Abstract
In recent years, rapid progress in the field of single-entity electrochemistry (SEE) has opened a novel exploratory area in the field of analytical and electrochemistry. SEE is a method of studying the behavior of particles at the single particle level, which yields important information on the diffusion coefficient, individual particle size, size distribution, catalytic activity, collision frequency, and internal substances of the particles. Various types of particles have been studied through SEE. Among them, this review focuses on the results of analyzing soft particles (cells, proteins, viruses, liposomes, enzymes, vesicles, emulsion droplets, micelles, carbonaceous nanomaterials, and others). We introduce the various electrochemical techniques used in SEE and discuss important concepts and equations that must be known to apply SEE. We also introduce the results of recent research on several important soft particles. Finally, the potential applications in areas such as sensors, materials, catalysts, energy, and biomedicine, as well as the trends of related works are described. In the future, research on soft particle analysis using SEE is expected to increase rapidly, and this review is expected to be a valuable reference for researchers in the field.
Original language | English |
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Article number | 111999 |
Journal | Biosensors and Bioelectronics |
Volume | 151 |
DOIs | |
State | Published - 1 Mar 2020 |
Bibliographical note
Funding Information:This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, and ICT ( NRF-2018R1C1B6008668 ).
Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, and ICT (NRF-2018R1C1B6008668).
Publisher Copyright:
© 2019 Elsevier B.V.
Keywords
- Bio-entities
- Nano-impact detection
- Single nanoparticle electrochemistry
- Single-entity electrochemistry
- Soft nanoparticles
- Stochastic electrochemical collision detection