This paper proposes a novel approach to digital circuits and neural networks using acid-base chemistry orchestrated by a robotic fluid handling device. The authors leverage the complementarity of acids and bases to encode binary information and perform information processing based on majority and negation operations. Building blocks for digital circuits are presented using dual-rail encoding, and the implementation of neural network classifiers is demonstrated experimentally, achieving a near-perfect match with in silico results.
Publisher
Nature Communications
Published On
Jan 30, 2023
Authors
Ahmed A. Agiza, Kady Oakley, Jacob K. Rosenstein, Brenda M. Rubenstein, Eunsuk Kim, Marc Riedel, Sherief Reda
Tags
digital circuits
neural networks
acid-base chemistry
information processing
robotic fluid handling
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