Vapor phase growth and oxygen vacancy engineering of ultrathin 2D Co <sub>3</sub> O <sub>4</sub> nanosheets for self-rectifying memristors
Two-dimensional (2D) materials show great promise for building next-generation memristors. However, their application in self-rectifying memristors (SRMs)—crucial for suppressing sneak-path currents in high-density arrays—is still underexplored. In this work, we address this gap by developing ultrathin non-layered Co<sub>3</sub>O<sub>4</sub> nanosheets through a vapor-phase growth strategy and precisely engineering their oxygen vacancies for high-performance SRMs. Our synergistic approach, combining salt-assisted vapor-liquid-solid, hydrate-assisted, and spatial confinement methods, enables the controlled synthesis of high-quality Co<sub>3</sub>O<sub>4</sub> nanosheets as thin as 0.46 nm with a single-atomic-layer thickness. We demonstrate that magnetically driven rapid thermal annealing (MD-RTA) effectively increases the oxygen vacancy (O<sub>v</sub>) concentration from 15.15% to 33.15%, as quantitatively confirmed by XPS, Raman, and KPFM. The resulting memristor exhibits excellent self-rectifying resistive switching behavior, with a high rectification ratio exceeding 10⁴ and a large ON/OFF ratio over 10<sup>4</sup>. The device also achieves high switching uniformity (coefficient of variation, <em>C</em><sub>v</sub> = 0.0979), stable cycling endurance over 100 DC cycles, and room-temperature operation. This study provides a reliable synthesis route for 2D non-layered materials and highlights defect engineering as an effective strategy for developing advanced in-memory computing devices with inherent crosstalk immunity.
Authors
- Zhengong Meng (ORCID: https://orcid.org/0000-0002-9859-0429)
- Chenyang Zha (ORCID: https://orcid.org/0000-0002-7491-9146)
- Linghai Zhang (ORCID: https://orcid.org/0000-0003-0536-5765)
- Yuhao Kong
- Mouyuan Liu
- Siling Liu
- Yang Du
- Xiangdong Yang
- Yujie Bai
- Huifang Ma
- Jianhong Zhang
- Shiyu Chen
- Jianhong Yang
Publication Details
- Journal
- Nano Research
- Published
- 2026-05-01
- DOI
- https://doi.org/10.26599/nr.2026.94908794
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00