FROM CHIP-IN-A-LAB TO LAB-ON-A-CHIP: THE DEVELOPMENT OF A PROTOTYPE FOR ACOUSTOFLUIDIC NANOPARTICLE SEPARATION

Transcription

1 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

2 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.

3 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 noninvasive (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.

4 iv TABLE OF CONTENTS List of Figures... v List of Equations... vi CHAPTER 1: INTRODUCTION : Dynamic Light Scattering Theory : Problem Statement : Objective CHAPTER 2: LITERATURE REVIEW : Conventional Separation Techniques : Acoustic Particle Separation CHAPTER 3: METHODOLOGY : Design Needs : Goals and Timeline : Experimental Setup CHAPTER 4: RESULTS : Separation in the Lab : Prototype Testing CHAPTER 5: CONCLUSIONS : Summary : Future Work Suggestions Appendix A: Team Appendix B: IDT Fabrication Appendix C: PDMS Fabrication Appendix D: PMMA Fabrication Appendix E: Supplementary Results References... 56

5 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 Figure 2.2: Calibration curves for SEC Figure 2.3: FFF underlying mechanism Figure 2.4: Normal vs. steric modes of FFF Figure 2.5: Schematic of a typical SPLITT separation device Figure 2.6: Acoustic tweezers for continuous particle separation Figure 2.7: Tilted angle acoustic tweezers Figure 3.1: Simulated and experimental microparticle trajectories Figure 3.2: Simulated nanoparticle trajectories at different vibrational amplitudes Figure 3.3: Simulated nanoparticle trajectories at different tilted angles Figure 3.4: Comparison of traditional and unidirectional IDT designs Figure 3.5: Device immobilized on thermoelectric cooler Figure 4.1: Acoustic separation of 5 µm beads from 240 nm beads Figure 4.2: Intensity plots obtained via DLS Figure 4.3: Acoustic separation of 1.3 µm beads from 240 nm beads Figure 4.4: Active region of separation device Figure 4.5: Acoustic separation of 900 nm beads from 240 nm beads Figure 4.6: Acoustic separation of 700 nm beads from 240 nm beads Figure 4.7: Determining separation efficiency Figure 4.8: Acoustic separation with PMMA chip Figure 4.9: Intensity plots obtained via DLS for PMMA chip Figure 4.10: Photos of prototype Figure 4.11: Internal layout of prototype Figure 4.12: Intensity plots obtained via DLS for prototype Figure 4.13: USB camera images of particle separation Figure 4.14: Comparison of experimental setups... 46

6 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 Equation 2.2: Acoustic radiation force Equation 2.3: Acoustic contrast factor Equation 2.4: Stokes drag force... 20

7 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 characterization 1 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 19 th 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

8 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, I o, 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

9 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

10 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 m 2 /s, while concentration is generally measured in units of mol/m 3. For spherical particles in solution, the diffusion coefficient can be determined by the Stokes-Einstein relation, shown in Equation 1.3. (1.3) Where k B 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

11 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

12 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, g 2 (q;τ), is shown below:

13 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 g 1 (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)

14 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

15 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 M n represents the number average molecular weight and M w represents the weight average molecular weight. These two quantities can be obtained from: (1.10) And: (1.11)

16 10 Where N i is the number of molecules and M i is the molecular mass. For samples containing latex standard spheres, PDI values are typically < 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

17 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 syphilis 14 and HIV 15 ), 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

18 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.

19 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) 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

20 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 (V e ). 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

21 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

22 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.

23 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

24 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

25 19 first technology to use SSAWs to manipulate and pattern cells and microparticles, but it has since been applied to continuous flow applications 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

26 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 (F r ) can be expressed as: 36 ( ) ( ) ( ) (2.2) ( ) (2.3) In Equations 2.2 and 2.3, p 0, V p, λ, k, x, ρ p, ρ f, β p, and β f 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 (F d ), which is given by the following equation: ( ) (2.4) Where η, R p, u p, and u f 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

27 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

28 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

29 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.

30 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 (F r V p ). 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, p 0 (F r p 2 0 ), and decreasing the wavelength, λ (F r 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

31 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 (p 0 ) is proportional to the square root of the applied power, P (p 0 P 1/2 ). 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

32 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

33 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.

34 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 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

35 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 (λ traditional =32.96 MHz, λ reflector =32.96 MHz, λ unidirectional =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.

36 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 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

37 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

38 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.

39 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.

40 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.

41 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.

42 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).

43 37 Figure 4.5: Acoustic separation of 900 nm 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. 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).

44 38 Figure 4.6: Acoustic separation of 700 nm 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. 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.

45 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

46 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

47 41 accurately measure the particles, as evidenced by the favorable comparison to a 240 nm control sample (Fig. 4.9 (c)). Figure 4.9: Intensity plots for the (a) unfiltered, (b) acoustic filtered, and (c) control samples. 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.

48 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.

49 43 Fig (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 (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

50 44 in the laboratory experiments were used when testing the prototype. Figure 4.12 shows the DLS results. Figure 4.12: Intensity plots for the (a) unfiltered, (b) acoustic filtered, and (c) control samples. In the unfiltered sample (Fig (a)), there are two distinct peaks; however, in both the acoustic filtered sample (Fig (b)) and control sample (Fig (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

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

52 46 Figure 4.14: (a) Laboratory vs. (b) prototype experimental setups for acoustic nanoparticle separation.

53 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

54 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, 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/cm 2, 6 40 MHz) are in a similar range as those used in ultrasonic imaging (~0.5 W/cm 2, 2 20 MHz) 48-51, which has proven to be extremely an biocompatible monitoring technique.

55 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.

56 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 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

57 51 Appendix C PDMS Fabrication The polydimethylsiloxane (PDMS) microfluidic channel was fabricated using a moldreplica 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, 2Hperfluorooctyltrichlorosilane (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.

58 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).

59 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. For the separation of 700 nm PS beads from 240 nm PS beads, a solution containing 120 µl of 700 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. For the separation of 500 nm PS beads from 240 nm PS beads, a solution containing 240 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.

60 54 Figure A3: Acoustic separation of 500 nm 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. 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.