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Processing of Self-Healing Polymers for Soft Robotics

  • Ellen Roels (Corresponding author)
  • , Seppe Terryn
  • , Fumiya Iida
  • , Anton W. Bosman
  • , Sophie Norvez
  • , Frank Clemens
  • , Guy Van Assche
  • , Bram Vanderborght
  • , Joost Brancart

    Research output: Contribution to journalReview articlepeer-review

    890 Downloads (Pure)

    Abstract

    Soft robots are, due to their softness, inherently safe and adapt well to unstructured environments. However, they are prone to various damage types. Self-healing polymers address this vulnerability. Self-healing soft robots can recover completely from macroscopic damage, extending their lifetime. For developing healable soft robots, various formative and additive manufacturing methods have been exploited to shape self-healing polymers into complex structures. Additionally, several novel manufacturing techniques, noted as (re)assembly binding techniques that are specific to self-healing polymers, have been created. Herein, the wide variety of processing techniques of self-healing polymers for robotics available in the literature is reviewed, and limitations and opportunities discussed thoroughly. Based on defined requirements for soft robots, these techniques are critically compared and validated. A strong focus is drawn to the reversible covalent and (physico)chemical cross-links present in the self-healing polymers that do not only endow healability to the resulting soft robotic components, but are also beneficial in many manufacturing techniques. They solve current obstacles in soft robots, including the formation of robust multi-material parts, recyclability, and stress relaxation. This review bridges two promising research fields, and guides the reader toward selecting a suitable processing method based on a self-healing polymer and the intended soft robotics application.

    Original languageEnglish
    Article number2104798
    Number of pages27
    JournalAdvanced Materials
    Volume34
    Issue number1
    DOIs
    Publication statusPublished - 6 Jan 2022

    Funding

    This literature review was performed in relation to and funded by the EU FET Open RIA Project SHERO (828818), the EU Marie Curie ITN project SMART (860108) and the FWO SBO project AMSeR (G028218N). In addition, the authors gratefully acknowledge the FWO (Fonds Wetenschappelijk Onderzoek) for the personal grants of Roels (1S84120N), Terryn (1100416N), and Brancart (12W4719N).

    FundersFunder number
    European Union's Horizon 2020 - Research and Innovation Framework Programme828818
    Fonds Wetenschappelijk Onderzoek12W4719N, 1100416N, 1S84120N

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