Issue 15, 2021

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.

Graphical abstract: Leveraging autocatalytic reactions for chemical domain image classification

Supplementary files

Article information

Article type
Edge Article
Submitted
24 10 2020
Accepted
02 3 2021
First published
03 3 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2021,12, 5464-5472

Leveraging autocatalytic reactions for chemical domain image classification

C. E. Arcadia, A. Dombroski, K. Oakley, S. L. Chen, H. Tann, C. Rose, E. Kim, S. Reda, B. M. Rubenstein and J. K. Rosenstein, Chem. Sci., 2021, 12, 5464 DOI: 10.1039/D0SC05860B

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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