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7 March 2016 High-throughput time-stretch microscopy with morphological and chemical specificity
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Particle analysis is an effective method in analytical chemistry for sizing and counting microparticles such as emulsions, colloids, and biological cells. However, conventional methods for particle analysis, which fall into two extreme categories, have severe limitations. Sieving and Coulter counting are capable of analyzing particles with high throughput, but due to their lack of detailed information such as morphological and chemical characteristics, they can only provide statistical results with low specificity. On the other hand, CCD or CMOS image sensors can be used to analyze individual microparticles with high content, but due to their slow charge download, the frame rate (hence, the throughput) is significantly limited. Here by integrating a time-stretch optical microscope with a three-color fluorescent analyzer on top of an inertial-focusing microfluidic device, we demonstrate an optofluidic particle analyzer with a sub-micrometer spatial resolution down to 780 nm and a high throughput of 10,000 particles/s. In addition to its morphological specificity, the particle analyzer provides chemical specificity to identify chemical expressions of particles via fluorescence detection. Our results indicate that we can identify different species of microparticles with high specificity without sacrificing throughput. Our method holds promise for high-precision statistical particle analysis in chemical industry and pharmaceutics.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cheng Lei, Masashi Ugawa, Taisuke Nozawa, Takuro Ideguchi, Dino Di Carlo, Sadao Ota, Yasuyuki Ozeki, and Keisuke Goda "High-throughput time-stretch microscopy with morphological and chemical specificity", Proc. SPIE 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, 97200X (7 March 2016);

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