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Updated: Mar 17, 2026

Scalable Syntheses of Graphene Oxide and Reduced Graphene Oxide using Cascade Design Oxidation and Highly Basic Reduction Reactions
Published on: July 3, 2025
Investigation of Measurement Cycle Reproducibility and Dynamic Resistance-Switching Capability in Reduced Graphene
Ricardo Barreto da Silva1, Artur Harres de Oliveira1, Josué Neroti Rigue1
1Departamento de Física, UFSM, Santa Maria 97105-900, Rio Grande do Sul, Brazil.
None:
Reduced graphene oxide (rGO) has been employed as a variable resistor in the development of RF and microwave devices, enabling electronic tuning of these systems. This paper investigates the experimental behavior of four different graphene samples in a microstrip with a gap attenuator structure. Beyond conventional DC resistance analysis, we assess the reproducibility of resistance across multiple measurement cycles and examine the dynamic resistance-switching capability and memory effect under current stepping. All samples exhibited resistance variation under the applied DC bias. However, this variation was not reproduced in subsequent measurement cycles, resulting in a reduced dynamic variation of the graphene resistance. Notably, the resistance variation decreased significantly from the first to subsequent cycles. Current step measurements revealed that rGO exhibits a memory effect, leading to resistances lower than the previous ones for a current of 1 mA, after being submitted to currents higher than 10 mA. Radio frequency measurements demonstrated that transmittance can be tuned via DC bias, with tunability strongly dependent on individual sample characteristics. Such behavior compromises the graphene's dynamic switching capability. Most existing studies highlight the tunability of graphene-based RF devices; our findings reveal, for the first time, that reduced graphene oxide exhibits cycle-to-cycle variability and memory effects, significantly restricting its use in dynamic applications. Therefore, fully harnessing the potential of graphene in applications requiring dynamic device tuning demands further advancements in material synthesis, processing, and device integration. Addressing challenges such as the observed memory effect is essential for the development of reliable and efficient graphene-based components.
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