Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals using Unsupervised Machine Learning and Template Matching

Nano-impact single-entity electrochemistry (NIE) is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To extract meaningful information from NIE experiments, statistical analysis of large datasets is necessary. In this study, we developed a method for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications.