4.6 Review

Bio-memristors based on silk fibroin

Journal

MATERIALS HORIZONS
Volume 8, Issue 12, Pages 3281-3294

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/d1mh01433a

Keywords

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Funding

  1. National Natural Science Foundation of China [51903045, 52173031]
  2. International Cooperation Fund of the Science and Technology Commission of Shanghai Municipality [19520744500]
  3. Basic Research Project of the Science and Technology Commission of Shanghai Municipality [21JC1400100]
  4. Program of Shanghai Academic/Technology Re-search Leader [20XD1400100]
  5. Fundamental Research Funds for the Central Universities [2232019D3-02, 2232019A3-06]
  6. Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University [CUSF-DH-D-2020049]

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Bio-memristors based on silk fibroin exhibit excellent performance and potential, playing important roles in non-volatile storage and bionic synapse fields. They provide new avenues for the design and application of high-performance bio-integrated systems.
Bio-memristors constitute candidates for the next generation of non-volatile storage and bionic synapses due to their biocompatibility, environmental benignity, sustainability, flexibility, degradability, and impressive memristive performance. Silk fibroin (SF), a natural and abundant biomaterial with excellent mechanical, optical, electrical, and structure-adjustable properties as well as being easy to process, has been utilized and shown to have potential in the construction of bio-memristors. Here, we first summarize the fundamental mechanisms of bio-memristors based on SF. Then, the latest achievements and developments of pristine and composited SF-based memristors are highlighted, followed by the integration of memristive devices. Finally, the challenges and insights associated with SF-based bio-memristors are presented. Advances in SF-based bio-memristors will open new avenues in the design and integration of high-performance bio-integrated systems and facilitate their application in logic operations, complex circuits, and neural networks.

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