Ismail Abdulghani, Nam Gwang-Hyeon, Lokhandwala Aziz, Pandey Siddhi Vinayak, Saurav Kalluvadi Veetil, You Yi, Jyothilal Hiran, Goutham Solleti, Sajja Ravalika, Keerthi Ashok, Radha Boya
Department of Physics and Astronomy, School of Natural Sciences, The University of Manchester, Mancheste, UK.
National Graphene Institute, The University of Manchester, Manchester, UK.
Nat Commun. 2025 Jul 30;16(1):7008. doi: 10.1038/s41467-025-61649-6.
Nanofluidic memristors, obtained by confining aqueous salt electrolyte within nanoscale channels, offer low energy consumption and the ability to mimic biological learning. Theoretically, four different types of memristors are possible, differentiated by their hysteresis loop direction. Here, we show that by varying electrolyte composition, pH, applied voltage frequency, channel material and height, all four memristor types can emerge in nanofluidic systems. We observed two hitherto unidentified memristor types in 2D nanochannels and investigated their molecular origins. A minimal mathematical model incorporating ion-ion interactions, surface charge, and channel entrance depletion successfully reproduces the observed memristive behaviors. We further investigate the impact of temperature on ionic mobility and memristors characteristics. In this work, we show that the channels display both volatile and non-volatile memory, including short-term depression akin to synapses, with signal recovery over time. These results suggest that nanofluidic devices may enable new neuromorphic architectures for pattern recognition and adaptive information processing.
通过将盐水电解质限制在纳米级通道内获得的纳米流体忆阻器具有低能耗以及模拟生物学习的能力。理论上，可能存在四种不同类型的忆阻器，它们通过磁滞回线方向来区分。在此，我们表明，通过改变电解质成分、pH值、施加电压频率、通道材料和高度，纳米流体系统中可以出现所有四种忆阻器类型。我们在二维纳米通道中观察到两种迄今未被识别的忆阻器类型，并研究了它们的分子起源。一个包含离子-离子相互作用、表面电荷和通道入口耗尽的最小数学模型成功地再现了观察到的忆阻行为。我们进一步研究了温度对离子迁移率和忆阻器特性的影响。在这项工作中，我们表明这些通道既显示出挥发性记忆又显示出非挥发性记忆，包括类似于突触的短期抑制，信号会随时间恢复。这些结果表明，纳米流体装置可能为模式识别和自适应信息处理带来新的神经形态架构。
