Zan Lamberger, Vivien Priebe, Matthias Ryma, Gregor Lang
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引用次数: 0
Abstract
A key goal of biofabrication is the production of 3D tissue models with biomimetic properties. In natural tissues, fibrils-mainly composed of collagen-play a critical role in stabilizing and spatially organizing the extracellular matrix. To use biomimetic fibers for reinforcing bioinks in 3D printing, fiber fragmentation is necessary to prevent nozzle clogging. However, existing fragmentation methods are often material-specific, poorly scalable, and provide limited control over fragment size and shape. A novel workflow is introduced for producing fiber fragments applicable to various materials and fabrication techniques such as electrospinning, melt-electrowriting, fused deposition modeling, wet spinning, and microfluidic spinning. The method uses a sacrificial membrane as a substrate for precise cryo-sectioning of fibers. A significant advantage is that no additional handling steps, such as fiber detachment or transfer, are needed, resulting in highly reproducible fiber sectioning with a quasi-monodisperse length distribution. The membrane can be rolled before cutting, preventing fibers from sticking together and significantly increasing production efficiency. This method is also versatile, applicable to multiple fiber types and materials without re-parameterization. Cell culture experiments demonstrate that the fibers maintain key properties necessary for cell-fiber interactions, making them suitable for systematic screenings in the development of anisotropic 3D tissue models.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.