4.5 Article

Nanofluidic optical diffraction interferometry for detection and classification of individual nanoparticles in a nanochannel

Journal

MICROFLUIDICS AND NANOFLUIDICS
Volume 26, Issue 8, Pages -

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s10404-022-02562-y

Keywords

Nanofluidics; Nanochannel; Diffraction; Light scattering; Interferometry

Funding

  1. JSPS [19H00850]
  2. Nanotechnology Platform Japan of the Ministry of Education, Culture, Sports, Science and Technology (MEXT)

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Detection and classification of nanoparticles at a single particle resolution have become crucial in the fields of materials chemistry, life science, and nanotechnology. This study presents the development of nanofluidic optical diffraction interferometry (NODI) for sensitive detection and classification of nanoparticles. The method utilizes optical diffraction by a single nanochannel as a reference light for interferometric light scattering detection, allowing for accurate measurement and discrimination of different nanoparticles.
Detection and classification of nanoparticles in flow at a single particle resolution are becoming extremely important with the progress in materials chemistry, life science, and nanotechnology. Interferometric light scattering has been widely used as a highly sensitive nanoparticle detection technique. However, its application to flow cytometric analyses is challenging, because precise fluidic control and complicated optical system are inevitable for accurate measurement of individual nanoparticles in flow. Here, we report the development of nanofluidic optical diffraction interferometry (NODI) which utilizes optical diffraction by a single nanochannel as a reference light for interferometric light scattering detection of nanoparticles in the nanochannel. Our method detected individual gold nanoparticles as small as 20 nm in diameter in flow by a simple optical system. Furthermore, by introducing a dual-wavelength measurement system, four types of gold and silver nanoparticles with 40-60 nm in diameter were discriminated based on their interferometric signals. Using a support vector machine (SVM) algorithm, classification of individual nanoparticles was achieved with over 70% accuracy. Our simple yet sensitive detection method will be widely used for the quantification and characterization of various nanoparticles.

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