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3 results for au:Aguadero_A in:cond-mat
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Yunqing Tang, Francesco Chiabrera, Alex Morata, Andrea Cavallaro, Maciej O. Liedke, Hemesh Avireddy, Mar Maller, Maik Butterling, Andreas Wagner, Michel Stchakovsky, Federico Baiutti, Ainara Aguadero, Albert Tarancón Ion intercalation of perovskite oxides in liquid electrolytes is a very promising method for controlling their functional properties while storing charge, which opens the potential application in different energy and information technologies. Although the role of defect chemistry in the oxygen intercalation in a gaseous environment is well established, the mechanism of ion intercalation in liquid electrolytes at room temperature is poorly understood. In this study, the defect chemistry during ion intercalation of La0.5Sr0.5FeO3-\delta thin films in alkaline electrolytes is studied. Oxygen and proton intercalation into the LSF perovskite structure is observed at moderate electrochemical potentials (0.5 V to -0.4 V), giving rise to a change in the oxidation state of Fe (as a charge compensation mechanism). The variation of the concentration of holes as a function of the intercalation potential was characterized by in-situ ellipsometry and the concentration of electron holes was indirectly quantified for different electrochemical potentials. Finally, a dilute defect chemistry model that describes the variation of defect species during ionic intercalation was developed.
Memristive systems emerge as strong candidates for the implementation of Resistive Random Access Memories (RRAM) and neuromorphic computing devices, as they can mimic the electrical analog behavior or biological synapses. In addition, complementary functionalities such as memcapacitance could significantly improve the performance of bio-inspired devices in key issues such as energy consumption. However, the physics of mem-systems is not fully understood so far, hampering their large-scale implementation in devices. Perovskites that undergo topotactic transitions and redox reactions show improved performance as mem-systems, compared to standard perovskites. In this paper we analyze different strategies to optimize the multi-mem behavior (memristive and memcapacitive) of topotactic redox La$_{1/2}$Sr$_{1/2}$Mn$_{1/2}$Co$_{1/2}$O$_{3-x}$ (LSMCO) films grown on Nb:SrTiO$_3$ (NSTO). We explored devices with different crystallinity (from amorphous to epitaxial LSMCO), out-of-plane orientation ((001) and (110)) and stimulated either with voltage or current pulses. We found that an optimum memory response is found for epitaxial (110) LSMCO stimulated with current pulses. Under these conditions, the system efficiently exchanges oxygen with the environment minimizing, at the same time, self-heating effects that trigger nanostructural and chemical changes which could affect the device integrity and performance. Our work contributes to pave the way for the integration of LSMCO-based devices in cross-bar arrays, in order to exploit their memristive and memcapacitive properties for the development of neuromorphic or in-memory computing devices
The possibility to develop neuromorphic computing devices able to mimic the extraordinary data processing capabilities of biological systems spurs the research on memristive systems. Memristors with additional functionalities such as robust memcapacitance can outperform standard devices in key aspects such as power consumption or miniaturization possibilities. In this work, we demonstrate a large memcapacitive response of a perovskite memristive interface, using the topotactic redox ability of La$_{0.5}$Sr$_{0.5}$Mn$_{0.5}$Co$_{0.5}$O$_{3-\delta}$ (LSMCO, 0 $\leq$ $\delta$ $\leq$ 0.62). We demonstrate that the multi-mem behaviour originates at the switchable n-p diode formed at the Nb:SrTiO3/LSMCO interface. We found for our Nb:SrTiO$_{3}$/LSMCO/Pt devices a memcapacitive effect C$_{HIGH}$/C$_{LOW}$ ~ 100 at 150 kHz. The proof-of-concept interface reported here opens a promising venue to use topotactic redox materials for disruptive nanoelectronics, with straightforward applications in neuromorphic computing technology.