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Electric Field-Induced Cutting of Hydrogel Microfibers with Precise Length Control for Micromotors and Building Blocks.

Xiaokang DengYukun RenLikai HouWeiyu LiuYankai JiaHongyuan Jiang
Published in: ACS applied materials & interfaces (2018)
Microfiber modules with controllable lengths emerged as novel biomimetic platforms and are significant for many tissue engineering applications. However, accurately controlling the length of microfibers on the scale of millimeter or even micrometer still remains challenging. Here, a novel and scalable strategy to generate microfiber modules with precisely tunable lengths ranging from 100 to 3500 μm via an alternating current (AC) electric field is presented. To control the microfiber length, double-emulsion droplets containing a chelating agent (sodium citrate) as a spacing node are first uniformly embedded in the microfibers in a controllable spatial arrangement. This process is precisely tuned by adjusting the flow rates, thus, tailoring the resulting multicompartmental microfiber structure. Next, an AC voltage signal is used to trigger the electric field-induced cutting process, where the time-averaged electrical force acting on the induced bipolar charge from the Maxwell-Wagner structural polarization mechanism breaks the stress balance at the interfaces, rupturing the double-emulsion droplets, and resulting in the burst release of the encapsulated chelating agents into the hydrogel cavity. The outer hydrogel shell is quickly dissolved by a chemical reaction, cutting the long fiber into a series of microfiber units of given length. Furthermore, adding magnetic nanoparticles endows magnetic functionality with these microfiber modules, which are allowed to serve as micromotors and building blocks. This electro-induced cutting method provides a facile strategy for the fabrication of microfibers with desired lengths, showing considerable promise for various chemical and biological applications.
Keyphrases
  • tissue engineering
  • high glucose
  • diabetic rats
  • drug delivery
  • oxidative stress
  • endothelial cells
  • lymph node
  • wound healing
  • big data
  • machine learning
  • artificial intelligence
  • network analysis