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n-Type Accumulation-Mode Organic Electrochemical Transistors as General Building Blocks for Low-Power Organic Artificial Neurons Enabling Multimodal Sensing Integration

  • Qiang He
  • , Zhongliang Zhou
  • , Christina J. Kousseff
  • , Iain McCulloch
  • , Qiang He
  • , Teck Lip Dexter Tam
  • , Hongkang Fu
  • , Nripan Mathews
  • , Wei Lin Leong

Research output: Contribution to journal › Article › peer-review

Abstract

The human integumentary system encodes external stimuli into temporally and spatially patterned spike signals for transmission to the central nervous system. Inspired by this biological process, we present a symmetric cross-coupled organic neuron based on accumulative n-type organic electrochemical transistors (OECTs). By utilizing only two identical n-type accumulation-mode OECTs, our system reproduces ten distinct biologically relevant spiking behaviors and operates robustly across diverse semiconductors and electrolytes, establishing a material-general framework for OECT-based neurons. Its spiking dynamics can be modulated by ionic, optical, and mechanical stimuli. Coupling the low-power neuron with a photodetector further enables light-driven, energy-autonomous operation. The system also enables multimodal sensory integration and selective signal decoupling via frequency-division multiplexing, offering a potential route toward complex sensory processing. Separately, the system achieves functional biological integration by interfacing with Venus flytraps to control leaf closure via artificial spikes. These results establish our materials-versatile strategy as a versatile framework for constructing organic artificial neuromorphic systems, providing a new pathway toward next-generation material-independent neuromorphic architectures, multimodal sensory integration, and bio-interfacing technologies.

Original languageEnglish (US)
Article numbere76368
JournalAdvanced Functional Materials
Volume36
Issue number53
DOIs
StatePublished - Jul 2 2026
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Materials Science
  • Condensed Matter Physics

Keywords

  • artificial neuromorphic systems
  • organic electrochemical transistors
  • spiking neurons

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