Unlocking Nanoprecipitation: A Pathway to High Reversibility in Nanofluidic Memristors

Solid-state nanochannels have emerged as a promising platform for the development of ionic circuit components with analog properties to their traditional electronic counterparts. In the last years, nanofluidic devices with memristive properties have attracted special interest due to their applicability in, for example, the construction of brain-like computing systems. In this work, an asymmetric track-etched nanofluidic channel with memory-enhanced ion transport is reported. The results illustrate that the formation of nanoprecipitates on the channel walls induces memory effects in ion transport, leading to characteristic hysteresis loops in the current-voltage curves, a hallmark of memristive behavior. Notably, these memristive properties are achievable with a straightforward experimental setup that combines an aqueous solvent and a relatively low-soluble inorganic salt. The various conductance states can be rapidly and reversibly tuned over prolonged time scales. Furthermore, under appropriate measurement conditions, the nanofluidic device can alternate between different iontronic regimes and states, encompassing ion current rectification, ON-OFF states, and memristor-like behavior. These findings provide insights into the design and optimization of nanofluidic devices for bioinspired ionic circuit components.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2024-10-18
DOI
https://doi.org/10.1021/acsami.4c11522
Citations
15
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.13

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article

Unlocking Nanoprecipitation: A Pathway to High Reversibility in Nanofluidic Memristors

María Eugenia Toimil‐Molares, Waldemar A. Marmisollé, Omar Azzaroni, Gregorio Laucirica
15 citations
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
3.13
article

Unlocking Nanoprecipitation: A Pathway to High Reversibility in Nanofluidic Memristors

María Eugenia Toimil‐Molares, Waldemar A. Marmisollé, Omar Azzaroni, Gregorio Laucirica
article en
15 citations

Abstract

Solid-state nanochannels have emerged as a promising platform for the development of ionic circuit components with analog properties to their traditional electronic counterparts. In the last years, nanofluidic devices with memristive properties have attracted special interest due to their applicability in, for example, the construction of brain-like computing systems. In this work, an asymmetric track-etched nanofluidic channel with memory-enhanced ion transport is reported. The results illustrate that the formation of nanoprecipitates on the channel walls induces memory effects in ion transport, leading to characteristic hysteresis loops in the current-voltage curves, a hallmark of memristive behavior. Notably, these memristive properties are achievable with a straightforward experimental setup that combines an aqueous solvent and a relatively low-soluble inorganic salt. The various conductance states can be rapidly and reversibly tuned over prolonged time scales. Furthermore, under appropriate measurement conditions, the nanofluidic device can alternate between different iontronic regimes and states, encompassing ion current rectification, ON-OFF states, and memristor-like behavior. These findings provide insights into the design and optimization of nanofluidic devices for bioinspired ionic circuit components.

ACS Applied Materials & InterfacesVol. 16(43)
GSI Helmholtz Centre for Heavy Ion Research (DE), Technische Universität Darmstadt (DE), Division of Materials Research (US), Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (AR), Centre de Géosciences (FR), Universidad Católica de Murcia (ES), Universidad Nacional de La Plata (AR)
Alexander von Humboldt-Stiftung, Deutsche Forschungsgemeinschaft, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de La Plata, Agencia Nacional de Promoción Científica y Tecnológica
Openalex Percentile: Top 8%
Advanced Memory and Neural Computing
3.13
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