Alginate-based hydrogels show the same complex mechanical behavior as brain tissue. (November 2020)

• Record Type: Journal Article
• Authors: Distler, T.; Schaller, E.; Steinmann, P.; Boccaccini, A.R.; Budday, S.
• Is Part Of: Journal of the mechanical behavior of biomedical materials. Volume 111(2020)
• Publication Date: 2020-11
• Subjects: Biomechanics -- Nonlinear material behavior -- Tissue engineering -- Brain tissue -- Alginate-gelatin hydrogels; Biomedical materials -- Periodicals; Biomedical materials -- Mechanical properties -- Periodicals; Biomedical materials; Biomedical materials -- Mechanical properties; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.jmbbm.2020.103979
• Languages: English
• ISSNs: 1751-6161

Abstract

Abstract: Mimicking the mechanical properties of native human tissues is one key route in tissue engineering. However, the successful creation of functional tissue equivalents requires the comprehensive understanding of the complex and nonlinear mechanical properties of both native tissues and biomaterials. Here, we demonstrate that it is possible to replicate the complex mechanical behavior of soft tissues, exemplary shown for porcine brain tissue, under multiple loading conditions, compression, tension, and torsional shear, through simple blends of alginate and gelatin hydrogels. Alginate exhibits a pronounced compression-tension asymmetry and a nonlinear behavior, while gelatin shows an almost linear response. Blended together, alginate-gelatin (ALG-GEL) hydrogels can resemble the characteristic nonlinear, conditioning, and compression-tension-asymmetric behavior of brain tissue. We demonstrate that hydrogel concentration and incubation effectively tune the stiffness and loading-mode-specific stress relaxation behavior. The stiffness increases with increasing hydrogel concentration and decreases with increasing incubation time. In addition, we observe slower stress relaxation after long incubation times. Our systematic approach highlights the importance of single component, multi-modal mechanical analysis of hydrogels to understand the distinct structure-mechanics relation of each hydrogel component to eventually mimic the response of native tissues. The presented dataset will allow for the structurally derived compositional design of hydrogels for a broad variety of tissue engineering applications.