An optoelectrochemical synapse based on a single-component n-type mixed conductor

Organic mixed ionic-electronic conductors (OMIECs) are materials that can be used to build bio-inspired electronic devices as they emulate ion-based cellular communication through doping with aqueous ionic charges. The integration of charge photogeneration and electrochemical doping processes in the polymer film enables optoelectronic applications that involve synaptic transistors. However, no OMIEC has yet been implemented to create a miniaturized photoactive platform capable of perceiving and processing multi-spectral visual information. Here, we present a materials and device design concept in which an n-type OMIEC film is incorporated into the micron-scale channel of an electrochemical transistor operating directly in an aqueous electrolyte under ambient conditions. The conjugated polymer channel, consisting of a fluorinated bisistain-lactone-bithiazole acceptor, has a current modulated in response to both electrical and optical stimuli, emulating the multimodal function of the visual nervous system. Our optoelectrochemical synapse achieves multilevel conductance states as well as transduction of visual information covering ultraviolet, visible, and near-infrared regions of the spectrum - a range beyond that of the human visual system's perception. The resulting transistor active-matrix array is capable of adaptive sensing, memory, and pre-processing of visual information, demonstrating an efficient optoelectronic neuromorphic system with multi-task learning capability.

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Publication Details

Journal
Nature Communications
Published
2025-02-13
DOI
https://doi.org/10.1038/s41467-025-56814-w
Citations
40
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
29.22

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article

An optoelectrochemical synapse based on a single-component n-type mixed conductor

Tianrui Chang, Nazek El‐Atab, Yizhou Zhong, Valentina Elena Musteata et al.
40 citations
Nature Communications
Advanced Memory and Neural Computing
29.22
article

An optoelectrochemical synapse based on a single-component n-type mixed conductor

Tianrui Chang, Nazek El‐Atab, Yizhou Zhong, Valentina Elena Musteata, Haifeng Ling, Shofarul Wustoni, Sahika Inal, Hanrui Li, Wentao Shan, Johana Uribe, Wan Yue, Yazhou Wang
article en
40 citations

Abstract

Organic mixed ionic-electronic conductors (OMIECs) are materials that can be used to build bio-inspired electronic devices as they emulate ion-based cellular communication through doping with aqueous ionic charges. The integration of charge photogeneration and electrochemical doping processes in the polymer film enables optoelectronic applications that involve synaptic transistors. However, no OMIEC has yet been implemented to create a miniaturized photoactive platform capable of perceiving and processing multi-spectral visual information. Here, we present a materials and device design concept in which an n-type OMIEC film is incorporated into the micron-scale channel of an electrochemical transistor operating directly in an aqueous electrolyte under ambient conditions. The conjugated polymer channel, consisting of a fluorinated bisistain-lactone-bithiazole acceptor, has a current modulated in response to both electrical and optical stimuli, emulating the multimodal function of the visual nervous system. Our optoelectrochemical synapse achieves multilevel conductance states as well as transduction of visual information covering ultraviolet, visible, and near-infrared regions of the spectrum - a range beyond that of the human visual system's perception. The resulting transistor active-matrix array is capable of adaptive sensing, memory, and pre-processing of visual information, demonstrating an efficient optoelectronic neuromorphic system with multi-task learning capability.

Nature CommunicationsVol. 16(1)
Sun Yat-sen University (CN), Nanjing University of Posts and Telecommunications (CN), King Abdullah University of Science and Technology (SA)
University of Oxford, Sun Yat-sen University, King Abdullah University of Science and Technology, Southern University of Science and Technology
Openalex Percentile: Top 0%
Advanced Memory and Neural Computing
29.22
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