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Permittivity-Based Microparticle Classification by the Integration of Impedance Cytometry and Microwave Resonators
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This study developed a microfluidic sensing platform combining impedance cytometry and microwave resonant sensing to classify microparticles based on their electrical permittivity. By independently measuring particle geometric and electric size, the system normalized microwave signals to isolate permittivity as a classification parameter, enabling differentiation of polystyrene and soda lime glass microparticles below 22 microns with over 94% accuracy. The technique has potential applications in microplastic identification and environmental monitoring.
Abstract Permittivity of microscopic particles can be used as a classification parameter for applications in materials and environmental sciences. However, directly measuring the permittivity of individual microparticles has proven to be challenging due to the convoluting effect of particle size on capacitive signals. To overcome this challenge, we built a sensing platform to independently obtain both the geometric and electric size of a particle, by combining impedance cytometry and microwave resonant sensing in a microfluidic chip. This way the microwave signal, which contains both permittivity and size effects, can be normalized by the size information provided by impedance cytometry to yield an intensive parameter that depends only on permittivity. The technique allowed us to differentiate between polystyrene and soda lime glass microparticles — below 22 microns in diameter— with more than 94% accuracy, despite their similar sizes and electrical characteristics. The technique offers a potential route for targeted applications such as environmental monitoring of microplastic pollution or quality control in pharmaceutical industry.
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