High-throughput real-time sensing in optomechanical systems with opto-mechano-fluidics

Suh, Jeewon

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https://hdl.handle.net/2142/113231 Copy

Description

Title

High-throughput real-time sensing in optomechanical systems with opto-mechano-fluidics

Author(s)

Suh, Jeewon

Issue Date

2021-05-21

Director of Research (if dissertation) or Advisor (if thesis)

Bahl, Gaurave

Doctoral Committee Chair(s)

Bahl, Gaurave

Committee Member(s)

• Cunningham, Brian T
• Nam, SungWoo
• Ewoldt, Randy H

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Date of Ingest

2022-01-12T22:51:31Z

Keyword(s)

Optomechanics, Opto-mechano-fluidics, particle sensor

Abstract

The increased use of biosensing technologies has saved countless patients and is continuously improving our quality of life. The use of biosensors has grown dramatically in the late 20th century to support medical diagnostic needs. In this context, technological advances in optical and mechanical devices have an impact on sensing applications. However, fundamental limitations on each optical and mechanical biosensor set a limit to their performances. Optical sensors cannot identify the mechanical properties of target biological particles related to their biological function. On the other hand, mechanical sensors do not exhibit high-throughput capability. Therefore, the integration of optics and mechanics continues to gain interest as an approach for addressing the sensors' fundamental challenges. Optomechanical biosensors demonstrate the potential to overcome the limitations of optical and mechanical biosensors while preserving their advantages. High-Q optical resonators allow label-free detection of individual nanoparticles through perturbation of optical signatures. However, these applications commonly encounter limitations that remain unresolved in practice. The detection techniques of optical resonators are especially limited due to reliance on random diffusion to deliver particles to the sensing region. Also, binding onto the resonator is necessary, causing the need for device cleaning for continuous detection. This implies a severe reduction in detection rate and makes the detection method impractical for sensing a large micro/nanoparticle population. More importantly, the optical interface cannot interact with the mechanical properties of target particles. However, recent studies in the link between mechanics and human diseases also motivate a need to understand the mechanical properties of bioparticles. Many microelectromechanical sensors (MEMS) have been demonstrated to measure mechanical properties, such as mass, compressibility, and viscoelasticity. The measurement is based on a resonant sensing principle with binding particles on a vibrating structure. However, these methods still have a persisting challenge of the binding requirement, causing low throughput. Furthermore, the resonators ought to operate in a liquid environment, which supports bioparticles' life functions. Consequently, mechanical acoustic loss lowers the mechanical quality factor that determines the minimum detectable mass of the mechanical sensors. To overcome this fundamental challenge, we have recently developed microfluidic optomechanical resonators that allow the detection of free-flowing particles in liquid media with near-perfect detection efficiency, without requiring labeling, binding, or direct access to the optical mode. Rapid detection of single particles is achieved through a long-range optomechanical interaction. The modification of the resonator vibrational modes during particle transits influences the scattered light spectra from the resonator. We call this resonator an opto-mechano-fluidic resonator (OMFR). This optomechanical method also uniquely quantifies mechanical parameters of single nanoparticles, which are not accessible through traditional optical measurements. In this thesis, we first provide the detailed fabrication steps of OMFRs and optomechanical testing. While this particle detection method can detect fast-flowing particles with very high bandwidth, the measurement rate is limited by the capability and complexity of the measurement apparatus. Furthermore, this higher bandwidth causes more noise. Therefore signal-to-noise ratio of mechanical vibration signal is not high enough to overcome noise floor related to the minimum particle size detection. An intensive curve fitting process requires a clear particle detection signal, resulting in impractical real-time sensing. We apply a hybrid electro-opto-mechanical (EOM) transduction for substantially increasing the bandwidth of the OMFR sensor, enabling real-time operation. The demonstrated improvements are obtained through high bandwidth lock-in measurement of the optical modulation induced by actuating the vibrational mode electrostatically at a fixed frequency. The presented system demonstrates temporal resolution of better than 20 \us (50,000 events/s) with particle sensing resolution (i.e., the particle size noise floor) down to 490 $nm$, operating in the air without any stabilization or environmental control. Our technique significantly enhances the sensing capabilities of high-Q optical resonators into the mechanical domain and allows extremely high-throughput analysis of large nanoparticle populations. We also present an imaging technique of the acoustic pressure modes in the OMFRs with a single particle as a perturbative probe. Experimental extraction of OMFR mode shapes, especially the acoustic pressure field within the fluidic core, is essential to calibrate sensitivity for extracting the particle parameters. The spatially distributed acoustic pressure fields of multiple vibrational modes in the OMFR system are estimated by tracking the resonant frequency shifts from a perturbative particle transit. The simulation result of the solid-liquid hybrid structure of the OMFR system also agrees with the experimental demonstration of the imaging calibration method. This calibration method opens a way to measure the compressibility measurement of target particles as well. This calibration result implies the radial position dependence of the particle detection signal. For a reliable sensor in the OMFR system, it turns out that fixing the radial position of a particle is desirable. We apply a passive focusing technique stemming from the rheological properties of non-Newtonian fluids. The passive focusing (i.e., viscoelastic focusing) enables transverse migration on particles in non-Newtonian flow in the OMFR, resulting in the central axis focusing of the channel. The radial position dependence is decoupled in the OMFR sensor by this focusing technique. As a result, the experimental results with the EOM measurement (frequency perturbation) and microscopy (particle size estimation) show the capability of particle mixture classification. Additionally, the empirical sensitivity calibration offers a better compressibility measurement without any simulation method compared to the initial results.

Graduation Semester

2021-08

Type of Resource

Thesis

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Copyright 2021 Jeewon Suh

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