6/6/2016 Rick Kubetz, Engineering Communications Office
Researchers from the University of Illinois at Urbana-Champaign have developed a new technique for extremely high speed photonic sensing of the mechanical properties of freely flowing particles using an opto-mechano-fluidic resonator (OMFR). This research potentially opens up completely new mechanical “axes of measurement” on micro/nanoparticles and bioparticles.
Written by Rick Kubetz, Engineering Communications Office
Researchers from the University of Illinois at Urbana-Champaign have developed a new technique for extremely high speed photonic sensing of the mechanical properties of freely flowing particles using an opto-mechano-fluidic resonator (OMFR). This research potentially opens up completely new mechanical “axes of measurement” on micro/nanoparticles and bioparticles.
High-speed optical detection methods, such as flow cytometry, are routinely used for analysis of large populations of particles through measurements of their optical properties, with analysis speeds approaching 50,000 particles/second. Optical sensors, however, cannot directly measure any mechanical properties of the particles (such as mass, density, compressibility, stiffness, etc). Until now, mechanical sensors have not approached the speed of optical flow cytometers, which makes routine measurements on large cell populations simply impractical.
Using bakers’ yeast and two types of microbeads, the researchers explored the particle-sensing capabilities of the OMFR.
“We have shown that our technique is sensitive to the density and compressibility of each individual particle as it passes by,” Bahl added. “The smallest detectable particle as reported in this work is around 660 nm.
“This work presents a new approach to perform resonantly enhanced optical sensing of freely flowing particles through the action of long-range phonons that extend between solid and fluid phases of the sensor and sample.”
The paper, “High-throughput sensing of freely flowing particles with optomechanofluidics,” is available online. The research was supported by National Science Foundation grants ECCS-1509391 and ECCS-1408539.
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