Nanopore Sensing for Single-Entity Analysis: Challenges, Enhancements, and Future with AI and Computational Methods

Abstract

The concept of single-entity electrochemistry (SEE) emerged as a transformative framework for studying individual particles, molecules, and cells, distinct from traditional ensemble approaches, following its formal introduction during the 2016 Faraday Discussion in York, UK. Within the repertoire of techniques available under SEE, nanopore sensing has distinguished itself as a highly precise and versatile method for single-entity analysis of individual nanoparticles, cells, viruses, and (bio)molecules. Its ability to probe and characterize individual entities at nanoscale resolution has made it a cornerstone technology in this field. This review examines the foundational principles of nanopore sensing, its inherent advantages, and the technical challenges associated with its implementation. Strategies to overcome these challenges, including the incorporation of hydrogel and polymer enhancements, are presented as effective solutions for improving the reliability and efficiency of nanopore-based experiments. An exploration of the two primary types of nanopores—solid-state nanopores and biological nanopores—follows, with a focus on their respective fabrication techniques, material properties, and functional advantages. Solid-state nanopores, known for their robustness and customizability, are compared with biological nanopores, which offer unparalleled molecular specificity due to their naturally evolved structures. The integration of computational tools is discussed as a critical component of nanopore sensing. Computational modeling and simulation methods are shown to play a pivotal role in interpreting complex nanopore data by providing insights into dynamic molecular interactions and enhancing experimental accuracy. These tools allow for the prediction and analysis of ion flux, molecular trajectories, and conformational changes, thereby broadening the scope and precision of nanopore-based studies. Applications of nanopore technology are highlighted, particularly in the detection and characterization of nanoparticles, biomolecules, and genetic material. Nanopore sensing has been successfully employed to study properties such as size, shape, and surface charge, as well as to identify specific biomolecules such as DNA and proteins. Its utility in both fundamental research and proof-of-concept studies demonstrates its adaptability across diverse fields, including material science, molecular biology, and chemical engineering. The review concludes by exploring the transformative potential of integrating artificial intelligence (AI) with nanopore technology. AI algorithms have shown remarkable promise in automating data acquisition and analysis, significantly enhancing the throughput and precision of nanopore experiments. Through machine learning and self-optimization techniques, AI streamlines the identification of patterns in complex datasets, enabling rapid and reliable single-entity characterization. This fusion of nanopore technology with AI not only reduces experimental burdens but also accelerates innovation in the field, paving the way for autonomous experimentation and high-throughput diagnostics.