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Interface-driven resistive switching and synaptic behavior in the graphene oxide-based memristive devices

  


Congratulations to our colleague PhD Student. Msc. Pham Phu Quan, for his recent publication entitled "Interface-driven resistive switching and synaptic behavior in the graphene oxide-based memristive devices" in the journal "Carbon" (Q1, IF = 11.9), which was a collaboration with our colleagues in the Surface Science Laboratory, Toyota Technological Instituate, Nagoya, Japan.

Graphene oxide (GO) has long been considered a versatile material for resistive switching, yet most reported devices exhibit only binary or filamentary behavior. Although GO is a flexible resistive-switching medium, the majority of memristors still primarily function in binary, filamentary modes after embedding GO in polymer matrices. In this study, we present a polymer-free, drop-cast GO device that isolates intrinsic metal/GO interfacial effects, enabling low-current, forming-free analog switching with robust synaptic function and state-dependent capacitance. The response can be adjusted from filamentary digital switching to self-rectifying analog behavior by modifying the top electrode (Cr, Al, Ag). Notably, all devices operate at low current without requiring a forming step, a key advance for enhancing endurance and scalability. Micro-Raman analysis further reveals thermal-driven reduction of GO under prolonged cycling, directly linking material changes to device degradation. Most strikingly, the Cr/GO/Al system exhibits rich neuromorphic dynamics, including short-term memory, long-term potentiation/depression, and pulse-width-dependent learning with non-monotonic relaxation, as well as precise multi-bit weight updates with a resolution of up to 9 bits. These results establish GO as a highly tunable, solution-processed platform that unifies memristive, memcapacitive, and synaptic functions. By bridging electrode/interface engineering with analog plasticity, this work highlights a pathway toward scalable, low-power, and multifunctional neuromorphic hardware.

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Acknowledgment 
This research was supported by the Vietnam National University - Ho Chi Minh (VNU-HCM), Ho Chi Minh City, Vietnam, under grant number DS2025-18-02.



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