Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.

The resistive switching (RS) mechanism in Ni-doped graphene oxide (GO) devices is studied. We found that RS depends strongly on the fabrication method of the GO sheet and on the electrode material. Resistive switching in GO-devices can be caused by the diffusion of ions from metallic electrode or by the migration of oxygen groups, depending on the fabrication process. We also show that GO-based structures possess activity-dependent modification capabilities, emphasized by the increase/decrease of device conductance after consecutive voltage sweeps of opposite polarity. Our results allow a better understanding of bipolar RS, towards future non-volatile memories and neuromorphic systems.

In the present study, we report a facile synthesis of novel Pd–NixB–SiO₂/RGO (Pd@NSG) nanocomposite and discuss its enhanced H₂spillover mechanism and high catalytic performance towards reduction of 4-nitrophenol (4-NP) to 4-aminopehol (4-AP).