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Programmable responsive hydrogels inspired by classical conditioning algorithm

Author

Listed:
  • Hang Zhang

    (Aalto University)

  • Hao Zeng

    (Tampere University)

  • Arri Priimagi

    (Tampere University)

  • Olli Ikkala

    (Aalto University)

Abstract

Living systems have inspired research on non-biological dynamic materials and systems chemistry to mimic specific complex biological functions. Upon pursuing ever more complex life-inspired non-biological systems, mimicking even the most elementary aspects of learning is a grand challenge. We demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning, i.e., conditioning, which is among the simplest forms of learning. Algorithmically, associative learning minimally requires responsivity to two different stimuli and a memory element. Herein, nanoparticles form the memory element, where a photoacid-driven pH-change leads to their chain-like assembly with a modified spectral behaviour. On associating selected light irradiation with heating, the gel starts to melt upon the irradiation, originally a neutral stimulus. A logic diagram describes such an evolution of the material response. Coupled chemical reactions drive the system out-of-equilibrium, allowing forgetting and memory recovery. The findings encourage to search non-biological materials towards associative and dynamic properties.

Suggested Citation

  • Hang Zhang & Hao Zeng & Arri Priimagi & Olli Ikkala, 2019. "Programmable responsive hydrogels inspired by classical conditioning algorithm," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11260-3
    DOI: 10.1038/s41467-019-11260-3
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    Cited by:

    1. Shanming Hu & Yuhuang Fang & Chen Liang & Matti Turunen & Olli Ikkala & Hang Zhang, 2023. "Thermally trainable dual network hydrogels," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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