Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics
Abstract Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-02-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-025-00882-x |
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| author | Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad |
| author_facet | Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad |
| author_sort | Muhammad Umair Khan |
| collection | DOAJ |
| description | Abstract Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG’s performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na+). The confined ion interactions in this system induce hysteresis in ion transport across various frequencies, resulting in significant ion memory effects. Our IFM successfully replicates diverse electric pulse patterns, making it highly suitable for neuromorphic computing. Furthermore, our system demonstrates synapse-like learning functions, storing and retrieving short-term (STM) and long-term memory (LTM). The fluidic memristor exhibits dynamic synapse-like features, making it a promising candidate for the hardware implementation of neural networks. FF TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials, further enhanced by intricate chemical designs for self-powered electronics. |
| format | Article |
| id | doaj-art-6c8adb59d09f43dcbee451062d1dfa4a |
| institution | DOAJ |
| issn | 2055-7434 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Microsystems & Nanoengineering |
| spelling | doaj-art-6c8adb59d09f43dcbee451062d1dfa4a2025-08-20T03:00:39ZengNature Publishing GroupMicrosystems & Nanoengineering2055-74342025-02-0111111210.1038/s41378-025-00882-xBrain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronicsMuhammad Umair Khan0Bilal Hassan1Anas Alazzam2Shimaa Eissa3Baker Mohammad4Center for Cyber-Physical Systems - System on Chip Lab, Khalifa UniversityDepartment of Electrical Engineering, Khalifa UniversityCenter for Cyber-Physical Systems - System on Chip Lab, Khalifa UniversityDepartment of Chemistry, Khalifa UniversityCenter for Cyber-Physical Systems - System on Chip Lab, Khalifa UniversityAbstract Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG’s performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na+). The confined ion interactions in this system induce hysteresis in ion transport across various frequencies, resulting in significant ion memory effects. Our IFM successfully replicates diverse electric pulse patterns, making it highly suitable for neuromorphic computing. Furthermore, our system demonstrates synapse-like learning functions, storing and retrieving short-term (STM) and long-term memory (LTM). The fluidic memristor exhibits dynamic synapse-like features, making it a promising candidate for the hardware implementation of neural networks. FF TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials, further enhanced by intricate chemical designs for self-powered electronics.https://doi.org/10.1038/s41378-025-00882-x |
| spellingShingle | Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics Microsystems & Nanoengineering |
| title | Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics |
| title_full | Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics |
| title_fullStr | Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics |
| title_full_unstemmed | Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics |
| title_short | Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics |
| title_sort | brain inspired iontronic fluidic memristive and memcapacitive device for self powered electronics |
| url | https://doi.org/10.1038/s41378-025-00882-x |
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