Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling

Ahmed A. Agiza (), Kady Oakley, Jacob K. Rosenstein, Brenda M. Rubenstein, Eunsuk Kim, Marc Riedel and Sherief Reda
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Ahmed A. Agiza: Brown University
Kady Oakley: Brown University
Jacob K. Rosenstein: Brown University
Brenda M. Rubenstein: Brown University
Eunsuk Kim: Brown University
Marc Riedel: University of Minnesota
Sherief Reda: Brown University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Acid-base reactions are ubiquitous, easy to prepare, and execute without sophisticated equipment. Acids and bases are also inherently complementary and naturally map to a universal representation of “0” and “1.” Here, we propose how to leverage acids, bases, and their reactions to encode binary information and perform information processing based upon the majority and negation operations. These operations form a functionally complete set that we use to implement more complex computations such as digital circuits and neural networks. We present the building blocks needed to build complete digital circuits using acids and bases for dual-rail encoding data values as complementary pairs, including a set of primitive logic functions that are widely applicable to molecular computation. We demonstrate how to implement neural network classifiers and some classes of digital circuits with acid-base reactions orchestrated by a robotic fluid handling device. We validate the neural network experimentally on a number of images with different formats, resulting in a perfect match to the in-silico classifier. Additionally, the simulation of our acid-base classifier matches the results of the in-silico classifier with approximately 99% similarity.

Date: 2023
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DOI: 10.1038/s41467-023-36206-8

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