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Leveraging autocatalytic reactions for chemical domain image classification.

ABSTRACT: Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide-alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of automated fluid transfers. The outputs of the operations are monitored using UV-vis spectroscopy. The growing interest in molecular information storage suggests that methods for computing in chemistry will become increasingly important for querying and manipulating molecular memory.

SUBMITTER: Arcadia CE

PROVIDER: S-EPMC8179570 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Leveraging autocatalytic reactions for chemical domain image classification.

Arcadia Christopher E CE Dombroski Amanda A Oakley Kady K Chen Shui Ling SL Tann Hokchhay H Rose Christopher C Kim Eunsuk E Reda Sherief S Rubenstein Brenda M BM Rosenstein Jacob K JK

Chemical science 20210303 15

Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide-alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of ...[more]

PMID: 34163768

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