FROM CHIP-IN-A-LAB TO LAB-ON-A-CHIP: THE DEVELOPMENT …

The Pennsylvania State University

The Graduate School

College of Engineering

FROM CHIP-IN-A-LAB TO LAB-ON-A-CHIP: THE DEVELOPMENT OF A

PROTOTYPE FOR ACOUSTOFLUIDIC NANOPARTICLE SEPARATION

A Thesis in

Engineering Science and Mechanics

by

Joseph M. Rufo

© 2015 Joseph M. Rufo

Submitted in Partial Fulfillment

of the Requirements

for the degree of

Master of Science

May 2015

ii

The thesis of Joseph M. Rufo was reviewed and approved* by the following:

Tony J. Huang

Professor of Engineering Science and Mechanics

Thesis Advisor

Jian Xu

Associate Professor of Engineering Science and Mechanics

Judith A. Todd

Professor of Engineering Science and Mechanics

P.B. Breneman Department Head

*Signatures are on file in The Graduate School.

iii

ABSTRACT

Dynamic Light Scattering (DLS) is a commonly used analytical technique for measuring

the size distribution of particles in solution. DLS is an attractive technique because it is non-

invasive (a low power laser is used so as not to damage the samples), requires very little sample

preparation, and can extract information from small volume samples with relatively low particle

concentrations. For these reasons, DLS has become a widely used analytical technique in many

industries. For example, in the development of new biopharmaceuticals, one of the major

limitations is the large number of tests that must be performed on limited amounts of sample.

DLS allows researchers to extract size distribution information from as little as 2 μL of sample,

saving the rest of the sample for other required tests.

Although the size range of particles that can be analyzed via DLS is large (ranging from

.03 nm to 10 μm), careful attention must be paid to the concentration of particles larger than 500

nm. If the concentration of larger particles is too high, it can prevent accurate measurement of

the smaller sized particles. However, many applications produce samples containing particles

above and below 500 nm (i.e. proteins and protein aggregates). The goal of this thesis was to

develop an acoustic-based separation technique that could establish a tunable cutoff diameter and

remove all particles larger than the cutoff diameter. The concept of acoustic-based separation

was initially demonstrated in a laboratory setting with polystyrene beads. First, mixed samples

were analyzed by DLS. The samples were then passed through our acoustic filter, and the

smaller fraction was again analyzed by DLS and compared to both the initial measurements and

samples of known concentrations. Results showed that our acoustic filter drastically improved

the quality of the DLS measurements. Finally, we replaced the expensive lab equipment with

custom electronics and constructed a prototype for acoustic nanoparticle separation.

iv

TABLE OF CONTENTS

List of Figures .............................................................................................................. v

List of Equations .......................................................................................................... vi

CHAPTER 1: INTRODUCTION ............................................................................... 1

1.1: Dynamic Light Scattering Theory ................................................................ 1

1.2: Problem Statement ........................................................................................ 9

1.3: Objective ....................................................................................................... 11

CHAPTER 2: LITERATURE REVIEW .................................................................... 13

2.1: Conventional Separation Techniques ........................................................... 13

2.2: Acoustic Particle Separation ......................................................................... 18

CHAPTER 3: METHODOLOGY .............................................................................. 24

3.1: Design Needs ................................................................................................ 24

3.2: Goals and Timeline ....................................................................................... 30

3.3: Experimental Setup ....................................................................................... 30

CHAPTER 4: RESULTS ............................................................................................ 33

4.1: Separation in the Lab .................................................................................... 33

4.2: Prototype Testing .......................................................................................... 42

CHAPTER 5: CONCLUSIONS ................................................................................. 47

5.1: Summary ....................................................................................................... 47

5.2: Future Work Suggestions ............................................................................. 47

Appendix A: Team ...................................................................................................... 49

Appendix B: IDT Fabrication ..................................................................................... 50

Appendix C: PDMS Fabrication ................................................................................. 51

Appendix D: PMMA Fabrication ............................................................................... 52

Appendix E: Supplementary Results .......................................................................... 53

References .................................................................................................................... 56

v

LIST OF FIGURES

Figure 1.1: Polar plot arising from Mie scattering ...................................................... 3

Figure 1.2: Schematic of a typical DLS experimental setup ...................................... 5

Figure 1.3: Fluctuations in optical signals for large vs. small particles ...................... 6

Figure 1.4: Intensity plots and autocorrelation functions ........................................... 8

Figure 2.1: Schematic of SEC separation ................................................................... 13

Figure 2.2: Calibration curves for SEC....................................................................... 14

Figure 2.3: FFF underlying mechanism ...................................................................... 16

Figure 2.4: Normal vs. steric modes of FFF ............................................................... 17

Figure 2.5: Schematic of a typical SPLITT separation device ................................... 18

Figure 2.6: Acoustic tweezers for continuous particle separation .............................. 21

Figure 2.7: Tilted angle acoustic tweezers .................................................................. 22

Figure 3.1: Simulated and experimental microparticle trajectories ............................ 26

Figure 3.2: Simulated nanoparticle trajectories at different vibrational amplitudes ... 27

Figure 3.3: Simulated nanoparticle trajectories at different tilted angles ................... 28

Figure 3.4: Comparison of traditional and unidirectional IDT designs ...................... 29

Figure 3.5: Device immobilized on thermoelectric cooler ......................................... 31

Figure 4.1: Acoustic separation of 5 µm beads from 240 nm beads .......................... 33

Figure 4.2: Intensity plots obtained via DLS .............................................................. 34

Figure 4.3: Acoustic separation of 1.3 µm beads from 240 nm beads ....................... 35

Figure 4.4: Active region of separation device ........................................................... 36

Figure 4.5: Acoustic separation of 900 nm beads from 240 nm beads ....................... 37

Figure 4.6: Acoustic separation of 700 nm beads from 240 nm beads ....................... 38

Figure 4.7: Determining separation efficiency ........................................................... 39

Figure 4.8: Acoustic separation with PMMA chip ..................................................... 40

Figure 4.9: Intensity plots obtained via DLS for PMMA chip ................................... 41

Figure 4.10: Photos of prototype ................................................................................ 42

Figure 4.11: Internal layout of prototype .................................................................... 43

Figure 4.12: Intensity plots obtained via DLS for prototype ...................................... 44

Figure 4.13: USB camera images of particle separation............................................. 45

Figure 4.14: Comparison of experimental setups ....................................................... 46

vi

LIST OF EQUATIONS

Equation 1.1: Rayleigh scattering ............................................................................... 2

Equation 1.2: Fick’s second law of diffusion ............................................................. 4

Equation 1.3: Stokes-Einstein relation ........................................................................ 4

Equation 1.4: Second-order autocorrelation function ................................................. 7

Equation 1.5: Siegert equation .................................................................................... 7

Equation 1.6: First-order autocorrelation function ..................................................... 7

Equation 1.7: Decay rate ............................................................................................. 7

Equation 1.8: Scattering vector ................................................................................... 7

Equation 1.9: Polydispersity index ............................................................................. 9

Equation 1.10: Number averaged molecular weight................................................... 9

Equation 1.11: Weight averaged molecular weight .................................................... 9

Equation 2.1: Calibration curve for size exclusion chromatography .......................... 14

Equation 2.2: Acoustic radiation force ....................................................................... 20

Equation 2.3: Acoustic contrast factor ........................................................................ 20

Equation 2.4: Stokes drag force .................................................................................. 20

1

CHAPTER 1: INTRODUCTION

1.1: Dynamic Light Scattering Theory

Dynamic light scattering (DLS) is an analysis technique that allows the size distribution

of particles in solution to be determined by analyzing the manner in which the particles scatter

light. DLS is based on the combination of three fundamental theories from physics: Rayleigh

scattering, Mie scattering, and Brownian motion. Rayleigh scattering and Mie scattering both

describe how particles of different sizes scatter light, while Brownian motion describes how

particles of different sizes move in solution. In a typical DLS configuration, particles in solution

are illuminated with a monochromatic, coherent light source. The scattered light is then collected

and analyzed, and particle size information can be obtained. DLS is a label-free, reagent-free,

measurement technique that can be performed on very small sample volumes (as little as 2 μL of

sample) over a large range of particle sizes (ranging from .03 nm to 10 μm). In addition, the

measurement process is typically automated, and there are no difficult sample preparation

requirements (samples are simply loaded into a cuvette). Finally, DLS is capable of analyzing a

variety of solvents, enabling it to be employed in applications ranging from food

characterization1 to process validation in the manufacturing of biopharmaceuticals.

2 With the

aforementioned advantages, DLS has become a widely used analytical technique in both research

and industrial settings. This chapter will describe basic DLS theory and how particle size

information is extracted from scattered light signals.

In the late 19th

century, British physicist Lord Rayleigh published four seminal papers

that described the scattering of light by small particles.3-6

Rayleigh noted that when the diameter

(d) of the particle is much smaller than the wavelength (λ) of incoming light (d <

λ), the

incident photon can interact with the charges within the particle. Specifically, the incoming

2

photon interacts with the electron cloud to create an oscillating dipole; as the dipole shifts,

energy is radiated equally in all directions.3 This isotropic radiation of energy is commonly

referred to as Rayleigh scattering. The intensity, I, of the light with an incoming intensity, Io,

scattered by any one particle with a refractive index, n, when measured at an angle, θ, and

distance, R, from the particle is given as:

(

)

(

)

(

)

(1.1)

The strong dependence of the scattering intensity on the wavelength of incoming light ( )

indicates that shorter wavelengths will be scattered much more strongly than longer wavelengths.

This exponential dependence explains why the sky appears blue, even though incoming solar

radiation spans the entire visible range.6 As the light passes through the atmosphere, atmospheric

molecules scatter blue light much stronger than any other visible wavelength. In DLS

instruments, the intensity of the scattered signal is analyzed to reveal information about the

diameter of the particles in solution. For a DLS instrument using a red HeNe laser with a

wavelength of 633 nm, Rayleigh scattering can provide size information for particles smaller

than around 60 nm.7 For larger sized particles, the manner in which light is scattered is slightly

different. Rather than isotropic scattering, the scattering intensity becomes distorted in the

forward direction. This form of scattering is called Mie scattering, named after German physicist

Gustav Mie. Mie developed his theory in an attempt to describe why the color of gold colloids

changes as the size of the gold nanoparticles is increases.8 Mie showed that the light extinction,

or the energy lost to the combined effects of absorption and scattering, is highly dependent on

the wavelength of incoming light and the size of the particle. Smaller sized gold particles show

extinction maxima in the blue and green wavelengths, causing them to appear red when

illuminated with white light; as the particle size is increased, the extinction maxima shift towards

3

red wavelengths, and the particles begin to appear bluer in color. Mie theory was developed as a

by solving Maxwell’s equations. The solution involves an infinite series of partial wave

equations, and ultimately, it can be used to describe the complex scattering and absorption of

light by a spherical particle with respect to distance and angle.9 While the full derivation of Mie

scattering is beyond the scope of this thesis, Figure 1.1 shows a typical polar plot that is used to

determine the size of a particle based on how it scatters light. On the horizontal axis, a

logarithmic plot of the intensity is shown. The polar axes indicate the angle at which the light is

measured when the particle is being illuminated from 180°.

Figure 1.1: Polar plot of scattered light intensity for different sized particles.7

The polar plot shows that light is scattered much more strongly in the forward direction,

regardless of particle size. When comparing the light scattered by a 1000 nm particle (red) to that

of a 500 nm particle (green), the 1000 nm particle scatters light nearly 100 times stronger in the

4

forward direction; however, for certain angles (i.e. 30°, 60°, 90°), the scattered light intensity is

practically identical for both particles. This can be problematic in DLS instruments because it

can make determining particle sizes very problematic when measuring from certain angles. As a

result, most DLS instruments utilize dual angle measurements to increase sensitivity and more

easily detect the presence of particles of different sizes.7 Unlike in Rayleigh scattering, careful

attention must be paid to the presence of maxima and minima in the scattering intensity plots

when using Mie theory to determine particle sizes.

The final factor that must be taken into account when determining the size distribution of

particles in solution is that the particles are constantly moving. This constant, random motion,

termed Brownian motion, is due to the constant collisions between the particles and molecules in

the solution.10

Brownian motion causes the concentration of particles in any given area to

fluctuate over time. Equation 1.2, Fick’s second law of diffusion, describes how concentration

changes with time (in one-dimension).

(1.2)

Where Φ(x,t) represents the concentration of a given substance as a function of location, x, and

time, t, and D is the diffusion coefficient. D is typically measured in units of m2/s, while

concentration is generally measured in units of mol/m3. For spherical particles in solution, the

diffusion coefficient can be determined by the Stokes-Einstein relation, shown in Equation 1.3.

(1.3)

Where kB is Boltzmann’s constant, T is the absolute temperature, η is the viscosity of the fluid,

and r is the radius of the particle in the fluid. Because the diffusion constant is inversely

proportional to the radius of the particle, smaller particles will undergo more rapid Brownian

5

motion than larger particles in fluids with identical temperatures and viscosities. At higher

temperatures, particles undergo more rapid Brownian motion. Thus DLS measurement devices

must have some mechanism of reporting the temperature of the fluid. In addition, it is essential

that the temperature and viscosity of the fluid are kept constant throughout a sample

measurement.

While Rayleigh scattering, Mie scattering, and Brownian motion all relate to the size of

the particles in solution, it may not be immediately evident how DLS measurement tools

determine particle size distributions based on scattered light signals. In order to carry out these

measurements, sufficient optics and signal processing equipment are required. Figure 1.2 is a

schematic of a typical setup for DLS measurements.11

A laser is used to provide a highly

monochromatic, highly coherent light source. It is focused through a lens onto the sample.

Typically, a plastic or glass cuvette is used to hold the sample. At a fixed angle, θ, a lens and

photon detector collect the scattered light signals. This information is converted from analog to

digital and analyzed by autocorrelation software, which is able to determine particle size

distributions.

Figure 1.2: Schematic of a typical DLS experimental setup.11

6

As previously mentioned, the intensity of the incoming optical signals is related to the

size of the particles (through Rayleigh and Mie scattering). The fluctuations in the optical signals

over time are due to the movement of particles in the solution, which is also related to the size of

the particles (through the Stokes-Einstein relation). Figure 1.3 compares the optical signals

collected from large and small particle samples.12

Because smaller particles are moving more

rapidly in the solution than larger particles, the optical signals collected for smaller particles

fluctuate more than those of the larger particles.

Figure 1.3: Fluctuations in optical signals for large vs. small particles.12

An autocorrelation function is used to quantify how slowly or rapidly an optical signal is

fluctuating. An autocorrelation function compares the intensity, I, of an optical signal at an initial

time, t, with the intensity at a later time, t + τ, and determines the degree to which they are

related. The normalized, second-order autocorrelation function, g2(q;τ), is shown below:

7

( ) ⟨ ( ) ( )⟩

⟨ ( )⟩ (1.4)

Where q is the wave vector and the brackets, < >, represent the expected value operator.

Equation 1.4 is a referred to as a second-order equation because it involves intensity

measurements, which are the squares of electric fields. Over sufficiently short time periods, τ,

I(t+τ) and I(t) remain closely correlated, as the particles have not had sufficient time to move,

and the correlation coefficient is 1. However, as τ is increased, I(t+τ) and I(t) will no longer be

correlated due to the particle’s movement, and the autocorrelation coefficient diminishes to 0. By

determining how quickly the autocorrelation function decays, one can determine the size of the

particles in solution.13

The autocorrelation function will decay much faster for smaller particles

because they are moving more rapidly in solution. Because Equation 1.4 requires infinite time

limits of evaluation; an approximation, known as the Siegert equation, must be implemented.13

The Siegert equation is shown below:

( ) [ ( )] (1.5)

Where β is a correction factor that is based on the experimental setup and g1(q;τ) is the first-order

autocorrelation function. If treated as a monodiperse sample, the first-order autocorrelation

function follows the simple exponential decay relationship as follows:

( ) ( ) (1.6)

With:

(1.7)

And:

(1.8)

8

Where Γ is the decay rate, q is the scattering vector, n is the refractive index of the fluid, and λ is

the wavelength of the laser. The decay rate of the autocorrelation function can therefore be used

to obtain the diffusion coefficient, which can be used in conjunction with Equation 1.3 to solve

for the particle radius. The diffusion coefficient is obtained by fitting the autocorrelation function

with a suitable algorithm, mainly cumulants analysis or distribution analysis.13

Figure 1.4 shows

intensity plots and corresponding autocorrelation plots for a sample with large particles and a

sample with small particles.

Figure 1.4: Intensity plots and autocorrelation functions for large and small particle samples.7

As expected, the correlation function for the large particles takes much longer to decay

than the correlation coefficient for the smaller particles. Subsequent cumulant analysis would

reveal that the diffusion coefficient in the case of the large particles is smaller than the diffusion

coefficient for the small particles. This diffusion coefficient is then converted to mean particle

size via the Stokes-Einstein relation. Many companies have utilized DLS theory to develop

9

commercial devices and software that can be used to automatically acquire particle size

distribution information from solutions.

1.2 Problem Statement

There are several cases in which DLS may not be suitable for measuring samples. The

first is when the sample is highly polydisperse (i.e. there is a wide size distribution in the

sample). One of the assumptions made in DLS theory is that the first order autocorrelation

function obeys a simple exponential decay; however, this assumption only hold true for

monodiperse samples. Because most samples are polydisperse, the autocorrelation function

usually becomes a sum of the exponential decays for each species in the population.13

However,

once the polysdispersity crosses a certain threshold, the fitting algorithms will fail to extract

useful information about the size distribution of particles in the sample. The polydisperisty index

(PDI), a measurement that quantifies the heterogeneity of the sample, is given by the following

equation:

(1.9)

Where Mn represents the number average molecular weight and Mw represents the weight

average molecular weight. These two quantities can be obtained from:

∑

∑ (1.10)

And:

∑

∑ (1.11)

10

Where Ni is the number of molecules and Mi is the molecular mass. For samples containing latex

standard spheres, PDI values are typically < 0.05.7 Distribution algorithms work well until the

PDI reaches 0.7; above this value, samples may not be suited for DLS measurements.7

The second factor that may deter the accuracy DLS measurements is particle aggregation.

Particle aggregation will increase the PDI, but even low levels of aggregation can negatively

impact a DLS measurement. Rayleigh scattering and Mie scattering are based on assumptions

that the particles are sphere-like. When particles aggregate, they are no longer sphere-like and

will scatter light in a different manner. These particle aggregates will be treated as larger

particles by the DLS software, resulting in inaccurate measurements. As a result, careful

attention must be paid to the dispersant (liquid medium) to make sure that it will not promote

particle aggregation.

Careful attention must also be paid to the concentration of particles. If the concentration

of particles is too high, particles will begin to interact with one another. Thus the movement of

the particles is no longer dictated by Brownian motion and the Stokes-Einstein equation. As a

result, the DLS software will incorrectly calculate the sizes of particles in the solution. Another

issue that arises at high particle concentrations is multiple scattering. DLS theory assumes that

each photon collected by detectors has only been scattered by one particle. When the

concentration is too high, photons begin to be scattered multiple times before reaching the

detectors. This will also lead to inaccurate results, as the initial conditions for Rayleigh scattering

and Mie scattering are no longer valid.

The final issue that can affect the accuracy of DLS measurements is particle

sedimentation. All particles will sediment to some degree; however, for DLS measurements, it is

essential that the rate of sedimentation is much slower than the rate of diffusion. If particles

11

sediment to the bottom of the cuvette, they will no longer scatter light and will appear invisible

to the DLS equipment. If a sample containing sedimenting particles is measured multiple times

sequentially, the count rate obtained by the DLS software will decrease with each measurement.

Particle sedimentation becomes an issue with particles larger than about 500 nm. Above 1 µm,

the particles begin to sediment very rapidly, and careful attention must be made in preparing the

samples for DLS measurements. For example, if the sample has been sitting for some time, it is

essential that sample be vortexed immediately prior to insertion into the DLS machine.

1.3: Objective

To address the issues associated with the inability of DLS instruments to accurately

measure samples containing large particles, we proposed to develop a continuous-flow,

microfluidic, acoustic filtration system. The proposed system could establish an adjustable cutoff

diameter, and all particles larger than the cutoff diameter would be removed from the sample.

The samples would be analyzed pre and post filtration and compared to samples of known

concentrations to assess the ability of the acoustic filter to improve the accuracy of DLS

measurements. Not only did we want to conduct proof of concept demonstrations, but we also

sought to develop a commercially viable alpha prototype. Often times in the microfluidics

research community, researchers claim to develop “lab-on-a-chip” devices. That is, all of the

functions that are normally carried out with expensive laboratory equipment and highly trained

personnel are integrated onto an automated, microfluidic chip. While there have been many

successful examples of this process (i.e. microfluidic chips run by cell phones for the diagnosis

of syphilis14

and HIV15

), one of the major bottlenecks in microfluidics research is in the

transition from lab demonstrations to real-world commercial products. This is due to the fact that

many microfluidics laboratories utilize expensive, bulky equipment that cannot easily be

12

replaced, rendering the technology commercially unviable. This approach is commonly referred

to as the “chip-in-a-lab” approach.16

Even though the technological feats are being carried out on

an inexpensive, portable, microfluidic chip, all of the peripheral equipment prevents the chip’s

use in practical, point-of-care (POC) applications. In this thesis, a strong emphasis was placed on

commercial development, cost reduction, and reliability engineering to ensure that the

technology developed could one day find real-world use and potentially be developed for other

applications.

13

CHAPTER 2: LITERATURE REVIEW

2.1: Conventional Separation Techniques

One of the most commonly used techniques for separating nanoparticles is size exclusion

chromatography (SEC).17-19

SEC is a high resolution separation technique that uses porous beads

to separate particles. A separation column is first filled with beads that have well-defined pore

sizes. The particle containing solvent is then injected into the separation column. As the solvent

elutes through the column, smaller particles are able to enter the beads, while larger particles

flow past the beads. The smaller particles take longer to elute through the column, as they must

travel through a larger effective elution volume. Figure 2.1 shows a schematic of how separation

via SEC is achieved.

Figure 2.1: Schematic of SEC separation.18

14

In SEC, both the solvent and beads must be tailored for the sample of interest. It is

important that the solvent does not dissolve the sample or damage the packing beads. In addition,

calibration curves for each column must be obtained prior to conducting measurements.

Molecular weight markers with known molecular weights (typically polystyrene) are injected

through the column to see how molecular weight (M) varies with elution volume (Ve).19

Equation 2.1 shows the equation used fit the calibration curve for SEC.

( ) (2.1)

Where b and c are constants related to the experimental setup. Once the constants are obtained

using known molecular weight samples, the molecular weights of unknown particles can be

found. Figure 2.2 shows the calibration curves obtained from packing materials with different

sized pores. The material with the larger pores (green) has a much higher slope than the material

with the smaller pores (blue).

Figure 2.2: Calibration curves for SEC.19

Particles larger than the exclusion limit will elute too quickly through the packing

material and cannot be quantified by the SEC instrument. Particles smaller than the penetration

15

limit take too long to elute through the packing material and also cannot be quantified by the

SEC instrument. Figure 2.2 shows the importance of selecting the proper pore size for the

sample. Typically, beads with small pore sizes are used to separate smaller particles and beads

with large pore sizes are used to separate larger particles. When a sample that contains a broad

range of particle sizes is used, a combination of beads can be employed. By collecting different

volumes of eluted samples in different collection containers, the samples can be sorted by their

size. Common applications of SEC separation include the purification of biopharmaceuticals,20

separation of carbon nanotubes,21

and the removal of large contaminants from cell culture

fluids.22

Although a powerful separation technique for certain applications, the upper size limit for

SEC separation is around 60 nm.17

This eliminates SEC separation as a technology that can be

used to improve the resolution of DLS, as particles larger than 500 nm need to be separated. In

addition to the size limitation, optimizing the conditions for SEC (i.e. column dimensions, flow

rate, packing density, pore size, bead diameter, etc.) is a very tedious process that requires highly

trained personnel. The development of a separation technique that can easily be adjusted to

separate many different types of samples would fill an immediate need for many separation

applications.

Another commonly used separation technique is field flow fractionation (FFF).23

Unlike

SEC, which is limited to an upper size limit of around 60 nm, the upper size limit for FFF is

around 700 nm (although separation of particles as large as 10 µm has been demonstrated).24

The

underlying mechanism to FFF is similar to SEC in that particles of different sizes will migrate

through a narrow channel at different rates based on their sizes. However, FFF only requires a

16

single aqueous phase, whereas SEC requires two phases: the solvent and the porous beads.

Figure 2.3 shows a schematic of how FFF is achieved.

Figure 2.3: FFF underlying mechanism.25

In the simplest embodiment of FFF, a solution containing particles is injected through a

narrow channel with a separation field acting perpendicular to the channel. The separation field

can be generated by various mechanisms including: cross flow, gravitational, centrifugal, thermal

gradient, electrical, and magnetic.24

The perpendicular field pushes the particles to different

heights in the channel based on the size of the particles. Within the channel, there exists a

parabolic flow profile. Thus particles located closer to the center of the channel will travel much

faster than particles located near the channel wall. These particles will elute through the channel

at a rate characteristic to their size, enabling size based separation.

FFF is complicated because the operating mode changes for particles found in different

size classes.26

For example, for particles under 500 nm, the external force will push the particles

towards the channel bottom. Upon reaching the channel bottom, particles will undergo Brownian

motion and diffuse towards the middle of the channel. As discussed earlier, smaller particles will

diffuse faster, allowing them to reach a higher final height and elute through the channel faster.

17

Thus for particles under 500 nm, smaller particles elute through the channel faster than larger

particles. This operational mode is referred to as “normal” mode.26

However, for particles

between 500 nm and 10 µm, larger particles will elute through the channel faster than smaller

particles. Under these conditions, when the external field acts on the particles, they will also be

pushed towards the channel bottom. But at this size range, the Brownian motion of the particles

begins to become negligible. Rather than diffuse, the particles form a thin layer near the channel

bottom. The larger particles in the layer protrude into the channel slightly more than the smaller

particles, allowing them to reach a higher final height and elute through the channel faster. This

mode of operation is referred to as “steric” or “reversed” mode.26

Figure 2.4 shows the two

operating modes of FFF.

Figure 2.4: Normal vs. steric modes of FFF.26

In a slightly modified configuration, FFF can be used to continuously separate particles

of different sizes into different outlets (rather than collecting different volumes at different

times). This configuration greatly simplifies the separation process and is referred to as split flow

lateral transport thin (SPLITT) separation.26

In this configuration, two inlet fluids are required:

the sample and an exchange buffer. A splitter is placed downstream to divide the channel into

two outlets. The separation field pushes the particles to different heights within the channel, and

18

the particles can be directly separated based on their final vertical position in the channel. Figure

2.5 shows a typical configuration of a SPLITT separation device.

Figure 2.5: Schematic of a typical SPLITT separation device.26

Many types of external fields have been applied to achieve separation, including

gravitational,27

magnetic,28

and dielectrophoretic forces.29

However, for gravitational separation,

only particles larger than about 20 µm can be separated at timescales comparable to other FFF

techniques.26

For magnetic and dielectrophoretic forces, only certain fluids with particular

magnetic or electric properties can be used. Furthermore, the separation is influenced by factors

other than size, complicating their use in purely size-based separation applications. In this regard,

the development of a separation technique that can be employed across a wide range of particle

sizes and is independent of the magnetic and electrical properties of the particles and solution

would be highly valuable.

2.2: Acoustic Particle Separation

In 2009, in an effort to create a more broadly applicable separation technique, the Penn

State Acoustofluidics Laboratory developed a standing surface acoustic wave (SSAW)

separation technology.30

This SSAW-based technology was based on the group’s previous work

on a particle manipulation technology called “acoustic tweezers”.31

Acoustic tweezers was the

19

first technology to use SSAWs to manipulate and pattern cells and microparticles, but it has since

been applied to continuous flow applications.32-34

The term “surface waves” refers to the manner

in which the acoustic waves are generated. Surface acoustic waves are generated via the

piezoelectric effect, a phenomenon observed in certain crystals in which an applied electric field

is converted into a mechanical strain; likewise, in these crystals, an applied mechanical force is

converted into an electric charge. Surface waves have advantages over bulk waves in that they

can reach much higher frequencies and require less power to generate.35

In the fabrication of the

acoustic tweezers, interdigital transducers (IDTs) are evaporated onto a lithium niobate

(LiNbO3) piezoelectric substrate. A signal generator is used to apply a periodic voltage to the

IDTs. As a result, mechanical waves are generated on the surface of the LiNbO3 substrate.

Because the IDTs are placed on either side of the microfluidic channel, waves propagate in

opposite directions underneath the microfluidic channel. When waves propagate in opposite

directions, they constructively interfere to create a standing wave field. Figure 2.6 shows the

typical configuration of an acoustic tweezers device.

Figure 2.5: Schematic of a typical acoustic tweezers device.35

In this standing wave field, there is a periodic distribution of pressure nodes (minimum

pressure) and pressure antinodes (maximum pressure).36

This standing wave field is coupled into

20

the liquid in the microchannel, resulting in periodic pressure fluctuations in the liquid. Pressure

fluctuations in the liquid result in acoustic radiation forces that act on particles to position them

at the pressure nodes or pressure antinodes, depending on the properties of the particle. The

acoustic radiation force (Fr) can be expressed as:36

(

) ( ) ( ) (2.2)

( )

(2.3)

In Equations 2.2 and 2.3, p0, Vp, λ, k, x, ρp, ρf, βp, and βf are acoustic pressure, volume of the

particle, wavelength, wave vector, distance from a pressure node, density of the particle, density

of the fluid, compressibility of the particle, and compressibility of the fluid, respectively.

Equation 2.3 describes the acoustic contrast factor, φ, which determines whether the particle

moves to pressure nodes or pressure antinodes in the SSAW field: the particle will move towards

pressure nodes if φ is positive and pressure antinodes if φ is negative. As the particle moves in

the fluid, it is opposed by Stokes drag force (Fd), which is given by the following equation:

( ) (2.4)

Where η, Rp, up, and uf are the viscosity of the fluid, radius of the particle, velocity of the

particle, and velocity of the fluid, respectively. Other forces acting on the particle include gravity

and buoyant forces, but they are almost balanced as they are generally similar in magnitude but

opposite in direction. Figure 2.6 shows how the acoustic tweezers can be employed to

continuously separate particles of different sizes.30

Fig 2.6 (a) shows a schematic of the device.

Particles are introduced from outlets on either side of the microchannel. Fig 2.6 (b) shows the

magnitude and direction of the different forces acting on particles of different sizes in the

channel. As the particles enter the acoustic region, the acoustic force drives the larger particles to

21

the center of the channel. However, because the magnitude of the acoustic force is proportional

to volume of the particle, the smaller particles do not experience enough force to push them to

the center outlet, and they remain in the side outlets.

Figure 2.6: (a) Schematic of acoustic tweezers used for continuous particle separation. (b)

Comparison of forces acting on the particles at different locations within the acoustic field.30

The device in Figure 2.6 separated 13,000 PS beads of two different sizes (particle 1:

0.87 μm polystyrene (PS) beads, particle 2: 4.17 μm PS beads) using only 30 mW of power. The

low power consumption of the SSAW-based sorting renders it a highly biocompatible sorting

technique. In addition, a separation efficiency of 80% was achieved.

In the device shown in Figure 2.6, the maximum separation distance (the distance

between the particles being sorted) is limited to a quarter of the acoustic wavelength. This leads

to relatively low separation efficiencies (all of the target particles are not separated from the

undesired population) and low sensitivity (it is difficult to separate particles that are similar in

22

size, density, and compressibility). To overcome these limitations, in 2014, a new configuration

was developed in which the channel was placed at angle to the IDTs. This approach, termed the

tilted-angle standing surface acoustic wave (taSSAW) approach, allows particles to be pushed

across multiple pressure nodes, thereby achieving separation distances of up to 10 times the

acoustic wavelength.34

The taSSAW-based separation device was used to successfully separate

MCF-7 cancer cells (~20 µm in diameter) from normal leukocytes (white blood cells, ~12 µm in

diameter). Figure 2.7 (a) shows a photo of the taSSAW-based separation device, while Figures

2.7 (b) and (c) show the device being used to separate 10 µm PS beads from 2 µm PS beads.34

Figure 2.7: (a) Photographic image of the ta-SSAW separation device. (b) The active region of

the sorting device and (c) the outlet, showing 10 µm beads being collected from the top outlet,

while the 2 µm beads remain in the bottom outlet.34

The taSSAW device achieved a separation efficiency of 99% for the separation of 10 µm

beads from 2 µm beads. In addition, the device separated 9.9 µm beads from 7.3 µm beads with

23

97% efficiency. The device was also able to separate 15 µm PS beads from HL-60 cells (a

human promyelocytic leukemia cell line with a diameter ~15 µm), demonstrating the potential

for compressibility-based separation. When separating MCF-7 cancer cells from white blood

cells, 71% of the MCF-7 cancer cells were recovered with a purity of 84%. This approach shows

tremendous potential for cancer diagnostics, where it is important to separate a small number of

leukemia cells from normal white blood cells.37

The taSSAW design was proven to be highly

tunable. By changing the operating parameters (i.e. flow rate and input power) as well as the

design parameters (i.e. IDT wavelength, IDT angle, channel height, and channel width), the

device could be tailored to different separation application. The taSSAW device also possesses

other advantages, such as its low cost, compact nature, ease of use, and low requirements for

external equipment. Overall, with slight improvements to the throughput (~2 µL/min for cell

separation), the taSSAW device could become a powerful tool for point-of-care diagnostic

applications.

24

CHAPTER 3: METHODOLOGY

3.1: Design Needs

There are many difficulties involved in transitioning from separating particles at the

micrometer scale to separating particles at the nanometer scale. From Equation 2.2, it can be seen

that the acoustic radiation force is directly proportional to the volume of the particle (Fr Vp). In

previous experiments, we had separated particles that were approximately 10 µm in diameter.

The objective of this project was to remove all particles larger than 500 nm in diameter. For an

acoustic separation device operating at a constant input power, frequency, and flow rate, the

acoustic radiation force experienced by a 500 nm particle is approximately 8,000 times less than

the acoustic radiation force experienced by a 10 µm particle. The magnitude of the acoustic

radiation force determines the lateral displacement of the particles. Therefore, in order to remove

all particles larger than 500 nm, we needed to generate sufficient acoustic radiation force to

move a 500 nm particle ~100 µm (the approximate distance required to move the particle to a

separate outlet) in the lateral direction. Many strategies were investigated to achieve this goal,

including: decreasing the flow rate (increases the amount of time particles spend in the acoustic

field and therefore increases the particles’ lateral migration), increasing the acoustic pressure, p0

(Fr p02), and decreasing the wavelength, λ (Fr λ

-1).

The most obvious way to increase the lateral migration of the particles is to decrease the

flow rate. The particles will spend a longer time in the acoustic field and attain a greater final

separation distance. However, decreasing the flow rate also decreases the throughput of the

device (the maximum volume of sample that can be processed per unit time). A minimum of ~

20 µL of sample is usually required for DLS measurements. As a result, we determined that the

minimum acceptable flow rate for our device should be ~ 1 µL/min in order to attain reasonable

25

sample processing times. In previous experiments using taSSAW devices for the manipulation of

micrometer sized particles, flow rates of ~2-10 µL/min were used. We were therefore limited in

the reductions we could make to the flow rates.

In order to increase the acoustic pressure, one can increase the power applied to the IDTs.

The acoustic pressure (p0) is proportional to the square root of the applied power, P (p0 P1/2

).

However, as the applied power is increased, heat is generated via resistive heating. This results in

heating of the sample fluid. When the fluid reaches ~60 °C, dissolved gasses begin to be released

(gas solubility decreases at increased temperatures).38

This leads to the formation of bubbles in

the microchannel that become trapped against the sidewall. These bubbles will continue to

expand, disrupting the laminar flow in the microchannel. Once the laminar flow is disrupted,

particles are no longer focused and will randomly exit both outlets of the device. Another issue

that arises at high temperatures is irreversible damage to the substrate. The LiNbO3 substrate is

prone to cracking from thermal stresses, so precautions must be made to control the temperature

of the substrate.39

Due to the heat related issues, it was determined that a thermoelectric cooling

system should be implemented. This would allow the device to operate at high input powers and

increase the acoustic pressure without running into the problems that are associated with

increased temperatures.

The final step taken to increase the lateral migration of nanometer sized particles was to

decrease the wavelength of the IDTs. Previous experiments used a wavelength of ~300 µm;

however, with access to the PSU nanofabrication facility, we were able to decrease the

wavelength to 100 µm. After obtaining the minimum flow rate and minimum wavelength, a

series of simulations were conducted to determine the optimal angle for nanoparticle separation.

A custom MATLAB code was developed to determine the effect that varying the tilted angle of

26

the IDTs, length of the IDTs, acoustic wavelength, input power, flow rate, and properties of the

sample fluid has on particle trajectories. In previous experiments, our simulations were able to

accurately predict particle trajectories at various operating conditions. Figure 3.1 shows the

simulation results (shaded regions) overlaid with experimental results (black dots) from previous

micrometer sized separation experiments.34

In Figure 3.1 (a), we see the trajectories of PS beads

of different sizes under a constant applied power, while Figure 3.1 (b) shows the trajectories of a

constant sized bead (15 µm) under different applied powers.

Figure 3.1: Comparison of simulated particle trajectories (shaded regions) and experimentally

observed particle trajectories (black dots) for (a) particles of different sizes and (b) different

input powers.34

The simulation results are generally in excellent agreement with the experimental results.

Similar simulations were used to obtain particle trajectories for nanometer sized particles. Figure

27

3.2 shows the trajectories of 500 nm (blue) and 1000 nm (red) PS beads. The solid black lines

represent the channel walls, while the dashed red lines represent the acoustic field. In all three

cases, the tilted angle of the IDTs is 15°. The vibration amplitude is increased from 10 nm in Fig.

3.2 (a) to 15 nm in Fig. 3.2 (b) to 20 nm in Fig. 3.3 (c).

Figure 3.2: Simulation results for the trajectories of 500 nm (blue) and 1000 nm (red) PS beads

a tilted IDT angle of 15° vibration amplitudes of at (a) 10 nm, (b) 15 nm, and (c) 20 nm.

It can be seen from the simulations that the 1000 nm beads were predicted to achieve a

lateral migration distance of ~400 µm and be pushed to the top channel wall at a vibration

amplitude of only 20 nm. Under the same conditions, the 500 nm particles attained a separation

distance of ~50 µm. These simulations indicated that the taSSAW platform could in fact be used

for nanoparticle separation. Next, we conducted simulations to see the effect of the tilted angle.

Figure 3.3 shows the trajectories of 500 nm (blue) and 1000 nm (red) PS beads under a constant

vibration amplitude of 20 nm, but different tilted angles. Fig. 3.3 (a) shows the particle

trajectories under the optimal tilted angle of 15°, while Fig. 3.3 (b) shows the particle trajectories

under a tilted angle of 30°. A series of simulations were conducted at increments of 5°, ranging

from 5° to 85°, to determine the optimal tilted angle.

28

Figure 3.3: Simulation results for the trajectories of 500 nm (blue) and 1000 nm (red) PS beads

a vibration amplitude of 20 nm and a tilted IDT angle of (a) 15° and (b) 30°.

While the simulations showed the potential for nanoparticle separation, we wanted to

increase the lateral displacement of the 500 nm particles. In order to achieve this, we looked into

ways of increasing the efficiency of the IDTs. Under our traditional design, surface waves

propagate in both directions from the IDTs. However, the microchannel is located in the center

of the two pairs of IDTs. Therefore, half of the acoustic energy never reaches the microchannel

when using a traditional IDT design. One way to increase the efficiency is to add reflectors at the

back end of each IDT to reflect acoustic energy back towards the microchannel. Another

approach is to design unidirectional IDTs, which only generate acoustic waves in the forward

direction.40-42

Unidirectional IDTs incorporate shorted floating electrodes with less acoustic

impedance than free space into their design, leading to higher vibrational amplitudes in the

forward direction.40

In addition, open floating electrodes with more acoustic impedance than free

space are incorporated to reflect waves towards the microchannel.40

Figure 3.4 (a) shows the

traditional IDT design,43

which generates waves that propagate in both directions, while Fig. 3.4

29

(b) shows the unidirectional IDT design.40

In both cases, coherent AC signals are applied to the

active fingers; however, no signals are applied to the shorted strips and open strips in the

unidirectional design.

Figure 3.4: Comparison of (a) traditional and (b) unidirectional IDT designs.40,43

The unidirectional, reflector (not shown), and traditional designs of IDTs were all

fabricated with the same number of electrode pairs (n=50) and identical wavelengths (λ=120 µm)

and their performance was analyzed via a 2 port network analyzer. In all cases, the IDTs were

fabricated by depositing a metal double layer (Cr/Au, 50 Å/500Å) with an e-beam evaporator

(Semicore Corp). More information on the IDT fabrication process can be found in Appendix B.

The resonance frequency for all three designs was similar (λtraditional=32.96 MHz, λreflector=32.96

MHz, λunidirectional=32.51 MHz); however, the unidirectional IDT showed the highest return loss

(17.78 dB), indicating it was the most efficient design. Therefore, in our nanoparticle separation

experiments we elected to use unidirectional IDTs.

30

3.2: Goals and Timeline

At the beginning of the project, a timeline was created, along with measures of success.

The project was divided into two phases. The first phase (6 months) was dedicated to

demonstrating the feasibility of nanoparticle separation, and the second phase (1.5 months) was

dedicated to prototype development. The goals for the first month of phase one were to

demonstrate the removal of 5 µm particles from smaller particles with 90% efficiency, and test

60-700 nm PS beads to check the loss of samples. In months 2-3, we would work to demonstrate

the removal of 5 µm particles from smaller particles with 95% efficiency, and demonstrate the

removal of 2 µm particles from smaller particles with 90% efficiency. In months 4-6, we sought

to demonstrate the removal of 1000 nm particles from smaller particles with 90% efficiency, and

demonstrate the removal of 500 nm particles from smaller particles with 90% efficiency. The

goals of phase 2 (months 7–8.5) were to use PMMA chips to replace PDMS chips, standardize

and streamline the fluidic design, develop a customized signal generator, amplifier, and other

electronics to drive SAW chips, and develop an Alpha prototype (an integrated system including

electronics in a box format). In all of our experiments, a DLS instrument (Zetasizer Nano,

Malvern Instruments) was used to quantify separation efficiency and sample loss. Videos of

experiments were obtained using a CCD camera (CoolSNAP HQ2, Photometrics) and analyzed

with ImageJ 1.46 software.

3.3: Experimental Setup

For initial experiments aimed at demonstrating the feasibility of nanoparticle separation,

polydimethylsiloxane (PDMS) was used as the material for the microchannel. PDMS is a good

material for rapid prototyping because devices can be made from standard soft lithography

techniques.44

This is an inexpensive process that enables the design of the device to be altered

31

fairly easily. A detailed procedure for PDMS device fabrication can be found in Appendix C. To

carry out particle separation experiments, the device was first immobilized on the stage of an

inverted microscope (Nikon TE2000U). In cases where the heat could potentially damage the

substrate, the device was immobilized on the surface of a thermoelectric cooler (TE Technology

Inc. CP-031). The inlet tubing was connected to a computer controlled syringe pump

(neMESYS, Cetoni GmbH) to allow for precise fluid control. The waste outlet tubing was

connected to another computer controlled syringe pump that was operated in withdrawal mode.

This ensures a stable, laminar flow inside of the microchannel and fixes the flow rate from the

outlet that is being collected. Figure 3.5 shows the device immobilized on the stage of the

thermoelectric cooler.

Figure 3.5: Device immobilized on thermoelectric cooler.

After the fluidic components have been connected, an RF signal generator (Agilent Tech,

E4422B) that is amplified with a power amplifier (Amplifier Research, 100A250A) is connected

32

to the IDTs. For the experiments using a cooling plate, an LED (MicroscopeNet, A92144L)

needed to be placed mounted underneath of the device to create a custom, reflection mode

microscope. Figure 3.6 (a) shows the signal generator and amplifier, Fig. 3.6 (b) shows the

cooling plate on the stage of the microscope and the syringe pumps, and Fig 3.6 (c) shows the

LED and connected leads of the IDTs.

Figure 3.6: Experimental setup including (a) signal generator and amplifier, (b) cooling plate

and pumps, and (c) LED.

The complex experimental setup is an example of the “chip-in-a-lab” approach referred

to in Chapter 1. In order for our acoustic separation technology to be commercially viable, the

development of a more compact experimental setup is required. Hence, the second phase of this

project was dedicated to prototype development.

33

CHAPTER 4: RESULTS

4.1: Separation in the Lab

Our first goal was to demonstrate the removal of 5 µm particles from smaller particles

with 90% efficiency. A solution containing 200 µL of 5 µm PS bead solution (1% solids by

volume), 40 µL of 240 nm PS bead solution (1% solids by volume), and 1 mL deionized (DI)

water was injected into the device at a flow rate of 5 µL/min. DI water was used as sheath fluid

and was injected into the device at a flow rate of 15 µL/min. Figure 4.1 shows images taken at

the outlet of the device during the particle separation process. When the acoustic power was

turned off (Fig. 4.1 (a)), all of the beads exited the bottom outlet. When the acoustic power was

turned on (Fig. 4.1 (b)), the 5 µm particles were pushed to the top outlet, while the 240 nm

particles remained in the bottom outlet (not visible). In both cases (acoustic on and acoustic off),

the bottom outlet was collected for 5 minutes and subsequently analyzed via DLS.

Figure 4.1: Acoustic separation of 5 µm beads from 240 nm beads when (a) the acoustic power

is off and (b) the acoustic power in turned on.

34

Although the particles are difficult to see, the DLS results indicate that nearly all of the 5

µm beads were removed from the sample. When the acoustic power was off, the Zetasizer

struggled to accurately measure the size distribution of the particles. This is due to the difficulties

involved in measuring particles larger than 500 nm (discussed in Chapter 1). There were three

separate peaks observed on the intensity plot, and each time the sample was analyzed, the

number and location of the peaks changed. The software reported an error message stating that

the “sample contains large particle/aggregates/dust” and the “sample is very polydisperse and

may not be suitable to DLS measurements”. For the sample collected when the acoustic power

was on, the Zetasizer produced a single peak near 240 nm that matched very well to the results

obtained from a control sample with the same concentration of 240 nm PS beads. Figure 4.2

compares the intensity plots from the acoustic off, acoustic on, and 240 nm control samples.

Figure 4.2: Intensity plots for the (a) unfiltered, (b) acoustic filtered, and (c) control samples.

35

After successfully demonstrating the removal of 5 μm beads from 240 nm beads, we

moved to separating 1.3 μm beads from 240 nm beads. A solution containing 140 µL of 1.3 µm

PS bead solution (1% solids by volume), 40 µL of 240 nm PS bead solution (1% solids by

volume), and 1 mL deionized (DI) water was injected into the device at a flow rate of 3 µL/min.

DI water was used as sheath fluid and was injected into the device at a flow rate of 9 µL/min.

Figure 4.3 shows the separation results. When the acoustic power was turned off (Fig. 4.3 (a)),

all of the beads exited the bottom outlet. When the acoustic power was turned on (Fig. 4.3 (b)),

the 5 µm particles were pushed to the top outlet, while the 240 nm particles remained in the

bottom outlet (not visible). In both cases (acoustic on and acoustic off), the bottom outlet was

collected for 5 minutes and subsequently analyzed via DLS (Fig 4.3 (c)).

Figure 4.3: Acoustic separation of 1.3 µm beads from 240 nm beads when (a) the acoustic

power is off and (b) the acoustic power in turned on. (c) DLS results from bottom outlet.

36

From the DLS data, it can be seen that nearly all of the 1.3 μm particles were removed

from the original (red) sample, while the acoustic filtered sample (green) matches reasonably

well with the 240 nm control (blue). Figure 4.4 shows the active region of the device when the

acoustic power is applied.

Figure 4.4: Active region of device.

The particles enter the active region focused in the center of the channel. As the 1.3 μm

particles enter the acoustic field, they are moved to pressure nodes (which cross the channel at a

15° angle). As a result, the majority of the 1.3 μm particles are pushed to the top of the channel.

The spacing, d, between the pressure nodes corresponds to half of the acoustic wavelength (λ =

100 μm, d = 50 μm). For the 240 nm particles, the acoustic radiation force is insufficient to push

the particles from their original streamline, and they remain focused in the center of the channel.

A similar experimental process was followed to demonstrate the separation of 900 nm beads

from 240 nm beads, 700 nm beads from 240 nm beads, and 500 nm beads from 240 nm beads.

The exact details for each case can be found in Appendix E. In each case, the bottom outlet was

collected and analyzed by DLS. Figure 4.5 shows the separation results for the separation of 900

nm beads from 240 nm beads. When the acoustic power was turned off (Fig. 4.5 (a)), all of the

beads exited the bottom outlet. When the acoustic power was turned on (Fig. 4.5 (b)), the 900 nm

particles were pushed to the top outlet, while the 240 nm particles remained in the bottom outlet

(not visible).

37

Figure 4.5: Acoustic separation of 900 nm beads from 240 nm beads when (a) the acoustic


Again, from the DLS data, it can be seen that nearly all of the 900 nm particles were

removed from the original (red) sample, while the acoustic filtered sample (green) matches

reasonably well with the 240 nm control (blue). Figure 4.6 shows the separation results for the

separation of 700 nm beads from 240 nm beads. When the acoustic power was turned off (Fig.

4.6 (a)), all of the beads exited the bottom outlet. When the acoustic power was turned on (Fig.

4.6 (b)), the 700 nm particles were pushed to the top outlet, while the 240 nm particles remained

in the bottom outlet (not visible).

38

Figure 4.6: Acoustic separation of 700 nm beads from 240 nm beads when (a) the acoustic


From the DLS data, it can be seen that nearly all of the 700 nm particles were removed

from the original (red) sample, while the acoustic filtered sample (green) matches reasonably

well with the 240 nm control (blue). The separation of 500 nm beads from 240 nm beads was

also performed (Appendix E); however, due to the difficulties in measuring bimodal samples, a

high concentration of 500 nm beads was required, which made it difficult to quantify the

separation efficiency. To measure the device’s separation efficiency, a sample containing only

600 nm beads was injected into the device. The bottom outlet was collected for 5 minutes while

the acoustic power was turned off and for 5 minutes when the acoustic power was turned on.

39

DLS was then used to obtain count rates (rather than intensity plots) for each sample. When the

acoustic power was off, an average count rate (measured in kilo counts per second (kcps)) of

62,464 kcps was obtained; when the acoustic power was on, this count rate decreased to 8,942

kcps, indicating that approximately 86% of the 600 nm particles were removed. Figure 4.7 shows

a histogram of the count rates for the acoustic off (red) and acoustic on (green) samples.

Figure 4.7: Comparison of count rates for a filtered vs. unfiltered sample containing 600 nm PS

beads.

After demonstrating the ability of our device to successfully separate nanoparticles, we

began to develop a prototype. The construction of the prototype involved replacing the

expensive, bulky signal generator and amplifier used in the lab with a more compact,

inexpensive, custom signal generator and amplifier. In addition, the PDMS chips were replaced

with PMMA chips, a much harder, more robust material. Details of the PMMA chip fabrication

40

can be found in Appendix D. To evaluate the functionality of the PMMA chip, we first

performed particle separation experiments using the lab equipment. A sample containing a

mixture of 5 μm, 1.3 μm and 240 nm PS beads was injected into the PMMA chip at a flow rate

of 2 μL/min. DI water was used as the sheath fluid and injected into the chip at 10 μL/min.

Figure 4.8 shows the separation results. When the SAW was turned off (Fig. 4.8 (a)), all of the

beads exited the bottom outlet. When the SAW was turned on (Fig. 4.8 (b)), the 5 μm and 1.3 μm

particles were pushed to the top outlet, while the 240 nm particles remained in the bottom outlet

(not visible).

Figure 4.8: PMMA chip for acoustic separation of 5 μm and 1.3 μm beads from 240 nm beads

when (a) the SAW is off and (b) the SAW is turned on.

In both cases, the bottom outlet was collected for 10 minutes and analyzed by DLS.

Figure 4.9 shows the intensity plots obtained by DLS. For the SAW OFF sample (Fig. 4.9 (a)),

the presence of large particles prevented accurate analysis by DLS. The Zetasizer produced many

error messages pertaining to the quality of the sample, and produced different results each time

the sample was analyzed. For the SAW ON sample (Fig. 4.9 (b)), the Zetasizer was able to

41

accurately measure the particles, as evidenced by the favorable comparison to a 240 nm control

sample (Fig. 4.9 (c)).


Although we were able to successfully separate particles using the PMMA chip, further

testing revealed that the PMMA chip was susceptible to leakage problems after multiple uses.

We are still in the process of optimizing the PMMA bonding process; however, due this issue,

PDMS chips were used to test the functionality of the prototype.

42

4.2: Prototype testing

The main purpose of the prototype was to simplify the experimental setup. A custom

signal generator and amplifier were designed and constructed to replace their bench top

counterparts. An off the shelf power supply was purchased to power the signal generator and

amplifier. A commercially available cooling system was also purchased. All of the components

were placed in a custom built, metal chassis, and finally, a camera was mounted on top of the

cooling plate to allow users to observe the particle separation process and troubleshoot if

necessary. Figure 4.10 shows completed prototype.

Figure 4.10: Images of the (a) completed prototype, (b) cooling stage, and (c) outlet collection.

43

Fig. 4.10 (a) shows the device when the lid to the platform is closed. The USB camera is

connected to a laptop or computer with image processing software capabilities. In Fig. 4.10 (b)

the lid to the platform is opened. The actual separation chip is placed on top of the cooling plate,

and connected to external pumps. Fig 4.10 (c) shows the waste and sample collection outlets.

Disposable microcentrifuges tubes, which can easily be inserted and removed from the fixed

support structure, are used to collect the sample and waste. Figure 4.11 shows a picture of the

prototype with the lid removed to expose the layout of the signal generator, amplifier, cooling

plate, and power supplies.

Figure 4.11: Internal layout of prototype.

We first tested the prototype’s ability to repeat the separation of 1.3 µm beads from 240

nm beads. The same parameters (sample concentration, flow rate, collection time) that were used

44

in the laboratory experiments were used when testing the prototype. Figure 4.12 shows the DLS

results.


In the unfiltered sample (Fig. 4.12 (a)), there are two distinct peaks; however, in both the

acoustic filtered sample (Fig. 4.12 (b)) and control sample (Fig. 4.12 (c)), there is only a single

peak. This indicated that the prototype was able to remove the majority of the 1.3 µm particles.

Although the quality of the images obtained using the USB camera is not very high (Figure

45

4.13), they show the particles being pushed to the top outlet. When no power is applied (Fig.

4.13 (a)), the particles in the active region (the area between the gold L-shaped markings) travel

along the bottom wall. After the power is applied (Fig. 4.13 (b)), particles in the active region are

pushed towards the top wall.

Figure 4.13: Images taken from USB camera depicting the particle separation process on the

prototype, (a) acoustic power off and (b) acoustic power on.

We also separated 900 nm particles from 240 nm particles using the prototype. The DLS

results can be found in Appendix E. As in previous experiments, the peak observed in the

unfiltered sample was removed in the acoustic filtered sample. Overall, the prototype was proven

to successfully separate particles of different sizes, and it greatly simplified the experimental

setup. Further validation studies are in progress. Figure 4.14 compares the initial experimental

setup to the setup enabled by the prototype. The prototype setup is much more compact, allowing

for true “lab-on-a-chip” applications.

46

Figure 4.14: (a) Laboratory vs. (b) prototype experimental setups for acoustic nanoparticle

separation.

47

CHAPTER 5: CONCLUSION

5.1: Summary

This project demonstrated the feasibility of acoustic nanoparticle separation. Experiments

were first performed in a laboratory setting to demonstrate the ability of our microfluidic chips.

We successfully removed 5 µm, 1.3 µm, 900 nm, 700 nm, and 500 nm PS beads from 240 nm PS

beads. In each case, only one peak was observed on the intensity plots of the acoustically filtered

samples, indicating that the majority of the larger particles were removed. In addition, the

filtered samples compared favorably with control samples that contained only 240 nm particles.

These results showed that an acoustic filter could be used in conjunction with a DLS instrument

to improve the accuracy of DLS measurements. However, due to the requirements for expensive

equipment and complex experimental setups, the commercial viability of our technology was

limited. In addition, the material used to fabricate our microfluidic chips, PDMS, is a soft,

deformable polymer that is not suitable for many commercial applications. In order to overcome

these limitations, we developed a prototype that replaced the expensive laboratory equipment

with compact, custom electronics. We fabricated microfluidic chips from PMMA, a much more

durable, thermoplastic, and successfully demonstrated their ability to separate 5 µm, 1.3 µm, and

900 nm PS beads from 240 nm PS beads. Further testing of the prototype is still underway;

however, the prototype holds promise for future nanoparticle separation applications.

5.2: Future Recommendations

In the next phase of development, we should seek to incorporate micropumps into the

prototype. Currently, syringe pumps are the only external component required for the prototype

to operate. If pumps can be integrated into the next prototype, it will be able to operate as a

48

standalone unit. This would enable portable applications and further simplify the experimental

setup. To further reduce the size of the device, the power supply for the cooling plate and the

power supply for the signal generator and amplifier should be integrated into a single power

supply. Aside from improvements to the prototype itself, I think it is important to demonstrate

the potential of acoustic nanoparticle separation in more diverse applications. For example, our

technology could potentially be used to separate components of the blood. This could be

particularly useful in the isolation of exosomes, 30-100 nm vesicles secreted by cells.45

Current

approaches to isolating exosomes require ultracentrifugation, which is time consuming, suffers

from low recovery rates (5-25% of initial exosome population)46

, and alters the morphology of

the exosomes.47

Our technique would eliminate the need for expensive centrifuges and we would

expect it to achieve higher recovery rates because the entire isolation process would take place

on a single chip. This differs from ultracentrifugation where multiple washing and resuspension

steps result in a loss of exosomes. Finally, we would expect our acoustic separation technology

to better preserve the integrity of the exosomes because the power intensity and frequency used

in our acoustic chip (0.1─3 W/cm2, 6─40 MHz)

30-34 are in a similar range as those used in

ultrasonic imaging (~0.5 W/cm2, 2─20 MHz)

48-51, which has proven to be extremely an

biocompatible monitoring technique.

49

Appendix A

Team

Throughout this research opportunity, I had the pleasure of interacting with most

members of the Penn State Acoustofluidics lab. Shown below is a picture of the group, along

with Dr. Tony Huang (center of the front row).

I would especially like to thank Yuchao Chen and Feng Guo. I worked with Yuchao for

most of the SAW separation work. Yuchao was instrumental in arranging project meetings for

brainstorming and had many helpful suggestions that were needed for the success of the project.

Feng provided excellent instruction on how to fabricate microfluidic chips and provided

excellent guidance for understanding the underlying physics behind SAW separation.

50

Appendix B

IDT Fabrication

The following IDT fabrication process, adapted from Shi et al., was followed:

“To achieve SAW with a high coupling coefficient, a Y + 128 X-propagation lithium

niobate (LiNbO3) wafer (500 mm thick) was used as the substrate for IDT deposition.

The LiNbO3 wafer was patterned with photoresist (SPR3012, MicroChem, Newton,

MA), a double metal layer (Cr/Au, 50 A˚ /800 A˚) was deposited (e-beam evaporator,

Semicore Corp) on the wafer, and a lift-off process was used to remove the photoresist

and the metal attached, thus obtaining the IDTs for SAW generation.”30

Figure A1: IDT Fabrication process.30

51

Appendix C

PDMS Fabrication

The polydimethylsiloxane (PDMS) microfluidic channel was fabricated using a mold-

replica procedure. The master mold was obtained by performing deep reactive ion etching

(DRIE, Adixen, Hingham, MA) on a pre-patterned silicon wafer with spun-on photoresist. Once

the master mold was fabricated, the microfluidic chamber was made by simply filling the mold

with PDMS gel, allowing it to cure, and then removing it from the mold. In order to make the

removal of PDMS easier, the master mold was first coated with 1H, 1H, 2H, 2H-

perfluorooctyltrichlorosilane (Sigma Aldrich) to reduce the surface energy. In order to form the

PDMS gel, Sylgard184TM Silicone Elastomer base was mixed with curing agent (Dow Corning,

Midland, MI) according to a 10:1 weight ratio. The mixture was cured at 70°C for 17 min to

remove all bubbles. After the PDMS was degassed, it was cut and peeled from the mold. Inlets

and outlets were made using a 0.75 mm punch (Harris uni-core). Finally, the PDMS was bonded

onto a lithium niobate substrate to form a sealed microchannel, and plastic tubing was connected

to the inlets and outlets.

52

Appendix D

PMMA Fabrication

The PMMA was fabricated using a laser cutter (Epilog Laser). The channel designs were

first drawn in SolidWorks, and then uploaded to the laser cutter. A blank sheet of PMMA was

loaded into the laser cutter, and multiple devices were simultaneously etched into the sheet.

Figure A2: PMMA sheet with multiple etched devices.

Each device was then cut from the sheet using a hand held acrylic cutter (Plaskolite).

Two different bonding methods were used to bond the PMMA channels to the LiNbO3 substrate:

UV epoxy (Thorlabs, NOA 61) and a double sided tape (3M, 524CW).

53

Appendix E

Supplementary Results

For the separation of 900 nm PS beads from 240 nm PS beads, a solution containing 200

µL of 900 nm PS bead solution (1% solids by volume), 40 µL of 240 nm PS bead solution (1%

solids by volume), and 1 mL deionized (DI) water was injected into the device at a flow rate of 5

µL/min. DI water was used as sheath fluid and was injected into the device at a flow rate of 10

µL/min. The samples were collected for 10 minutes each (acoustic on, acoustic off) and diluted

with DI water to 1 mL.













The DLS data from the separation of 500 nm PS beads from 240 nm PS beads is shown

below in Figure A3.

54

Figure A3: Acoustic separation of 500 nm beads from 240 nm beads when (a) the acoustic


Due to the difficulties in measuring bimodal samples and the high concentration of 500

nm beads used, the second peak appears close to 1000 nm rather than 500 nm in the unfiltered

sample. However, only one peak is observed in the acoustic filtered sample.

The DLS data from the separation of 900 nm PS beads from 240 nm PS beads using the

prototype is shown below in Figure A4.

55

Figure A4: Intensity plots for the (a) unfiltered, (b) acoustic filtered, and (c) control samples.

When the acoustic power was off, the Zetasizer struggled to accurately measure the size

distribution of the particles. The location of the peaks changed each time the sample was

analyzed. For the sample collected when the acoustic power was on, the Zetasizer produced a

single peak near 240 nm that matched very well to the results obtained from a control sample

with the same concentration of 240 nm PS beads.

56

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Joseph Rufo [email protected]

Campus Address Permanent Address

300 Waupelani Drive, Apt. 1045 701 Haviland Drive

State College, PA 16801 Bryn Mawr, PA 19010

(610) 420-0630 (610) 420-0630

OVERVIEW

Enthusiastic, disciplined student with a passion for continued learning. My strong

communication skills and technical writing ability have made me a valuable asset in a

multidisciplinary engineering lab. I am seeking a position where I can leverage these skills to add

value to a dynamic organization.

EDUCATION

The Pennsylvania State University, University Park, PA (May 2015)

M.S. Engineering Science and Mechanics

GPA 3.85/4.0

The Pennsylvania State University, University Park, PA (May 2013)

B.S. Engineering Science and Mechanics, Nanotechnology minor

GPA 3.43/4.0

PROFESSIONAL SKILLS

Experience with standard soft lithography fabrication techniques, signal processing, optical

system design, and computational fluid dynamics. Working knowledge of C++ and MATLAB to

create mathematical models. Skilled in using SolidWorks and AutoCAD to create 3D models.

EXPERIENCE

Penn State Acoustofluidics Lab, University Park, PA (August 2012 – current)

Graduate Student Researcher

Led an industry sponsored research project to design and develop a prototype for acoustic

nanoparticle separation.

Created a project timeline, established target goals and measures of success, assigned

various tasks to members of the groups, presented progress in weekly internal meetings,

and wrote bi-weekly status updates for our industry sponsor.

Collaborated with team to develop a low-cost, portable, flow cytometer.

Received second place for the P.B. Breneman Best Design in Research Award.

Asymmetric Therapeutics, State College, PA (January 2013 – May 2013)

Design Engineer / Senior Capstone Project

Worked with engineering team and medical professionals at the University of Pittsburgh

Medical Center, to design and build an affordable, non-paramagnetic chair for

magnetoencephalography (MEG) testing.

Performed patent searches, conducted feasibility studies, and carried out extensive

research on material properties.

Comcast, Downingtown, PA (June 2012-August 2012)

P&E Engineering Metrics and Analysis Intern

Tested equipment from a variety of vendors to assess product performance across a wide

range of physical and environmental conditions.

Created reports that aided in qualifying products for use in the Comcast network. PUBLICATIONS

Po-Hsun Huang, Nitesh Nama, Zhangming Mao, Peng Li, Joseph Rufo, Yuchao Chen,

Yuliang Xie, Cheng-Hsin Wei, Lin Wang, and Tony Jun Huang, A reliable, programmable

acoustofluidic pump powered by oscillating sharp-edge structures, Lab on a Chip, Vol.

14, pp. 4319-4323, 2014.

Chenglong Zhao, Yuliang Xie, Zhangming Mao, Yanhui Zhao, Joseph Rufo, Shikuan Yang,

Feng Guo, John D. Mai, and Tony Jun Huang, Theory and experiment on particle

trapping and manipulation via optothermally generated bubbles, Lab on a Chip, Vol. 14,

pp.384-391, 2014.

Ahmad Ahsan Nawaz, Xiangjun Zhang, Xiaole Mao, Joseph Rufo, Sz-Chin Steven Lin, Feng

Guo, Yanhui Zhao, Michael Lapsley, Peng Li, J. Philip McCoy, Stewart J. Levine, and Tony

Jun Huang, Sub-micrometer-precision, three-dimensional (3D) hydrodynamic focusing

via “microfluidic drifting”, Lab on a Chip, Vol. 14, pp. 415-423, 2014.

Po-Hsun Huang, Yuliang Xie, Daniel Ahmed, Joseph Rufo, Nitesh Nama, Yuchao Chen,

Chung Yu Chan, and Tony Jun Huang, An acoustofluidic micromixer based on oscillating

sidewall sharp-edges, Lab on a Chip, Vol. 13, pp. 3847-3852, 2013.

Yanhui Zhao, Danqi Chen, Hongjun Yue, Jarrod B. French, Joseph Rufo, Stephen J.

Benkovic and Tony Jun Huang, Lab-on-a-chip technologies for single-molecule

studies, Lab on a Chip, Vol. 13, pp. 2183-2198, 2013.

Yuliang Xie, Chenglong Zhao, Yanhui Zhao, Sixing Li, Joseph Rufo, Shikuan Yang, Feng

Guo, and Tony Jun Huang, Optoacoustic tweezers: a programmable, localized cell

concentrator based on opto-thermally generated, acoustically activated, surface

bubbles, Lab on a Chip, Vol. 13, pp. 1772-1779, 2013.

ACHIEVEMENTS

P.B. Breneman Best Design in Research Award, Second Place, 2013 Dean’s List (3 semesters), 2009-2012 President’s Freshman Award, The Pennsylvania State University, 2009 Captain of Second City Troop Youth Rugby Football Club, 2009

ACTIVITIES

Second City Troop Rugby Football Club, 2008 – current

Teaching Assistant for Engineering Mechanics, January 2014 – January 2015

FROM CHIP-IN-A-LAB TO LAB-ON-A-CHIP: THE DEVELOPMENT …

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