4.6 Article

Micro-to-Nano Bimodal Single-Particle Sensing Using Optical Tweezers

期刊

JOURNAL OF PHYSICAL CHEMISTRY C
卷 126, 期 26, 页码 10713-10721

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcc.2c00593

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资金

  1. JSPS KAKENHI [JP18H05242, JP18H01372]
  2. Japan Science and Technology Agency (JST) CREST [JPMJCR1903]

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This study proposes a novel method to enhance the signal-to-noise ratio of electrical signals of single particles using optical tweezers and a microchannel. By trapping a microparticle in a micro-orifice, the electrical signal from a target particle passing by can be sharpened and separated from background noise. By switching the micro-orifice between opening and closing with near-infrared light, target particles can be transported to the sensing section effectively.
Recently, electrical sensing techniques for single objects, such as nanoparticles, biomolecules, and viruses, have attracted a great deal of attention. To achieve both high throughput and high measurement accuracy, target objects need to be quickly transported to a small sensing section embedded in a fluidic channel. In the present study, we propose a novel method to improve the signal-to-noise (S/N) ratio of electrical signals of single particles, using optical tweezers and a microchannel. Optically trapping a 2 mu m microparticle in a micro-orifice that has a comparable dimension of 3.0 mu m (W), 2.5 mu m (H), and 3.0 mu m (L), the electrical signal from a small target particle that passes by the microparticle is sharpened and separated from the background noise. By irradiation with near-infrared light, the micro-orifice can be switched between opening and closing by optical tweezers, which works effectively to bring target particles to the sensing section using liquid flows and electrophoretic transport. As a result, the S/N ratio of electrical sensing of the smaller particle is improved by a factor of 5. The present microfluidic chip enables us to electrically detect particles of several hundreds of nanometers. Based on the present method, identification of single nanoparticles will also be feasible by using machine learning in the near future.

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