Programmable responsive hydrogels inspired by classical conditioning algorithm

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Standard

Programmable responsive hydrogels inspired by classical conditioning algorithm. / Zhang, Hang; Zeng, Hao; Priimagi, Arri; Ikkala, Olli.

julkaisussa: Nature Communications, Vuosikerta 10, 3267, 22.07.2019, s. 1-8.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Harvard

APA

Vancouver

Author

Bibtex - Lataa

@article{addf51a6470d4a1ebb48bcde138ae635,
title = "Programmable responsive hydrogels inspired by classical conditioning algorithm",
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 nonbiological materials towards associative and dynamic properties.",
keywords = "GOLD NANOPARTICLES, SIZE, ABSORPTION, SYSTEMS, SHAPE",
author = "Hang Zhang and Hao Zeng and Arri Priimagi and Olli Ikkala",
note = "| openaire: EC/H2020/679646/EU//PHOTOTUNE Lehdess{\"a} mainitaan rahoituksessa my{\"o}s fysiikan H2020 DRIVEN-projekti, mutta eri grant-numerolla, jota ei l{\"o}ydy Cordiksesta.",
year = "2019",
month = "7",
day = "22",
doi = "10.1038/s41467-019-11260-3",
language = "English",
volume = "10",
pages = "1--8",
journal = "Nature Communications",
issn = "2041-1723",

}

RIS - Lataa

TY - JOUR

T1 - Programmable responsive hydrogels inspired by classical conditioning algorithm

AU - Zhang, Hang

AU - Zeng, Hao

AU - Priimagi, Arri

AU - Ikkala, Olli

N1 - | openaire: EC/H2020/679646/EU//PHOTOTUNE Lehdessä mainitaan rahoituksessa myös fysiikan H2020 DRIVEN-projekti, mutta eri grant-numerolla, jota ei löydy Cordiksesta.

PY - 2019/7/22

Y1 - 2019/7/22

N2 - 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 nonbiological materials towards associative and dynamic properties.

AB - 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 nonbiological materials towards associative and dynamic properties.

KW - GOLD NANOPARTICLES

KW - SIZE

KW - ABSORPTION

KW - SYSTEMS

KW - SHAPE

U2 - 10.1038/s41467-019-11260-3

DO - 10.1038/s41467-019-11260-3

M3 - Article

VL - 10

SP - 1

EP - 8

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 3267

ER -

ID: 36039454