Leveraging autocatalytic reactions for chemical domain image classification

作者：Christopher E. Arcadia, Amanda Dombroski, Kady Oakley, Shui Ling Chen, Hokchhay Tann, Christopher Rose, Eunsuk Kim, Sherief Reda, Brenda M. Rubenstein, Jacob K. Rosenstein · 发表于：Chemical Science · 年份：2021 · DOI：10.1039/d0sc05860b · 被引用次数：12 · 研究领域：Photoreceptor and optogenetics research、Advanced biosensing and bioanalysis techniques、Innovative Microfluidic and Catalytic Techniques Innovation

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.