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A liquid metal-based module emulating the intelligent preying logic of flytrap.

Yuanyuan YangYajing Shen
Published in: Nature communications (2024)
Plant species like the Venus flytrap possess unique abilities to intelligently respond to various external stimuli, ensuring successful prey capture. Their nerve-devoided structure provides valuable insights for exploring natural intelligence and constructing intelligent systems solely from materials, but limited knowledge is currently available and the engineering realization of such concept remains a significant challenge. Drawing upon the flytrap's action potential resulting from ion diffusion, we propose a signal accumulation/attenuation model and a corresponding liquid metal-based logic module, which operates on the basis of the shape change of liquid metal within a sodium hydroxide buffer solution. The module itself exhibits memory and counting properties without involving any other electronic components, intelligently responding to various stimulus sequences, and reproducing the flytrap's most logical function. We also demonstrate and forecast its potential as a moving window integration-based high-pass filter, artificial synapse in neural networks, and other related applications. This research provides a fresh perspective on comprehending the intelligence inherent in nature and its realization through physical structures, which is expected to inspire logic device development in a broad engineering field.
Keyphrases
  • neural network
  • ionic liquid
  • healthcare
  • physical activity
  • working memory
  • high resolution
  • gold nanoparticles