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Particle Dynamics in a Dielectrophoretic Microdevice

01 Jan 2006-pp 259-276
TL;DR: In this article, a dielectrophoretic device was designed to trap, separate, and concentrate biological components carried in solution, where the operating principle of the device is the dielectric interaction between the spheres and the fluid.
Abstract: A dielectrophoretic device has been designed to trap, separate, and concentrate biological components carried in solution. The operating principle of the device is the dielectrophoretic interaction between the spheres and the fluid. The device was designed and manufactured by at Purdue University [6]. The device consists of a microchannel with a depth of 11.6 μm, width of 350 μm, and length of 3.3 mm. The channel was anisotropically etched in silicon to produce a trapezoidal cross-section. The channel was covered by a piece of anodically bonded glass. A schematic view and digital photo of the device are shown in Figure 13.1. Bright regions represent platinum electrodes and the dark regions represent the electrode gaps. The electrodes are covered by a 0.3 μm thick layer of PECVD silicon dioxide, which insulates the electrodes from the liquid medium, suppressing electrolysis. The electrodes are arranged in interdigitated pairs so that the first and third electrodes from Figure 13.1 are always at the same potential. The second and fourth electrodes are also at the same potential, but can be at a different potential than the first and third electrodes. An alternating electric potential is applied to the interdigitated electrodes to create an electromagnetic field with steep spatial gradients. Particle motion through the resulting electric field gradients causes polarization of the suspended components, resulting in a body force that repels particle motion into increasing field gradients.

Summary (2 min read)

1.1 DEP Device

  • A dielectrophoretic device has been designed to trap, separate, and concentrate biological components carried in solution.
  • The operating principle of the device is the dielectrophoretic interaction between the spheres and the fluid.
  • The channel was covered by a piece of anodically bonded glass.
  • The electrodes are arranged in interdigitated pairs so that the first and third electrodes from Figure 1 are always at the same potential.
  • An HP 33120A arbitrary waveform generator was used as the AC signal source to produce sinusoidal signal with frequency specified at 1MHz.

1.2 Micro Particle Image Velocimetry

  • Images of the particles were acquired using a standard µPIV system.
  • The particles are coated with a red fluorescing dye (λabs=542 nm, λemit=612 nm).
  • A three-by-three pixel binning scheme was used in this experiment, producing images measuring 464 by 346 pixels, which were captured at a speed of 20 frames per second.
  • One problem with depthwise velocity gradients is cross-correlation peak deformation which reduces the signal to noise ratio of a PIV measurement (Cummings, 2001).
  • There are both hardware and software approaches toward resolving these problems.

2.1 Deconvolution Method

  • Simulated PIV experiments show a qualitative similarity between the cross-correlation function from a test flow containing velocity gradients in the depthwise direction and the corresponding velocity histogram for that depthwise gradient, suggesting that the deconvolution procedure may work.
  • This hypothesis is based on observations of cross-correlations from experimental situations.
  • The new idea is to extract velocity distributions by deconvolving a PIV cross-correlation function with its autocorrelation.
  • One drawback to deconvolution procedures is sensitivity to noise resulting from a division operation in frequency space.
  • Thus, it is important to have high information density in both the crosscorrelation and autocorrelation.

2.2 Synthetic Image Method

  • Since deconvolution is inherently sensitive to noise, it would be beneficial to eliminate the deconvolution step from the velocity profile extraction process.
  • Since the deconvolution of a function with an impulse is the original function, this would render the deconvolution operation trivial and unnecessary.
  • The autocorrelation is related to the particle intensity distributions from a set of PIV recordings, i.e. the shape of particles in an image pair.
  • This is the basis for the synthetic image method.
  • Furthermore, in µPIV the particles are already very small, so making them any smaller could render them invisible to the camera; imaged particle intensity decreases proportionally as the cube of particle radius.

2.3 Comparison of Techniques

  • Three cases were examined to compare and validate the two methods of velocity distribution extraction from cross-correlation PIV.
  • The first is a uniform flow (depth of field small compared to flow gradients), the second is a linear shear (near wall region of a channel flow), and the third is a parabolic channel flow (depth averaged pressure-driven flow in a shallow micro device).
  • The uniform one dimensional velocity profile simulated in this case study is given by Vx = 6.294.
  • The synthetic image method is a slightly better predictor of the velocity histogram than the deconvolution method by virtue of the steeper gradients at the edges of the distribution.
  • The same spurious oscillations were found, so they must be inherent to this particular case.

3 EXPERIMENTAL RESULTS

  • The experiments presented here are designed to quantify the dielectrophoretic performance of the device.
  • Consequently the particle intensity distributions as recorded by the camera are partly due to the geometric image of the particle and partly due to diffraction effects.
  • A uniform color scale was applied to these figures for representing velocity magnitude, such that velocity changes between cases can be more easily interpreted.
  • A second type of PIV analysis was performed in which the velocity distribution within each interrogation was extracted by either deconvolving the autocorrelation function for the interrogation spot with the cross-correlation function or by using an image processing technique.
  • It can be qualitatively confirmed that particles migrate to the top of the channel by observing particle shapes in the images from the higher voltage cases, i.e. comparing the many particle shapes found in Figure 2 (top) which is acquired at 0.5 volts with the single particle shape found in Figure 2 which is acquired at 4.0 volts.

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Particle Dynamics in a Dielectrophoretic Microdevice
S.T. Wereley and I. Whitacre
Purdue University, School of Mechanical Engineering,
West Lafayette, IN 47907-2088, USA, wereley@purdue.edu
ABSTRACT
Micro particle image velocimetry (µPIV) has been effectively used to measure fluid velocities in micron scale devices, such as
a microchannel with moderate depth. Measurements of pressure driven flows through very shallow or thin microchannels can
be obscured by depthwise velocity gradients which deform cross-correlation peaks used to measure velocity. Two methods are
proposed for deducing velocity distributions from cross-correlation-based PIV measurements. One method, the deconvolution
method, involves deconvolving a cross-correlation with an autocorrelation. This method is shown to approximate velocity
distributions, but was found to be sensitive to noise in correlations and is subject to spurious oscillations. A second method,
the synthetic image method, was developed to improve upon the successes of the deconvolution method. This method
involves extracting the locations of particles in a PIV recording and generating simulated particle images with a prescribed
single pixel particle intensity distribution. This method was found to accurately predict simulated and experimental velocity
distributions. These methods are benchmarked with simulated case studies, and used to measure velocity distributions in a
dielectrophoretic particle trapping device. The dielectrophoretic forces are seen to cause the particle to migrate toward the top
of the device and to trap them near the leading edge of each electrode.
1 INTRODUCTION AND SET UP
1.1 DEP Device
A dielectrophoretic device has been designed to trap, separate, and concentrate biological components carried in solution.
The operating principle of the device is the dielectrophoretic interaction between the spheres and the fluid. The device was
designed and manufactured by Haibo Li at Purdue University, a student in Prof. Bashir’s research group (Li, et al, 2004). The
device consists of a microchannel with a depth of 11.6 µm, width of 350 µm, and length of 3.3 mm. The channel was
anisotropically etched in silicon to produce a trapezoidal cross-section. The channel was covered by a piece of anodically
bonded glass. A schematic view and digital photo of the device are shown in Figure 1. Bright regions represent platinum
electrodes and the dark regions represent the electrode gaps. The electrodes are covered by a 0.3 µm thick layer of PECVD
silicon dioxide, which insulates the electrodes from the liquid medium, suppressing electrolysis. The electrodes are arranged
in interdigitated pairs so that the first and third electrodes from Figure 1 are always at the same potential. The second and
fourth electrodes are also at the same potential, but can be at a different potential than the first and third electrodes. An
alternating electric potential is applied to the interdigitated electrodes to create an electromagnetic field with steep spatial
gradients. Particle motion through the resulting electric field gradients causes polarization of the suspended components,
resulting in a body force that repels particle motion into increasing field gradients. In the experiments, sample solutions were
injected into the chamber using a syringe pump (World Precision Instruments Inc., SP200i) and a 250µl gas-tight luer-lock
syringe (ILS250TLL, World Precision Instruments Inc.). The flow rate could be adjusted and precautions were taken to avoid
air bubbles. An HP 33120A arbitrary waveform generator was used as the AC signal source to produce sinusoidal signal with
frequency specified at 1MHz.
1.2 Micro Particle Image Velocimetry
Images of the particles were acquired using a standard µPIV system. In these experiments a mercury lamp is used to
illuminate the 0.7 µm polystyrene latex (PSL) microspheres (Duke Scientific) that are suspended in de-ionized water in
concentrations of about 0.1% by volume. The particles are coated with a red fluorescing dye (λ
abs
=542 nm, λ
emit
=612 nm). The
images were acquired using a Photometrics CoolSNAP HQ interline transfer monochrome camera (Roper Scientific). This
camera is capable of 65% quantum efficiency around the 610 nm wavelength. The largest available image size that can be
accommodated by the CCD array is 1392 by 1040 pixels, but the camera has the capability of pixel binning, which can
drastically increase the acquisition frame rate by reducing the number of pixels that need to be digitized. A three-by-three
pixel binning scheme was used in this experiment, producing images measuring 464 by 346 pixels, which were captured at a
speed of 20 frames per second. The average focused particle diameter in the images was approximately 3 pixels.
Shallow Channel Considerations
When performing µPIV measurements on shallow microchannels, the depth of focus of the microscope can be comparable in
size to the depth of the flow. A PIV cross-correlation peak, the location of which is the basis for conventional PIV velocity
measurements, is a combination of the velocity distribution in the interrogation region and some function of average particle

shape. PIV velocity measurements containing velocity gradients can substantially deviate from the ideal case of depthwise
uniform flow. Gradients within the light sheet plane have been addressed by image correction techniques (Wereley, et al.,
2002), but gradients in the depthwise direction remain problematic. They can cause inaccurate velocity measurements due to
the presence of multiple velocities within an interrogation region that are independent of mesh refinement. One problem with
depthwise velocity gradients is cross-correlation peak deformation which reduces the signal to noise ratio of a PIV
measurement (Cummings, 2001). Cross-correlation peak deformation can also reduce the effectiveness of subpixel peak fitting
schemes which are based on a particular cross-correlation shape, such as a common five point Gaussian fit. There are both
hardware and software approaches toward resolving these problems. In situations where a large in plane region must be
imaged in a relatively thin device, the physics of the imaging system dictate that the entire depth of the channel will be
focused. Hence a software approach must be used. Two different approaches are explored: 1. deconvolving the cross
correlation with the autocorrelation and 2. image processing to replace the original particle images with unit impulse particle
images.
2 MODELING/THEORY
2.1 Deconvolution Method
Simulated PIV experiments show a qualitative similarity between the cross-correlation function from a test flow containing
velocity gradients in the depthwise direction and the corresponding velocity histogram for that depthwise gradient, suggesting
that the deconvolution procedure may work. The major hypothesis of the deconvolution method is that the PIV cross-
correlation function can be approximated by the convolution of a particle image autocorrelation with the velocity distribution
in the interrogation region. This hypothesis is based on observations of cross-correlations from experimental situations. For
the case of a uniform flow, the velocity distribution is an impulse, and the resulting cross-correlation can be approximated by a
position-shifted autocorrelation. This suggests that the cross-correlation can be approximated by a convolution of the impulse
velocity distribution and an image autocorrelation.
Olsen and Adrian (2000) approximated the cross-correlation as a convolution of mean particle intensity, a fluctuating noise
component, and a displacement component. Deconvolution of a cross-correlation with an autocorrelation is used by
Cummings (1999) to increase the signal-to-noise ratio for a locally uniform flow. The new idea is to extract velocity
distributions by deconvolving a PIV cross-correlation function with its autocorrelation. The result is a two-dimensional
approximation of the underlying velocity distribution. One drawback to deconvolution procedures is sensitivity to noise
resulting from a division operation in frequency space. Thus, it is important to have high information density in both the cross-
correlation and autocorrelation. The information density can be increased by correlation averaging both the cross-correlation
and autocorrelation (Meinhart et al., 2000).
2.2 Synthetic Image Method
Since deconvolution is inherently sensitive to noise, it would be beneficial to eliminate the deconvolution step from the
velocity profile extraction process. This could be done if the autocorrelation were an impulse or delta function. Since the
deconvolution of a function with an impulse is the original function, this would render the deconvolution operation trivial and
unnecessary.
The autocorrelation is related to the particle intensity distributions from a set of PIV recordings, i.e. the shape of particles in an
image pair. So, the most practical way to affect the autocorrelation is to alter the imaged shape of these particles. If the
particle intensity distributions in a set of PIV recordings are reduced to single pixel impulses, the autocorrelation appears as a
single pixel impulse. This desirable autocorrelation trivializes the deconvolution method such that the cross-correlation alone
is equal to the deconvolution of the cross-correlation and the autocorrelation. This is the basis for the synthetic image method.
Experimentally PIV recordings containing uniformly illuminated single pixel particle images can be approximately obtained
by illuminating very small seed particles with a high intensity laser sheet, such that most particles are imaged by a single pixel
by a digital camera. However, this approach can only be an approximation because even very small particles located near the
edge of a pixel would be imaged over two neighboring pixels. Furthermore, in µPIV the particles are already very small, so
making them any smaller could render them invisible to the camera; imaged particle intensity decreases proportionally as the
cube of particle radius. Also, the image of a very small particle is dominated by diffraction optics, thus reducing the physical
particle size will have very little impact on the imaged particle size due to a finite diffraction limited spot size. The typical
point response function associated with microscope objectives used in µPIV is 5 pixels, so the desired particle intensity
distribution cannot easily be obtained in raw experimental images.

2.3 Comparison of Techniques
Three cases were examined to compare and validate the two methods of velocity distribution extraction from cross-correlation
PIV. The cases involve three different velocity profiles that are frequently encountered in µPIV. The first is a uniform flow
(depth of field small compared to flow gradients), the second is a linear shear (near wall region of a channel flow), and the
third is a parabolic channel flow (depth averaged pressure-driven flow in a shallow micro device).
Uniform Flow
The velocity profile of uniform flow is an ideal situation for making accurate PIV measurements. This case also has the
simplest velocity histogram, a delta function at the uniform velocity value. The uniform one dimensional velocity profile
simulated in this case study is given by V
x
= 6.294. Both the deconvolution and the synthetic image methods generate the
expected histograms and are shown in Figure 3. Since the histograms are calculated only at integer pixel values, they both
show a peak at 6 pixels and a lower, but non-zero value at 7 pixels. By taking the average of the histogram values at 6 and 7
pixels, the true value of the uniform displacement can be found. For example in the case of the synthetic image method, V
x,mean
= (6×1+7×0.46)/1.46 = 6.32, which is very near the input value of 6.294.
Linear Shear
Another common flow profile is a linear shear. In microfluidics, this type of flow can be seen in the near wall region of a
parabolic flow profile. Since the displacement probability density function for a linear shear has the simple shape of a top hat,
this type of flow presents a good case to further benchmark the methods of deconvolution and synthetic images. This
simulation case study used the velocity profile given by V
x
= 6.294×Z+1.248 where Z is the position along the axis of the
imaging system and can assume values between 0 and 1. Consequently we expect a top hat with the left edge at 1.248 and the
right edge at 7.552. The results are plotted in Figure 4. The synthetic image method is a slightly better predictor of the
velocity histogram than the deconvolution method by virtue of the steeper gradients at the edges of the distribution. This
behavior is expected because images analyzed by deconvolution contain particle diameter and particle intensity variations, as
well as slight readout noise.
Parabolic or Poiseuille Flow
The final common velocity profile to be considered is parabolic or Poiseuille flow. This type of flow is found in pressure-
driven microchannel devices. Furthermore, it is the profile expected from the LOC experimental device being considered here
with zero voltage applied to the DEP electrodes. This simulation used the velocity profile is given by V
x
= 50.352×(Z-Z
2
), with
Z varying between 0 and 1. Hence, we expect a velocity distribution varying between a minimum of 0 pixels at Z = 0 and Z =
1 and a maximum of 12.558 at Z = ½. The results are plotted in Figure 5. These results clearly show the increased accuracy of
the synthetic image method over the deconvolution method. The synthetic image data very closely agrees with the velocity
histogram. The deconvolution method suffers spurious oscillations but still gives a reasonable approximation of the velocity
histogram. These spurious oscillations were initially attributed to a lack of statistical convergence, so additional image sets
were added, eventually totaling ten thousand sets. The same spurious oscillations were found, so they must be inherent to this
particular case. It is not typical for such oscillations to occur, but as demonstrated, the deconvolution method has definite
limitations.
3 EXPERIMENTAL RESULTS
The experiments presented here are designed to quantify the dielectrophoretic performance of the device. The experiments
used six sets of 800 images each to analyze the effect of dielectrophoresis on particle motion in the test device. These images
are high quality with low readout noise, as can be seen in the example fluorescent image of Figure 6. The top image
demonstrates the many different particle intensity distributions which are typically present in a µPIV image in which the
particles are distributed randomly within the focal plane. The bottom figure shows how, as the result of the DEP force, the
particles migrate to the top of the channel and all have nearly identical images. The top image also shows that when a
significant DEP force exists, the particles are trapped at the electrode locations by the increase in DEP force there. In general,
the observed particle image shape is the convolution of the geometric particle image with the point response function of the
imaging system. The point response function of a microscope is an Airy function when the point being imaged is located at the
focal plane. When the point is displaced from the focal plane, the Airy function becomes a Lommel function (Born and Wolf,
1997). For a standard microscope the diffraction limited spot size is given by
1.22
e
d
NA
λ
= (1)
A numerical aperture (or NA) of 1.00 and an incident light wavelength ? of 540nm results in a diffraction limited spot size of
0.66µm, while the particles used are 0.69µm. Consequently the particle intensity distributions as recorded by the camera are
partly due to the geometric image of the particle and partly due to diffraction effects. Hence, the distance of any particle from
the focal plane can be determined by the size and shape of the diffraction rings.

Two different PIV analyses were performed. Initially a conventional µPIV analysis was performed to obtain an estimate of the
average particle velocity field. Because the goal of the conventional µPIV analysis is not to extract velocity distribution, only
the median velocity is reported. Figure 6 shows vector plots for the experimental cases of 0.5 volts (top) and 4.0 volts
(bottom). A uniform color scale was applied to these figures for representing velocity magnitude, such that velocity changes
between cases can be more easily interpreted.
A second type of PIV analysis was performed in which the velocity distribution within each interrogation was extracted by
either deconvolving the autocorrelation function for the interrogation spot with the cross-correlation function or by using an
image processing technique. This approach reveals more about the particle dynamics because there are considerable gradients
in the depthwise direction in this case. The two cases used in Figure 6 are replotted again in Figure 7 as probability density
functions. The increase in the richness of the information is clearly evident although its interpretation is far from
straightforward.
A preliminary investigation of the results reveals some very interesting particle dynamics in this device. For example, initially
the average particle velocity increases as the voltage increases. This phenomenon is explained by particles being displaced
from the channel bottom into faster areas of the fluid flow. This biases the velocity distribution toward higher velocities,
altering the shape of the cross-correlation peak to favor higher velocities even though the fluid flow is constant. For higher
voltages the effect of particles being hindered by axial field gradients is compounded by particles being forced beyond the high
speed central portion of the flow profile by the DEP force. It can be qualitatively confirmed that particles migrate to the top of
the channel by observing particle shapes in the images from the higher voltage cases, i.e. comparing the many particle shapes
found in Figure 2 (top) which is acquired at 0.5 volts with the single particle shape found in Figure 2 (bottom) which is
acquired at 4.0 volts. It is confirmed that the particles are indeed at the top of the channel by traversing the focal plane
throughout the depth of the device. Furthermore, the rapid fluctuations in the particle velocities inside the device call into
question whether the steady drag assumption, which is used in nearly all DEP particle dynamics equations, is appropriate.
4 CONCLUSIONS
The dynamics of particles traveling through the device described in this paper are very complicated, exhibiting migration
normal to the electrodes as well as trapping behavior in the plane of the electrode. Several novel PIV interrogation techniques
are applied to shed light on the particle dynamics. Further work is needed to assess the accuracy of the steady drag
assumption.
REFERENCES
M. Born, E. Wolf, Principles of optics, Oxford Press, Pergamon, 1997.
E.B. Cummings, “An image processing and optimal nonlinear filtering technique for PIV of microflows”, Experiments in
Fluids, Vol. 29, [Suppl.]:S42-50, 2001.
HB Li, Y Zheng, D Akin, R Bashir, “Characterization and Modeling of a Micro-Fluidic Dielectrophoresis Filter for
Biological Species,” submitted to J. Microelectromechanical Sys. (2004).
C.D. Meinhart, S.T. Wereley, J.G. Santiago, 2000, “A PIV algorithm for estimating time-averaged velocity fields,” J.
Fluids Eng., Vol. 122, 809-814.
M.G. Olsen, R.J. Adrian, 2000, “Out-of-focus effects on particle image visibility and correlation in microscopic particle
image velocimetry,” Experiments in Fluids, [suppl.], S166-S174.
S.T. Wereley, L. Gui, C.D. Meinhart, “Advanced Algorithms for Microscale Velocimetry,” AIAA J., Vol. 40, No. 6, 1047-
1055, 2002.

Figure 1. (a) Schematic view of experimental apparatus and (b) photo of apparatus.

Citations
More filters
Journal ArticleDOI
TL;DR: In this paper, a detailed theoretical analysis was performed using window correlation, singe-pixel ensemble-correlation and particle tracking evaluation methods, and different findings were validated experimentally for microscopic, long-range microscopic and large field imaging conditions.
Abstract: The reliable measurement of mean flow properties near walls and interfaces between different fluids or fluid and gas phases is a very important task, as well as a challenging problem, in many fields of science and technology. Due to the decreasing concentration of tracer particles and the strong flow gradients, these velocity measurements are usually biased. To investigate the reason and the effect of the bias errors systematically, a detailed theoretical analysis was performed using window-correlation, singe-pixel ensemble-correlation and particle tracking evaluation methods. The different findings were validated experimentally for microscopic, long-range microscopic and large field imaging conditions. It is shown that for constant flow gradients and homogeneous particle image density, the bias errors are usually averaged out. This legitimates the use of these techniques far away from walls or interfaces. However, for inhomogeneous seeding and/or nonconstant flow gradients, only PTV image analysis techniques give reliable results. This implies that for wall distances below half an interrogation window dimension, the singe-pixel ensemble-correlation or PTV evaluation should always be applied. For distances smaller than the particle image diameter, only PTV yields reliable results.

239 citations

Journal ArticleDOI
TL;DR: In this paper, a generalization of the description of the displacement-correlation peak in particle image velocimetry (PIV) to include the effects due to local velocity gradients at the scale of the interrogation domain is presented.
Abstract: This paper presents a generalization of the description of the displacement-correlation peak in particle image velocimetry (PIV) to include the effects due to local velocity gradients at the scale of the interrogation domain. A general expression is derived that describes the amplitude, location and width of the displacement-correlation peak in the presence of local velocity gradients. Simplified expressions are obtained for the peak centroid and peak width for simple non-uniform motions. The results confirm that local gradients can be ignored provided that the variation of the displacement within the interrogation domain does not exceed the (mean) particle-image diameter. An additional bias occurs for a spatially accelerating or decelerating fluid, which implies an artificial "particle inertia" even when the particles can be considered as ideal tracers.

170 citations


Cites background from "Particle Dynamics in a Dielectropho..."

  • ...This particular shape of the displacement-correlation peak is reported by Wereley and Whitacre (2006)....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the authors used particle image velocimetry (PIV) to estimate the centerline velocity in thin-gap devices subject to a large depth of focus and Poiseuille flow conditions across the gap.
Abstract: Quantitative in-plane velocity measurement by means of particle image velocimetry (PIV) within thin-gap devices subject to a large depth of focus and Poiseuille flow conditions across the gap is investigated. The primary obstacles to a reliable quantitative measurement are due to the effects of the inherent wall-normal velocity gradient and the inertial migration of particles in the wall-normal direction. Specifically, in the simplest case of no particle migration, the PIV correlation peak is broadened due to velocity variations within the interrogation region, and the result is expected to predict the maximum centerline velocity. The current work demonstrates, however, that there is an inevitable underestimation of the peak velocity due to the convolution of the fluid displacement probability distribution function (PDF) by the particle image size that introduces a biased error typically up to 33 % of the centerline velocity for all but the smallest particle images and largest displacements. Due to the low signal-to-noise ratio caused by the velocity gradient, the probability of a valid estimate is significantly impaired, demanding an unrealistically high concentration of tracer particles. In addition, inertial particle migration within the channel introduces a selective sampling of the velocity PDF, causing a second correlation peak to emerge as the particles rapidly move away from the wall, making a reliable measurement troublesome. In later times, the particles reach their equilibrium position and hence sample only a single velocity value, presenting conditions similar to traditional PIV interrogations, with the correlation function reduced to a single symmetric peak. A practical procedure is proposed to make PIV quantitative by manipulating the particles to their equilibrium position prior to performing measurements. A demonstration of a reliable PIV measurement under appropriate working conditions is discussed for diffusive Rayleigh–Benard convection in a Hele-Shaw cell.

11 citations

DissertationDOI
01 Jan 2014
TL;DR: In this paper, a particle image velocimetry (PIV) measurement was proposed to make quantitative velocity measurement in microfluidic systems and narrow devices by manipulating the particles to their equilibrium position through inertial induced migration.
Abstract: An experimental analogue was developed to investigate instability propagation of a multicomponent fluid system in porous media. This type of flow pattern has been observed in a broad range of applications from oil enhanced recovery to geological storage of byproduct materials such as CO 2. The main focus of this study is on the engineering instrumentation and implementation of experimental measurement techniques in microfluidic systems, more specifically in a thin-gap device that is used as a model for a saturated porous medium. Initially, quantitative in-plane velocity measurement by means of particle image velocimetry (PIV) within thin gap devices subject to a large depth-of-focus and Poiseuille flow conditions is studied extensively. The temporal velocity measurement is then coupled with a simultaneous concentration measurement by means of LED induced fluorescence (LIF). The primary obstacles to a reliable quantitative PIV measurement are due to the effects of the inherent wall-normal velocity gradient and the inertial migration of particles in the wall-normal direction. After quantification of both effects, a novel measurement technique is proposed to make quantitative velocity measurement in microfluidic systems and narrow devices by manipulating the particles to their equilibrium position through inertial induced migration. This single camera technique is significantly simpler and cheaper to apply comparing to the existing multi-camera systems as well as micro-PIV implementations, which are restricted to a small field-of-view. A demonstration of a reliable PIV measurement under appropriate parameter design is then discussed for diffusive Rayleigh-Bénard convection in a Hele Shaw cell. For concentration measurements, the main difficulty of making LIF quantitative is its highly sensitive response to the experimental settings due to extreme sensitivity of the fluorescence to the environment factors and illumination conditions. A calibration procedure is required prior to performing any meaningful quantitative measurements. Additionally, the effect of photobleaching can be significant, which impairs the measurement as will be discussed later in further detail. Eventually after calibration and correction methods for velocity and concentration measurement techniques, a simultaneous PIV/LIF is performed to quantify the behavior of instability fingers in the developed experimental system.

3 citations

03 Jul 2013
TL;DR: In this article, the authors used particle image velocimetry (PIV) to estimate the centerline velocity in thin-gap devices subject to a large depth of focus and Poiseuille flow conditions across the gap.
Abstract: Quantitative in-plane velocity measurement by means of particle image velocimetry (PIV) within thin-gap devices subject to a large depth of focus and Poiseuille flow conditions across the gap is investigated. The primary obstacles to a reliable quantitative measurement are due to the effects of the inherent wall-normal velocity gradient and the inertial migration of particles in the wall-normal direction. Specifically, in the simplest case of no particle migration, the PIV correlation peak is broadened due to velocity variations within the interrogation region, and the result is expected to predict the maximum centerline velocity. The current work demonstrates, however, that there is an inevitable underestimation of the peak velocity due to the convolution of the fluid displacement probability distribution function (PDF) by the particle image size that introduces a biased error typically up to 33 % of the centerline velocity for all but the smallest particle images and largest displacements. Due to the low signal-to-noise ratio caused by the velocity gradient, the probability of a valid estimate is significantly impaired, demanding an unrealistically high concentration of tracer particles. In addition, inertial particle migration within the channel introduces a selective sampling of the velocity PDF, causing a second correlation peak to emerge as the particles rapidly move away from the wall, making a reliable measurement troublesome. In later times, the particles reach their equilibrium position and hence sample only a single velocity value, presenting conditions similar to traditional PIV interrogations, with the correlation function reduced to a single symmetric peak. A practical procedure is proposed to make PIV quantitative by manipulating the particles to their equilibrium position prior to performing measurements. A demonstration of a reliable PIV measurement under appropriate working conditions is discussed for diffusive Rayleigh–Benard convection in a Hele-Shaw cell.

3 citations

References
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"Particle Dynamics in a Dielectropho..." refers background in this paper

  • ...When the point is displaced from the focal plane, the Airy function becomes a Lommel function (Born and Wolf, 1997)....

    [...]

Journal ArticleDOI
TL;DR: In this article, the frequency dependency and magnitude of electrothermally induced fluid flow are discussed for low frequencies (up to 500 kHz) and the effects of Brownian motion, diffusion and buoyancy force are discussed in the context of the controlled manipulation of sub-micrometre particles.
Abstract: Ac electrokinetics is concerned with the study of the movement and behaviour of particles in suspension when they are subjected to ac electrical fields. The development of new microfabricated electrode structures has meant that particles down to the size of macromolecules have been manipulated, but on this scale forces other than electrokinetic affect particles behaviour. The high electrical fields, which are required to produce sufficient force to move a particle, result in heat dissipation in the medium. This in turn produces thermal gradients, which may give rise to fluid motion through buoyancy, and electrothermal forces. In this paper, the frequency dependency and magnitude of electrothermally induced fluid flow are discussed. A new type of fluid flow is identified for low frequencies (up to 500 kHz). Our preliminary observations indicate that it has its origin in the action of a tangential electrical field on the diffuse double layer of the microfabricated electrodes. The effects of Brownian motion, diffusion and the buoyancy force are discussed in the context of the controlled manipulation of sub-micrometre particles. The orders of magnitude of the various forces experienced by a sub-micrometre latex particle in a model electrode structure are calculated. The results are compared with experiment and the relative influence of each type of force on the overall behaviour of particles is described.

1,184 citations


"Particle Dynamics in a Dielectropho..." refers background in this paper

  • ...This force can be used to manipulate particles suspended in a fluid [5, 7, 11]....

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  • ...Differences in permittivity between two materials result in a net force which depends on the gradient of the electric field [11]....

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Journal ArticleDOI
TL;DR: The application of dielectrophoresis to particle discrimination, separation, and fractionation is reviewed, some advantages and disadvantages of currently available approaches are considered, and some caveats are noted.
Abstract: The application of dielectrophoresis to particle discrimination, separation, and fractionation is reviewed, some advantages and disadvantages of currently available approaches are considered, and some caveats are noted.

811 citations


"Particle Dynamics in a Dielectropho..." refers background in this paper

  • ...Gascoyne & Vykoukal [4] presents a review of DEP with emphasis on manipulation of bioparticles....

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  • ...However, for these small particles, DEP force may be both augmented and dominated by the particle’s electrical double layer, particularly for low conductivity solutions [4]....

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Journal ArticleDOI
TL;DR: In this paper, a PIV algorithm is presented for estimating time-averaged or phaseaveraged velocity fields, which can be applied to situations where signal strength is not sufficient for standard cross correlation techniques, such as a low number of particle images in an interrogation spot, or poor image quality.
Abstract: A PIV algorithm is presented for estimating time-averaged or phase-averaged velocity fields. The algorithm can be applied to situations where signal strength is not sufficient for standard cross correlation techniques, such as a low number of particle images in an interrogation spot, or poor image quality. The algorithm can also be used to increase the spatial resolution of measurements by allowing smaller interrogation spots than those required for standard cross correlation techniques. The quality of the velocity measurements can be dramatically increased by averaging a series of instantaneous corelation functions, before determining the location of the signal peak, as opposed to the commonly used technique of estimating instantaneous velocity fields first and then averaging the velocity fields. The algorithm is applied to a 30 μm×300 μm microchannel flow

516 citations


"Particle Dynamics in a Dielectropho..." refers background in this paper

  • ...The information density can be increased by correlation averaging both the cross-correlation and autocorrelation [8]....

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Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "Particle dynamics in a dielectrophoretic microdevice" ?

This method involves extracting the locations of particles in a PIV recording and generating simulated particle images with a prescribed single pixel particle intensity distribution. The dielectrophoretic forces are seen to cause the particle to migrate toward the top of the device and to trap them near the leading edge of each electrode.