzle geometry parameters under constant flow rate or constant pressure. The in silico 3D bioprinting platform developed in this study offers the potential to assist and accelerate further development of 3D bioprinting.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Reina Romo, Esther ✱; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique ; Department of Mechanical Engineering and Manufacturing, University of Seville, Seville, Spain
Mandal, Sourav ✱; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique
Amorim, Paulo; Prometheus, The Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium ; Materials Technology TC, Campus Group T, KU Leuven, Leuven, Belgium
Bloemen, Veerle; Prometheus, The Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium ; Materials Technology TC, Campus Group T, KU Leuven, Leuven, Belgium
Ferraris, Eleonora; Department of Mechanical Engineering, Campus de Nayer, KU Leuven, Leuven, Belgium
Geris, Liesbet ; Université de Liège - ULiège > GIGA > GIGA In silico medecine - Biomechanics Research Unit ; Prometheus, The Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium ; Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium ; Biomechanics Section, Department of Mechanical Engineering, KU Leuven , Leuven, Belgium
✱ These authors have contributed equally to this work.
Language :
English
Title :
Towards the Experimentally-Informed In Silico Nozzle Design Optimization for Extrusion-Based Bioprinting of Shear-Thinning Hydrogels.
Universidad de Sevilla ULiège - Université de Liège F.R.S.-FNRS - Fonds de la Recherche Scientifique ERC - European Research Council
Funding text :
ER-R received support from the “José Castillejo grant” (CAS17/ 00179), This work received financial support from FNRS (grant T.0256.16) and under the European Union’s Horizon 2020 research and innovation programme from the European Research Council (ERC CoG INSITE 772418) and from EU H2020 RIA (JointPromise, 874837). SM acknowledges the “IPD-STEMA” fellowship offered by University of Liège, Belgium.The authors would like to thank Bernard Hocq and Thierry Marchal for Ansys Polyflow license, and Nina Van Steevoort for initial model execution. SM acknowledges Mojtaba Barzegery and other team members from KULeuven for useful discussion sessions.
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