Emerging Data Processing Methods for Single-Entity Electrochemistry

doi:10.1002/anie.202316551

Emerging Data Processing Methods for Single-Entity Electrochemistry

2024-03-15

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DOI：10.1002/anie.202316551

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Single-entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single-entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five-year advances in signal processing techniques for single-entity electrochemistry and highlight their importance in obtaining high-quality data and extracting effective features from electrochemical signals, which are generally applicable in single-entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single-molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single-entity electrochemical measurements would revolutionize the field of single-entity analysis, leading to new fundamental discoveries. This Minireview summarizes the latest advances in data processing techniques for single-entity electrochemistry towards achieving automation, real-time monitoring, increased sensitivity, as well as improved temporal and current resolution. image

Keywords:

signal processing

single-entity electrochemistry

nanopore sensing

single particle collision

time-frequency transform

Emerging Data Processing Methods for Single-Entity Electrochemistry

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