Sensing the Performance of Artificially Intelligent Nanopores Developed by Integrating Solid-State Nanopores with Machine Learning Methods

Solid-state nanopores with a through-hole diameter of less than a few hundred nanometers can detect single nanoparticles owing to the ionic current flowing through the nanopore. Improvements in our understanding of the electrical properties of nanopores and the flow dynamics of a single nanoparticle passing through a nanopore have facilitated the use of machine learning methods for single nanoparticle detection. However, the sensing performance of solid-state nanopores integrated with machine learning methods has not been investigated to date. In this work, we reveal the sensing performance of artificially intelligent nanopores (AINs) comprising solid-state nanopores combined with a machine learning approach. An AIN with a diameter of 295 nm provided single nanoparticle identification with an accuracy of >91% based on single ionic current–time waveforms of 2–7 types of polystyrene nanoparticles with nominal diameters ranging from 90 to 300 nm. A 98% sample identification accuracy was achieved for the samples involving nanoparticles with diameters of 200 and 220 nm. Additionally, AINs can be used to develop multiplex diagnostics for infectious diseases owing to the capability of AINs to identify several viruses.