journal article·2018-08

Dialdehyde cellulose nanocrystal/gelatin hydrogel optimized for 3D printing applications

Yani Jiang, Jiping Zhou, Zhe Yang, Dongfang Liu, Xiaodong Xv, Guoqi Zhao, Hongcan Shi, Qi Zhang

Journal of Materials Science· Volume 53· Issue 16
Publisher page

Abstract

Three-dimensional (3D) printing, used to fabricate modular and patient-specific scaffolds with high structural complexity and design flexibility, has drawn wide attentions in the tissue engineering area. However, one of the key problems hindering the application and development of 3D printing in TE area is the poor mechanical property of bio-inks. In this work, we aimed to design a high-strength hydrogel system based on dialdehyde cellulose nanocrystals (DAC) and gelatin (GEL) as a new bio-ink for 3D printing of scaffolds. The DAC were prepared and used as a natural crosslinker to interact with the GEL through a Schiff base reaction. The mechanical test results indicated that the breaking strength of the optimal 4:8-DAC/GEL sample was almost 41.3-fold greater than that of the GEL hydrogel. According to the rheological test results, the 4:8-DAC/GEL sample incubated for 3 h was proposed as a bio-ink for 3D printing. Then, the printing conditions, including the printing pressure and nozzle speed, as well as additional crosslinking conditions for a freshly printed scaffold, i.e., the crosslinking time and temperature, were optimized. Crosslinked scaffolds with adjustable porosity and good fidelity were successfully obtained. The biocompatibility of 4:8-DAC/GEL was also investigated by CCK-8 and Hoechst 33342/PI double-staining assays. Collectively, these results confirm the good potential of the 4:8-DAC/GEL hydrogel as a 3D bio-ink for application in tissue repair.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: 3D Printing in Biomedical Research · Additive Manufacturing and 3D Printing Technologies · Electrospun Nanofibers in Biomedical Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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