In-Bath 3D Printing of Anisotropic Shape-Memory Cryogels Functionalized with Bone-Bioactive Nanoparticles

材料科学 纳米复合材料 自愈水凝胶 纳米颗粒 纳米技术 制作 高分子化学 医学 病理 替代医学
作者
Edgar J. Castanheira,Luís P. G. Monteiro,Vítor M. Gaspar,Tiago Ruivo Correia,João M. M. Rodrigues,João F. Mano
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
标识
DOI:10.1021/acsami.3c18290
摘要

Cryogels exhibit unique shape memory with full recovery and structural stability features after multiple injections. These constructs also possess enhanced cell permeability and nutrient diffusion when compared to typical bulk hydrogels. Volumetric processing of cryogels functionalized with nanosized units has potential to widen their biomedical applications, however this has remained challenging and relatively underexplored. In this study, we report a novel methodology that combines suspension 3D printing with directional freezing for the fabrication of nanocomposite cryogels with configurable anisotropy. When compared to conventional bulk or freeze-dried hydrogels, nanocomposite cryogel formulations exhibit excellent shape recovery (>95%) and higher pore connectivity. Suspension printing, assisted with a prechilled metal grid, was optimized to induce anisotropy. The addition of calcium- and phosphate-doped mesoporous silica nanoparticles into the cryogel matrix enhanced bioactivity toward orthopedic applications without hindering the printing process. Notably, the nanocomposite 3D printed cryogels exhibit injectable shape memory while also featuring a lamellar topography. The fabrication of these constructs was highly reproducible and exhibited potential for a cell-delivery injectable cryogel with no cytotoxicity to human-derived adipose stem cells. Hence, in this work, it was possible to combine a gravity defying 3D printed methodology with injectable and controlled anisotropic macroporous structures containing bioactive nanoparticles. This methodology ameliorates highly tunable injectable 3D printed anisotropic nanocomposite cryogels with a user-programmable degree of structural complexity.
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