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.

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.

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