This study describes the hydrothermal growth of ZnO nanostructures on few-layer graphene sheets and their optical and structural properties. The ZnO nanostructures were grown on graphene sheets of a few layers thick (few-layer graphene) without a seed layer. By changing the hydrothermal growth parameters, including temperature, reagent concentration and pH value of the solution, we readily controlled the dimensions, density and morphology of the ZnO nanostructures. More importantly, single-crystalline ZnO nanostructures grew directly on graphene, as determined by transmission electron microscopy. In addition, from the photoluminescence and cathodoluminescence spectra, strong near-band-edge emission was observed without any deep-level emission, indicating that the ZnO nanostructures grown on few-layer graphene were of high optical quality.

Reservoir computing (RC), a low-power computational framework derived from recurrent neural networks, is suitable for temporal/sequential data processing. Here, we report the development of RC devices utilizing Ag–Ag2S core–shell nanoparticles (NPs), synthesized by a simple wet chemical protocol, as the reservoir layer. We examined the NP-based reservoir layer for the required properties of RC hardware, such as echo state property, and then performed the benchmark tasks. Our study on NP-based reservoirs highlighted the importance of the dynamics between the NPs as indicated by the rich high dimensionality due to the echo state property. These dynamics affected the accuracy (up to 99%) of the target waveforms that were generated with a low number of readout channels. Our study demonstrates the great potential of Ag–Ag2S NPs for the development of next-generation RC hardware.

Amorphous TaOx thin films were deposited at different temperatures, and the resistance switching properties of the Pt/TaOx/Pt structure were investigated. X‐ray photoelectron spectroscopy showed that the amount of Ta2O5 in the film decreased and the content of Ta suboxides increased substantially when the growth temperature was increased. Unipolar resistance switching near the anode was stable only for TaOx film grown at room temperature. The experimental results revealed the critical effect of the film composition on the resistance switching behavior of TaOx films. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

The neuromorphic learning switching behavior was investigated in electric devices that were constructed based on the aggregation of thiol-stabilized Ag/Ag2S core–shell nanoparticles (NPs). The NPs were synthesized using the two-phase modified Brust–Schiffrin procedure and exhibited Ag–S and Ag–S–R bonding states at the surfaces of Ag NPs. The memristive behavior of such a device at room temperature under ambient pressure, which can be used to emulate the functions of biological synapses with long and short memories, is achieved. The importance of the Ag2S and the thiol layer at the surface of Ag NPs for the demonstration of learning behavior, such as the potentiation and depression of synapses in the human brain is explained.

The advanced progress of electronic-based devices for artificial neural networks and recent trends in neuromorphic engineering are discussed in this review. Recent studies indicate that the memristor and transistor are two types of devices that can be implemented as neuromorphic devices. The electrical switching characteristics and physical mechanism of neuromorphic devices based on metal oxide, metal sulfide, silicon, and carbon materials are broadly covered in this review. Moreover, the switching performance comparison of several materials mentioned above are well highlighted, which would be useful for the further development of memristive devices. Recent progress in synaptic devices and the application of a switching device in the learning process is also discussed in this paper.

We succeeded in bridging unzipped graphene nanoribbons (GNRs) and separating them from unwanted single-walled carbon nanotubes (SWNTs) using a frequency-dependent dielectrophoresis (DEP) method by varying the frequency and applied voltage used for future assembly. Atomic force micrographs and Raman spectra proved that unzipped GNRs were successfully bridged by the DEP method at frequencies higher than 13 MHz. The theoretical calculation also supported the finding that only GNRs were collected from a mixture of SWNTs/GNRs suspensions.

No abstract

No abstract