Dielectric characterization of bioparticles via electrokinetics: The past, present, and the future

Autor: Soumya K. Srivastava, Ezekiel O. Adekanmbi
Rok vydání: 2019
Předmět:
Zdroj: Applied Physics Reviews. 6:041313
ISSN: 1931-9401
Popis: Electrical properties of biological cells are useful to distinguish cells, either in their homogenous or heterogenous populations. They provide insight into the health, geometry, growth, differentiation, function, and physiological state, including death of any biological cell, i.e., phenotype and genotype of a cell. These properties play an important role in designing various microfluidic chip-based diagnostic tools that utilize electric field gradients for cell movement. Reported studies over several decades have revealed that electrorotation, dielectric spectroscopy, and dielectrophoresis are the most common cell characterization techniques to obtain electrical parameters. However, in each of these characterization techniques, several advancements have been reported especially within the last decade. Details of these advances vary from sophisticated methods like grinding electrode materials and mixing them with polymer composites for use as electrorotation electrodes to simple targeted means like using biological cells itself as electrodes. These advances in technologies are very well discussed in this review. Sequentially, a complete description of the characterized electrical properties targeted to specific bioparticles of interest is presented. The main concepts of dielectrophoresis, electrorotation, and impedance cytometry are given alongside the generated spectra including their analyses for both single and multiple cells. Also, various methods of electrode design, spacing, and fabrication are adequately discussed. The materials used for fabricating the electrodes and their advancement over time with respect to the choice of the materials are also substantially addressed. Finally, with the growing trend observed within this time frame, the future direction of bioparticle characterization could be predicted.
Databáze: OpenAIRE