4.6 Article

Top-Down Synthesis of Hollow Graphene Nanostructures for Use in Resistive Switching Memory Devices

期刊

ADVANCED ELECTRONIC MATERIALS
卷 3, 期 10, 页码 -

出版社

WILEY
DOI: 10.1002/aelm.201700264

关键词

density functional theory calculations; graphene nanorings; hollow nanostructures; quantum confinement effects

资金

  1. National Research Foundation of Korea (NRF) - Korean government [NRF-2014R1A1A3053111, NRF-2016R1D1A1A02937051, NRF-2015R1D1A1A01060480]
  2. Ministry of Science, ICT, and future Planning (MSIP)
  3. Pohang Accelarator Laboratory (PAL)
  4. POSCO TJ Foundation
  5. National Research Foundation of Korea (NRF) - Ministry of Science, and ICT and Future Planning [2009-0082580]
  6. National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2013R1A1A2064456]
  7. GRI (GIST Research Institute) - GIST

向作者/读者索取更多资源

Hollow nanostructures exhibit a wide range of potential applications because of the quantum confinement effect that arises from the narrow width of such nanostructures. It is challenging to synthesize hollow nanostructures of 2D materials. A facile top-down approach is presented for synthesizing high-quality hollow graphene nanostructures from highly oriented pyrolytic graphite target. Hollow graphene nanostructures composed of few-layered graphene nanorings (GNRs) are synthesized in acetone and poly(4-vinylpyridine) (P4VP) using the laser ablation in liquid technique. Density functional theory calculations of the layered GNRs indicate that their bandgap converges with an increase in the number of layers. To demonstrate the feasibility of the synthesized NRs for use in device applications, flexible two-terminal devices with a Pt/GNRs-P4VP/indium tin oxide structure are fabricated; the devices exhibit excellent memory characteristics, with the ON/OFF ratio being 2 x 10(4) and the retention time being 10(5) s. These results suggest that synthesizing uniform bandgap-engineered GNRs in organic media through laser ablation is an excellent method for realizing high-density, low-cost non-volatile memories in a scalable manner.

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