4.8 Article

Reproducible and low-power multistate bio-memristor from interpenetrating network electrolyte design

期刊

INFOMAT
卷 4, 期 11, 页码 -

出版社

WILEY
DOI: 10.1002/inf2.12350

关键词

bio-memristor; chitosan; interpenetrating network electrolyte; reproducible multistate; structural stability

资金

  1. Overseas Expertise Introduction Projects for Discipline Innovation [B14003]
  2. China Postdoctoral Science Foundation [2021M700379]
  3. Fundamental Research Funds for Central Universities [FRFTP-18-001C1]
  4. National Key Research and Development Program of China [2018YFA0703500]
  5. National Natural Science Foundation of China [51991340, 51991342, 52072029, 52102153, 52188101]

向作者/读者索取更多资源

In this study, a reproducible and low-power multistate bio-memristor was developed by designing a chitosan-reduced graphene oxide interpenetrating network electrolyte. The bio-memristor showed stable resistive switching, reproducible multistate storage, and low power consumption.
Bio-memristor can address the inherent limitations of conventional memory components in artificial perceptual systems due to their biocompatibility with biological tissue. The actual deployment of bio-memristor is restricted by the lack of reproducibility, high power consumption, and insufficient storage capacity. Here, a reproducible and low-power multistate bio-memristor is developed by designing the chitosan (CS)-reduced graphene oxide (rGO) interpenetrating network electrolyte. The interpenetrating network structure of the CS-rGO electrolyte reinforces structural stability and improves ionic conductivity. The bio-memristor equipped with CS-rGO active layer shows stable bipolar resistive switching up to 100 consecutive cycles, reproducible multistate storage with six different memory states, and low programming power of 9.4 mu W. The fabricated biocompatible CS-rGO device also exhibits deformation stability of memory operation over 10(3) bending cycles, high biocompatibility with HEK293 cells, and skin adhesion. This work provides an enlightening design strategy to develop high-performance bio-memristors for applications in artificial perceptual systems.

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