Fabrication and Design Considerations for
Microfluidics-based Tactile Sensors for
Prosthetic Hand
Shehreen Dheda, Department of Biomedical Engineering, University of
California, Irvine
Jeffrey S. Fisher, Department of Biomedical Engineering, University of
California, Irvine
Abraham. P. Lee, PhD, Department of Biomedical Engineering, University of
California, Irvine
August 2006
Abstract: Many variations of the prosthetic hand exist today. However, only a few very simply
exhibit the facility of tactile sensing. In the skin of the human hand, there are four main types of
mechanoreceptors. Each plays an important role in detecting the different types of stimuli that
are present in everyday life. The objective here is to develop the first microfluidics-based
synthetic tactile sensor array to be embedded within the artificial skin of a prosthetic hand. Two
synthetic sensor types are designed such that they mimic two of the biological tactile sensors.
The key components of the project considered here are the fabrication process and the design.
The fabrication process includes selectively filling the array with fluid and preventing
permeation of fluid out of the device, while the design includes computer modeling of the
transduction of mechanical stimuli to measurable volume changes within the microfluidic sensor.
Three techniques were investigated for filling the devices. It was found that all three of the
techniques have drawbacks, though modifications are being developed and tested for one of the
methods. Some of the filled devices were kept for monitoring the pervaporation of fluid from the
devices. Over a period of weeks it was observed that pervaporation had taken place from the
devices and its rate was determined to be approximately constant. Lastly, a simulation of part of
the device was developed and tested in order to quantify the transduction of pressure applied to
changes in volume of the sensor.
Keywords: filling, human prosthetics, mechanoreceptors, microfluidics, pervaporation,
poly(dimethylsiloxane), synthetic tactile sensor
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1. Project Background
At present, prosthetic hands do not fully exhibit the facility of touch, or tactile, sensation.
Hence, users do not have sensory interaction with the hand. Some prosthetic hands have a single
sensor on the thumb and a sensor to regulate grasp and prevent damage of the hand (Biddiss and
Chau, 2006). However, compared to the natural tactile sensory system of the human hand, such
features are unable to allow for the same complexity of function or interaction with the dynamic
environment an individual faces everyday. As a result, many research groups are working to
develop prosthetic hands with greater functions and capabilities, and others are developing tactile
sensors and sensor arrays that are able to be integrated to prosthetic limbs. Examples of such
sensors are those based on electroactive polymers (Biddiss and Chau, 2006), strain gauges (Fan
et al., 2004), microinductors (Futai et al., 2003), piezoelectric polymeric transducers (Howe and
Cutkosky, 1993), and MEMS (microelectromechanical system) (Kim et al., 2005). The objective
of this project is to develop that first microfluidics-based tactile sensor array that is capable of
perceiving different components of touch sensation. This array is to be made within the layers of
polymers that will act as the artificial skin of the prosthetic hand.
1.1 Biological Mechanoreceptors
There are four main types of biological mechanoreceptors found in the glabrous skin of
the human hand. Each of these types integrate to produce what is known as the sense of touch, or
tactile sensation. Merkel cells encapsulate the ends of slowly adapting type 1 (SA-1) afferents
and are important in the recognition of edges, curved surfaces and corners. Meissner corpuscles
surround the ends of rapidly adapting (RA) afferents, which are crucial for the detection of
dynamic forces on the skin. Skin stretch is sensed by slowly adapting type 2 (SA-2) afferents that
terminate in Ruffini corpuscles and high frequency stimuli are perceived by Pacinian afferents
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that end in Pacinian corpuscles. (Johnson, 2001) Of the four types, Merkel cell-SA-1 and
Meissner corpuscles-RA mechanoreceptors will be considered in this paper because of their
specific roles in tactile sensation. Figure 1 is section of skin showing the four mechanoreceptor
types and their location in human skin.
Fig. 1: A schematic representation of human skin. The four mechanoreceptor types, Merkel disks
and Meissner, Pacinian and Ruffini corpuscles, are shown. The skin also has receptors for
temperature sensing and hair receptor for flow and touch sensing. (Engel et al., 2005)
SA-1 and RA type sensors are the most abundant mechanoreceptors in the skin and are
found near the epidermal layer. Individual SA-1 afferents reside within a Merkel cell. Each
afferent has a spatial resolution of about 0.5 mm and receptive field diameters of about 2-3 mm.
SA-1 afferents are continuously stimulated by sustained indentation, and the stimulation is
characterized by a slowly adapted discharge, which is linear to the depth of indentation. SA-1
afferents, in general, perceive form and texture information. RA afferents, on the other hand,
reside within Meissner corpuscles in groups of six to eights per corpuscles. The receptive field of
RA afferents is about a 3-5 mm diameter, and the spatial resolution is less than that of SA-1
afferents because of the uniformity of the former’s response to stimuli. These afferents only
respond to dynamic skin deformation and are insensitive to static forces because, it is believed,
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of the fluid filled corpuscle surrounding the afferents. Overall, SA-1 sensors have lesser
sensitivity, higher spatial resolution and a broad dynamic range, while RA sensors have greater
sensitivity, poorer resolution and a narrow dynamic range. (Johnson, 2001)
1.2 Synthetic Tactile Sensor Design
The design of the two sensor types are shown in Figure 2. The sensor features are molded
into poly(dimethylsiloxane) PDMS, while electrode arrays are laid onto either glass, for macroscale investigation of the devices, or polyimide, for the actual device. In both sensors, as force is
applied to the reservoir (the hemisphere) of the sensors, fluid flows out into the channels and
interacts with the specific electrode array.
Fig. 2: SA1 and RA type synthetic tactile sensors. The sensor reservoirs and channels are molded
into the top poly(dimethylsiloxane) layer and the electrodes are patterned onto the bottom
polyimide layer.
The SA1 sensor, on the left of Figure 2, is based on the simple concept of capacitance.
The basic formula of capacitance for a parallel plate capacitor is C   0 A d , where C is the
capacitance,  is the dielectric constant,  0 is the permittivity of free space, A is the area of the
plate, and d is the distance between the two plates (Serway and Beichner, 803-819). As fluid
flows from the reservoir when force is applied, the dielectric constant changes between the
electrodes, resulting in a change in capacitance. Hence, if an electrode array is part of a circuit
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and a current is flowed through it, the change in capacitance will change the current within the
circuit.
The RA sensor, on the right of Figure 2, is based on a more complicated concept of
capacitance in relation to microfluidic flow. At micro-level scales, when a current is applied
across two electrodes with an electrolyte flowing between them, their capacitance is based
largely on the capacitance of the flowing electrolyte as compared to that of the electrodes in the
absence of the electrolyte. (Collins and Lee, 2004) This allows for the detection of fluid flow
between the electrodes. A change in the current in the circuit occurs only at the instant that the
pressure on the reservoir changes because the fluid is caused to flow through the channels—that
is between the electrodes. Therefore, the synthetic SA1 sensor type is continuously stimulated
during indentation of its reservoir, while the synthetic RA sensor type is only sensitive to
dynamic changes in its reservoir indentation.
Since, the devices are microfluidic devices, that is based on fluids and fluid flows at the
micrometer level, the filling of the devices, the pervaporation, or loss, of fluid out of the devices
and the relationship between the applied pressure and the volume change at such small size
scales are some of the important considerations in this project. For example, it is desired that
only the reservoirs be filled with fluid, therefore a method must be sought that will allow for
selective filling of the devices. In addition, it is also known that water has the tendency to
pervaporate into PDMS, despite the hydrophobic nature of the polymer, (Watson and Baron,
1996) and hence it is important to determine the rate of loss of fluid into the PDMS surrounding
the reservoir so to accommodate for this phenomenon. Also, it is important to determine the
relationship between pressure applied to the sensor reservoir and the resultant output of fluid,
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which, in turn, determines other parameters of the sensor design. Due to these reasons, these
three issues were investigated as follows.
2. Materials and Methods
The devices were made from poly(dimethylsiloxane) (PDMS) (Sylgard 184, Dow
Corning) and glass microscope slides. PDMS is fabricated by combining a 10:1 ratio of PDMS
prepolymer and its curing agent. The mixture is stirred vigorously, degassed in a vacuum
chamber, poured onto a silicon master and cured in an oven at 65°C for 2-3 hrs. The silicon
master is patterned with features using silicon and SU-8 molds. For the purpose of the
investigation the reservoir of the sensors had a radius of 2.5mm and the channels had dimensions
of 100 µm. Once the PDMS is cured, a portion of the PDMS mold with the sensor design is cut
and peeled off the silicon master. Both the PDMS piece and a microscope slide were placed in a
plasma cleaner (Model PDC-001, Harrick Scientific Corporation) and were treated, in vacuum,
at 29.6W for 2 minutes. They were then removed from the cleaner and bonded together such that
the side of the PDMS layer with the embedded sensor features was in contact with the slide. The
devices were left for about 5 minutes to allow for complete bonding. Plasma treatment allows for
an irreversible bond to be formed between the two layers.
2.1 Filling of the Devices
Three filling techniques were investigated as follows. Each method was attempted a
number of times. The first method is by syringe. The PDMS membrane is punctured at an angle
to the reservoir and the needle is guided into the reservoir. Once the tip of the needle is inside the
reservoir, fluid is slowly injected into it. When the reservoir is completely filled, the needle is
removed. The second method attempted was using a syringe pump (PicoPlus, Harvard
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Apparatus) to fill the reservoir. The devices for the technique were modified such that there were
three channels leading out from the reservoir: an inlet channel, an outlet channel, and the sensor
channel. The modified device design is shown in Figure 3. The free ends of the inlet channel and
the outlet channel on the PDMS layer were punched using a 16 gauge needle before plasma
treatment. A syringe is filled with the fluid and tubing is slid onto the needle of the syringe. The
other end of the tubing is pushed into the inlet channel, making sure that there is some space
between the tubing end and the slide so that the fluid will be able to pass through. The syringe is
placed in the pump, which is set to pumping rate of 30, 40 or 50 µl/s. Fluid is gradually injected
into the reservoir as air exits through the outlet channel.
Fig. 3: The modified sensor design. The inlet channel, outlet channel, and sensor channels are
labeled. The two holes on end of the inlet and of the outlet are necessary for both the pump
method and the COT method (described below).
The channel outgas technique (COT) was the third method that had been explored. This
technique is based on submerging the PDMS device in a beaker filled with the required solution
and then exposing the solution to a lower ambient pressure for a certain period of time. The
modified sensor devices, as shown in Figure 3, were also filled using this method. The device is
placed at the bottom of the beaker such that there are no air bubbles trapped underneath. The
solution must have a height of at least a few millimeters above the device. The beaker is then
placed in a vacuum, and the air pressure gradually lowered to about 200-600 mtorr over a period
of a one minute during which air within the device starts to leave through the inlet and outlet.
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The pressure is then returned to atmospheric pressure after 10 min, and the device is allowed to
remain in the solution until it is completely filled, which takes usually less than 1 min. The
device is then removed from the solution, rinsed, and dried. (Monahan et al., 2001)
2.2 Permeation of Fluid from the Device
Reservoirs of the devices were gradually filled with dilute KCL solution of 0.4 mM using
a syringe. In each device, when the reservoir was nearly filled, the fluid no longer entered and
one or more air bubbles remained. The channels in each of the devices remained dry. A layer of
uncured PDMS was smoothed onto the top surface of the devices in order to cover the points
where the syringe punctured the devices. Pictures of the air bubbles present were taken using an
inverted microscope (Nikon Eclipse TE 2000-S) and a color camera (Spot Insight QE 4.2,
Diagnostic Instruments, Inc.). The devices were kept at room temperature for a number of weeks.
This also allowed for the top PDMS layer of the devices to cure, which takes about 24 hours.
Pictures of the bubbles were taken daily under the inverted microscope at 4x magnification,
using the color camera, for a period of weeks and were analyzed using Adobe Illustrator 10 ©.
2.3 Computer Simulation of Device Reservoirs
Computer simulations of the sensor reservoirs were produced, simulated, run, and
processed using Computational Fluid Dynamic-Research Corporation (CFD-RC) software. CFDGEOM was used to construct a model of a reservoir of radius 0.9 mm. The model was then
simulated and run using CFD-ACE+ and post-processing of the results was performed in CFDVIEW. The model was made such that a force is applied to the top of the reservoir, while fluid is
expelled from a small outlet at the bottom of the reservoir. The print output obtained from ACE+
was set to include a mass flux summary for the outlet. The pressure applied to the top of the
reservoir was varied over a pressure range of 0-60000 Nm-2.
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3. Results and Discussion
3.1 Filling of the Devices
Three filling techniques were attempted in order to fill the devices selectively such that
only the reservoir of the sensor was filled, while the channel remained dry. In almost all cases of
the devices filled by syringe, except that of two devices, an air bubble became trapped inside the
reservoir and filling would no longer be possible. One reason for this may be that at one point the
fluid would start to move out of the reservoir via the path created by the needle in the PDMS
layer. As the water filled up the reservoir, the increased air pressure prevented the flow of more
fluid into the reservoir and the fluid was pushed out through this more favorable path, which led
to the lower pressure of the atmosphere.
For the second method, it is was observed that, as the reservoir filled with fluid from the
inlet channel, air continued to escape out through the outlet channel while fluid would not enter
into the sensor channel, possibly due to surface tension and air pressure interactions at the mouth
of the channel. However, once fluid began to enter into the sensor channel, further entrance of
the fluid into the reservoir resulted in the filling of the sensor channels, while either
simultaneously filling the remaining volume of the reservoir or not continuing to fill it at all.
Another observation was that if the fluid came into contact with the outlet channel while air was
still present in the reservoir, the fluid would flow into the channel. As more fluid flowed into the
reservoir, air would remain in the reservoir and the fluid would continue to flow out of the device
through the outlet channel. Therefore, this method also resulted in the formation of an air bubble
in the device reservoir as shown in Figure 4.
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Fig. 4: An air bubble that forms during filling with a pump. Fluid flows from the inlet channel
(left) into the reservoir. Once the fluid comes into contact with the outlet channel, fluid flows
into the outlet channel, most of the time leaving an air bubble in the reservoir. (Sensor channel is
not shown.)
The COT method proved to be successful for all trials. Refilling the device was also
possible with this method. In the case of both unfilled and partially unfilled devices, the time
needed in the solution after the air pressure was brought back to atmospheric pressure varied. It
was found that by using a stronger vacuum, the time needed for the entire device, unfilled or
partially filled, to fill is considerably reduced. For an empty device the time for complete filling,
after removing from vacuum, was less than one minute. However, for a device with partial
filling, the time needed for filling to complete was greater than one hour. The main drawback of
this method is that the entire device including the sensor channel becomes filled. Modifications
are being made to this method to allow for selective filling.
3.2 Pervaporation of Fluid from the Device
Pictures obtained also depict the increase in size of an air bubble in a device. Three of
those taken during the first two weeks are shown in Figure 5. The change in size of the bubble is
clearly visible indicating the loss of fluid from the device.
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Fig. 5: Air bubbles on days 1, 8 and 16, respectively. The increase in size of the air bubble is
clearly observable.
Data obtained from the analysis of the all the images is represented by the plot in Figure
6. As time passed, it was observed that the volume of the air bubble, assuming it to be a perfect
sphere, increases, indicated by the positive slope of the graph. With further calculations and unit
conversions, it was determined that the percent fluid loss per day was approximately a constant
1.6  0.1 % at a rate of about 0.0005 ml/day, which is represented by the linear slope of the
graph, and within 64 days, if the rate remains constant over the entire period, the reservoir would
lose all the fluid it initially contained. Although, this plot represents the loss of fluid in one
device, other devices that were studied gave similar results. Overall, the investigation of
pervaporation from a larger number of devices will lead to a more accurate analysis of this
phenomenon.
Fig. 6: Air bubble volume in ml vs. time in days. There is an increase in the size of the air bubble
in the reservoir, indicating pervaporation from the reservoir.
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3.3 Computer Simulation of Device Reservoirs
Data obtained from the output summary for mass flux was in units of kg/s/rad. Although
it is being determined how to convert this and other output values obtained, an evaluation of the
data has been made and is presented in Figure 7. The magnitude of the percent decrease in
volume of water in the reservoir is plotted against the pressure applied to the top of the reservoir.
It is seen that the relationship between these two quantities is linear within this range indicating
that there will be a constant increase in fluid entering the sensor channel as more pressure is
applied.
Fig. 7: Magnitude of the percent decrease in volume of fluid in the reservoir vs. applied pressure
in N/m2. This change indicates the flow of fluid into the channels as pressure is applied to the
reservoir.
Using VIEW, many different aspects of the device were imaged such as the fluid flow
inside of the reservoir, the distribution of magnitudes of stress components within the PDMS
layer, the distribution of the magnitudes of displacement of the PDMS layer, etc. For example,
the flow of fluid inside the reservoir and the xx-stress component distribution within the PDMS
layer were visualized for the application of an applied pressure of 40000 N/m2, which caused
deformation of the reservoir, and therefore stress developed within the reservoir and fluid flowed
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from it. Figure 8a shows the reservoir before deformation, while Figure 8b shows that the xxstress component within the PDMS layer above the reservoir due to the pressure. Lastly, Figure
8c shows fluid flow out of the reservoir due to the pressure.
(a)
(b)
(c)
Fig. 8: Stress distribution and fluid flow for an applied pressure of 40000 N/m2. (a) No applied
pressure. (b) Magnitude of xx-stress component throughout PDMA layer above reservoir. (c)
Fluid flow within and out of reservoir. In (b) and (c), the deformation due to the applied pressure
is visible.
4. Conclusion
Overall, of the three filling techniques, the channel outgas technique proved to be the
most effective in filling the devices without the formation of any air bubbles. However, all parts
of the device including the sensor channel became filled. Modifications to this method are
necessary to enable selective filling of the devices, though this may increase the complexity of
the filling technique. New methods of filling that may be less complicated, while at the same
time allow for selective filling and the absence of air bubbles, can also be explored. Loss of fluid
from the devices is another concern which has been investigated. Since, pervaporation is found
to occur over a period of time, it is essential that preventative measures be taken to ensure a
longer life of the devices. One measure, for example, could be laying down a fluid impermeable
layer above the PDMS layer. Lastly, computer simulations allowed for the determination of
quantities and their relationships which would have been difficult to perform practically.
Optimizing simulation parameters and using better geometries to design models will allow for
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more accurate analyses to be made, and will eventually aid in simulating the complete sensor
device.
5. Acknowledgements
I would like to express my gratitude to the National Science Foundation (NSF) for
funding the IM-SURE program. I would also like to thank Said Shokair, Edward Olano, Sarah
Martin, and the rest of the UROP team for organizing and coordinating the IM-SURE program.
Lastly, I would like to thank Dr. Abe Lee, Jeff Fisher, Tim Tseng, and other members of the Lee
Lab for their help, guidance and encouragement during the course of this research.
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