Journal Pre-proof
A passive, biocompatible microfluidic flow sensor to assess flows in a
cerebral spinal fluid shunt
Ariane Garrett (Investigation) (Formal analysis) (Validation), Garrett
J. Soler (Investigation) (Formal analysis) (Validation), Michael L.
Diluna (Conceptualization) (Methodology), Ryan A. Grant
(Conceptualization) (Methodology) (Funding acquisition), Hitten P.
Zaveri (Conceptualization) (Methodology) (Validation) (Funding
acquisition), Kazunori Hoshino (Conceptualization) (Methodology)
(Formal analysis) (Investigation) (Validation) (Resources) (Funding
acquisition)
PII:
S0924-4247(19)32055-2
DOI:
https://doi.org/10.1016/j.sna.2020.112110
Reference:
SNA 112110
To appear in:
Sensors and Actuators: A. Physical
Received Date:
9 November 2019
Revised Date:
27 March 2020
Accepted Date:
25 May 2020
Please cite this article as: { doi: https://doi.org/
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© 2020 Published by Elsevier.
Title: A Passive, Biocompatible Microfluidic Flow Sensor to Assess Flows in a Cerebral Spinal
Fluid Shunt
Authors: Ariane Garrett1, Garrett J. Soler1, Michael L. Diluna2, Ryan A. Grant3, Hitten P. Zaveri4, and
Kazunori Hoshino1
Corresponding Author: Kazunori Hoshino
Present/Permanent Address:
1Department
of Biomedical Engineering, University of Connecticut, Storrs, CT
of Neurosurgery, Yale University School of Medicine, New Haven, CT
3Geisinger Medical Center, Danville, PA 17822
4Department of Neurology, Yale University School of Medicine, New Haven, CT
2Department
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Graphical abstract
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A polydimethylsiloxane (PDMS) microfluidic flow sensor that can measure flow rates of between
20- 120 ml/hr has been developed for use in a cerebral spinal fluid shunt.
The flow sensor integrates a fully passive, implantable optical system that interacts with light
transmitted from outside the body and reflects it back to the skin surface, so that the flow in the
shunt results in a detectable deflection of a light spot.
All the materials used for the device are biocompatible materials suitable for this application.
The ability of the sensor to measure flow was determined by evaluating the relationship between
the deflection of the light spot and linearly-increasing flow rates. In addition, the stability of the
sensor was measured by continuously pulsing fluid over the sensor for a duration of two weeks.
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Highlights
Abstract
Hydrocephalus is a disease in which a buildup of cerebral spinal fluid in the ventricles of the brain can
cause brain trauma and death if untreated. The current standard of treatment is to insert a shunt to drain
the fluid from the ventricles in the brain to the abdomen. A shortcoming of this approach is that the shunt
may get clogged, and this failure is not easily detected. We have developed a novel microfluidic sensor
for application in a cerebrospinal fluid shunt. The sensor system is totally passive and no implanted
electronics are required. The sensor itself is a bending based cantilever made from biocompatible
polydimethylsiloxane (PDMS) and is capable of measuring flow rates from 20 ml/hr to 120 ml/hr, which is
the indicated range for the flow rate of cerebral spinal fluid. The sensor is paired with an optical detection
system that uses a small light spot to read the changes in sensor position due to flow. The light is input
from outside the brain into the implanted optical system and the output light is measured by a camera
external to the body. The sensor stability was verified by running cerebral spinal fluid over the cantilever
continuously for two weeks. The ability of the sensor to measure pulsed flow and linearly increasing flow
rates was also verified. All of the materials used for the device are biocompatible materials and are
amenable for manufacturing and animal tests and clinical studies.
Keywords: Hydrocephalus, cantilever sensor, flow sensor, cerebral spinal fluid shunt, implantable sensor
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1. Introduction
Hydrocephalus is a disease afflicting around 0.4 to 0.6% of the population [1]. Those affected
have a buildup of cerebral spinal fluid (CSF) in the intracranial ventricular system. CSF is essential to
regulate neuronal function and maintain homeostasis within the brain’s interstitial fluid [2]. It is primarily
secreted from the choroid plexus, and brain tissue is also responsible for some secretion [3].
Hydrocephalus is typically caused from blockage in the ventricular system, an overproduction of CSF, or
an under-absorption of CSF [4]. The buildup of this fluid increases intracranial pressure, resulting in pain,
brain damage, and even death if untreated [2]. The current standard of treatment is a cerebral spinal fluid
shunt inserted into the brain to drain fluid to the heart, lungs, or abdomen [2]. Typical shunt designs
consist of two catheters and a one-way valve. The first catheter is implanted into the brain ventricle and
connected to the main valve, which then releases CSF to the second catheter. This catheter drains the
CSF to the absorption site, typically the peritoneum or abdomen [5]. The valves are implanted directly
below the scalp. Most valves operate through changes in differential pressure between the proximal and
distal catheters. Typically, flow through a shunt can range from .6ml/hr to 116 ml/hr. If the elastic reservoir
of the shunt valve is being pumped, flow can reach up to 2000 ml/hr [5] Approximately 65,000 shunts are
implanted each year, and they are intended to be lifelong implants [2]. However, these implants have a
40% malfunction rate within the first two years and a 98% malfunction rate at 10 years, indicating serious
problems with shunt designs [6,7,8,9]. In addition, there is no direct, non-invasive way to assess if a shunt
is functioning correctly. Standard practice is to take a head CT, MRI, or X-ray to rule out breakages, or
perform an invasive shunt tap to measure pressure and qualitatively measure flow [9,10]. However, less
than 50% of patients who undergo this work up will actually have a malfunctioning shunt [6]. Clearly, there
is a great need for means of evaluating flow through a shunt quickly and non-invasively.
There are no commercially available means of directly measuring flow through a shunt. Madson
et al. reported a device, “Shunt Check”, which measures thermal change through the shunt and correlates
it to flow in the distal catheter. However, patients must cool their skin with ice for one minute prior to
measuring flow, which can be uncomfortable [11]. Bork et al. developed a CMOS thermal anemometer
based sensor. Their sensor incorporates a heat source and a temperature sensor to measure the heat
flux induced by the fluid to find the flow rate. This device has not been developed into a clinical device,
possibly reflecting the difficulty in integrating these electrical sensors and components into a
biocompatible, implantable system [12].
Here a unique novel cerebral spinal fluid flow sensor is proposed that will eliminate uncertainty
about device functionality by allowing physicians to measure the flow rate and pressure through the
device quickly and non-invasively. In addition, the sensor is biocompatible and no implanted electronics
are required. The device has the potential capability to reduce cost and time for both physicians and
patients, and reduce the uncertainty patients experience about the function of their shunt.
2. Design
2.1 Sensing Principles
Figure 1(a) illustrates the concept of our system. Here, a typical commercially available shunt has been
labeled as “shunt valve”. The flow sensor has been designed to be a separate component that is easily
attached directly below the shunt valve and along its flow path. Figure 1(b) illustrates the working principle
of the flow sensor. A light is input onto the patient’s skin directly above the implanted optical system which
is several millimeters below the surface of the skin. The optical system focuses the light onto the
membrane flow sensor. The membrane flow sensor has a reflective gold tip, which reflects the focused
light spot back to the surface of the skin. The flow sensor’s tip reflects the light at varying angles as the
flow sensor deviates from its equilibrium position. Therefore, the output light spot is deflected on the
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surface of the skin corresponding to the flow over the sensor. The reflection from the sensor is monitored
by an external camera and indicates the flow rate. It is advantageous if the function of an implantable
device can be monitored passively without the need to implant additional active electrical components.
The easy integration of the device with commercially available shunts is also an advantage of this design.
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Figure 1: (a) Concept illustration of the flow sensor device. The sensing device is designed to be
implanted in the flow path of the cerebral spinal fluid shunt, directly below the valve. Note that both the
camera and the light source are located outside of the body. (b) An illustration of the working principle of
the flow sensor.
We next describe the design parameters of the flow sensor.
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As shown in Figure 1(b), the flow sensor can be modeled as a cantilever with a single force applied at the
tip, for which the deflection of the cantilever is given by:
𝑦(𝑥) =
𝐹𝑥 2 (3𝐿−𝑥)
(1)
6𝐸𝐼
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where 𝐹 is force, 𝐸 is Young’s modulus for the sensor, 𝐼 is the area moment of inertia, 𝐿 is the length of
the sensor, and 𝑥 is the distance from the point of interest to the base of the cantilever.
The derivative of this equation is given by:
𝑦′(𝑥) =
2𝐹𝐿𝑥−𝐹𝑥 2
2𝐸𝐼
. (2)
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In addition, this is related directly to the angle of displacement (𝜃) of the flow sensor such that
𝑦 ′ (𝐿) =
𝐹𝐿2
2𝐸𝐼
𝑑
= tan𝜃 = . (3)
ℎ
where 𝑑 is the displacement of the light spot due to flow, and ℎ is the height from the sensor tip to the
surface of the skin.
From rearranging equation (3), the force at the tip of the cantilever is found to be
𝐹=
2𝐸𝐼∙tan𝜃
𝐿2
(4)
From equation (1), the force at the tip F and the displacement at the tip 𝑦(𝐿) are correlated as:
𝐹=(
3𝐸𝐼
𝐿3
) ∙ 𝑦(𝐿) (5)
From this equation, we find the cantilever spring constant K, which is correlated with 𝐸, 𝐼, and 𝐿:
𝐾=
3𝐸𝐼
𝐿3
(6)
Force is related to pressure by
𝐹 = ∆𝑃 ∙ 𝐴 (7)
Where ∆𝑃 is the change in pressure across the flow sensor, and 𝐴 is the cross-sectional area of the
nozzle opening.
In addition, flow rate 𝑄 and the pressure drop ∆𝑃 can be correlated by
∆𝑃 = 𝑄 ∙ 𝑅𝑓 (8)
3𝑅𝑓 𝐴ℎ
2𝐾𝐿
∙ 𝑄 (9)
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𝑑=
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where 𝑅𝑓 is the flow resistance of the sensing part.
Rearranging the above equations, the relationship between flow rate and displacement of the light spot
due to flow can be determined.
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Due to the small deflections of the cantilever, it is more practical to measure the changing light intensity of
the spot rather than the net displacement, which will be discussed in the result section.
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2.2 Optical System Design
The optical system consists of the input light source, a right-angle prism dielectric mirror, a ball lens, and
a fiber optic aligned as shown in figure 2(a). The input light source is a 5W red light LED with a peak
wavelength of 620-625 nm driven at a current of 1.2 A. The right-angle prism mirror was purchased from
ThorLabs (Newton, NJ) and has dimensions of 3 mm. It reflects the incoming light at a ninety-degree
angle into the ball lens, which was purchased from Edmund Optics (Barrington, NJ) and has a 2.5 mm
diameter and an index of 1.458. The ball lens serves to couple the light reflected off of the mirror into the
fiber optic. The fiber optic was purchased from ThorLabs and has a core diameter of 365 µm and a
numerical aperture (NA) of 0.22. The fiber serves two purposes. Firstly, it serves as an aperture so that
the light spot is not larger than the gold sensor tip, which is 1 mm in diameter. Secondly, it allows the
output light spot to be separated from the input light source so that the output light intensity data is
distinguishable from the light entering the system. The tip of the fiber is placed 2 mm away from the gold
sensor tip, out of the flow path. In the current experimental setup, the total size of the optical system is 53
mm. However, there is a possibility for further minituarization with development of the device. Current
commercially available shunts on the market range in size, with lengths between 24 and 47 mm, widths
between 16 and 24 mm, and heights between 7 and 8 mm [13]. As the device will be an attachment to
available shunts, the ideal size will be equal or smaller to the device which is already acceptable for
implantation. Currently the width of the device is 8 mm, the height is 12 mm, and the length is 56 mm.
However, the device can readily be scaled down. The height of the device is constrained only by the
height of the sensor, which is 7 mm in length and angled at 45 degrees, resulting in a minimum height of
about 5 mm. The length of the device is constrained by the length of the fiber optic and the lengths of the
mirror and ball lens, which could be minimized to about 15 mm. The width of the device is constrained by
the width of the mirror, ball lens, and sensor and could be reduced to about 5 mm. Therefore a fully
miniaturized version of the device would have dimensions of 5 mm x 15 mm x 5 mm, well below the
current size of implanted CSF shunts.
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Figure 2: (a) Diagram of the passive, implantable optical system used to read the displacement of the flow
sensor. (b) Sensor compartment.
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2.3 Flow Sensor Compartment
The flow sensor is placed in the flow sensor compartment, which is made from implantable polypropylene
purchased from Ensinger (Shelton, CT). The compartment consists of a base on which the flow sensor
rests at a 45-degree angle and a cover to hold the flow sensor in place. There is a clear PDMS window
over the top of the compartment that allows light to enter and exit the flow sensor compartment. It is
tightly packed to the compartment to prevent leaking. The cerebral spinal fluid flows into one side of the
compartment and enters the channel which directs the fluid to the flow sensor. The flow sensor
completely covers the channel through which the CSF flows so that the sensor must be displaced by fluid
flow, and liquid that enters the compartment cannot flow backwards after it passes over the sensor. After
passing over the flow sensor, the fluid goes to the shunt tubing and continues to flow towards the
absorption site. The clear PDMS window over the top of the compartment prevents leakage as the fluid
leaves the compartment. The sensor compartment has dimensions of 8 mm x 12 mm x 14 mm (w x l x h),
and the inner channels through which CSF flow have an inner diameter of 1 mm. As mentioned in the
previous section, the compartment size has the potential to be scaled down. The flow sensor
compartment is shown in Figure 2(b).
The device consists of the optical system, the flow sensor, and the flow sensor compartment. The optical
system is aligned so that the light spot precisely hits the gold tip of the sensor. The sensor is set at a 45degree angle and therefore reflects the light directly upwards to the surface of the skin. The output light is
then measured. As described previously, the changes in the angle of the flow sensor due to flow result in
deflection of the output light spot, which is measured by a camera.
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2.4 Flow Sensor Fabrication
The flow sensor is made from polydimethylsiloxane (PDMS), a silicone-based polymer that is commonly
used in medical devices [14]. In addition, PDMS has several beneficial properties, namely that it is
hydrophobic and does not absorb water, and it is optically transparent for wavelengths longer than 256
nm [15]. In addition, its thickness is easily manipulated through molding and spin coating techniques [14].
In all applications discussed below, Sylgard 184 PDMS and curing agents (Dow Corning, Midland, MI)
were utilized. The membrane flow sensor has a reflective gold circle embedded at the tip, which has a
diameter of 1.2 mm. First, the PDMS for the sensor was prepared in a 10:1 ratio of PDMS to curing agent
following the standard procedure [16,17]. The PDMS was then spin-coated for 1 minute at 1500 rpms to
achieve the desired thickness of 50 micrometers and allowed to cure in an oven at 30 degrees Celsius for
2 hours (step 1)[16,17]. After curing, the gold leaf was carefully placed on top of the PDMS by hand (step
2). The gold leaf sticks to the surface of the PDMS, so it is possible to then handle and cut the PDMS
without removing the gold surface. The gold leaf was purchased from L.A. Gold Leaf (Azusa, CA) and has
a thickness of 0.12 microns. After the gold is plated onto the PDMS, it is cut to create the 1mm diameter
circles that are subsequently embedded into the tip of the flow sensor (step 4). The gold plated PDMS
was cut using the Silver Bullet professional precision cutting machine (Silver Bullet Cutters, Apple Valley,
MN). Once the gold circles are fabricated, they are aligned in a well with a depth of 100 micrometers, the
desired thickness of the final sensors. The gold circles are placed with the plated side facing upwards.
Then, PDMS is prepared in the same procedure as dictated for the gold tips and poured over the gold
circles. The mold was then placed in the oven and allowed to cure for 4 hours. Once cured, the PDMS
with gold circles embedded was removed from the well and cut into the desired sensor shape by the
Silver Bullet precision cutter. The membrane sensor has a width of 2 mm, and a length of 2.7 mm. The
steps of fabrication are shown in Figure 3.
Step 4: Pour
PDMS well over
gold circles
Step 5: Cut
out final
result.
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Step 3: Cut out
circles
Gold
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PDMS
Step 2: Deposit
gold on PDMS
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Step 1: Spin coat
50 microns PDMS
onto glass
50 Microns PDMS
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Figure 3: Steps in PDMS sensor fabrication.
Cut out
sensors
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Glass
1 mm
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The surface roughness of gold leaf is typically 100-200nm (RMS) [18,19], which gave us sufficient
reflectance for our application as demonstrated in the result section. It is also known that PDMS-gold
composite films are susceptible to wrinkling when the fabrication process involves high temperatures (e.g.
thermal deposition of gold onto PDMS), due to the thermal expansion mismatch of the gold and the
PDMS substrate [20]. However, our sensor fabrication process is relatively low temperature (max
temperature is 60 degrees Celsius) and no wrinkles were observed in the gold film after curing of PDMS.
To verify that there was no effect from wrinkling on the sensor measurements, images were taken when
the sensor was at rest (flow rate = 0 ml/hr) and being deflected (flow rate =120 ml/hr) and compared. The
halfwidth of the intensity profile curve for each image was used to compare the light spot deformations of
the sensor at rest and while being displaced. Both the first and second images had half-widths of 1.3 mm.
The small difference in halfwidth indicates that the wrinkles had a negligible effect on the net intensity
measurement used to determine the light spot displacement. These images and their corresponding light
intensity profiles are shown below in figure 4. In addition, the maximum radius of curvature for the gold
mirror was calculated at the edge of the gold mirror, 1.4 mm from the base of the sensor. The radius of
curvature can be found by calculating the second derivative of the deflection, 𝑦 ′′ , which is derived from
the first derivative given in equation (2):
𝑦 ′′ (𝑥) =
𝐹(𝐿−𝑥)
𝐸𝐼
. (10)
The radius of curvature for the sensor is then given by:
𝑅𝑠 =
1
𝑦′′
. (11)
The strain at the surface of the sensor can then be calculated using the radius of curvature (𝑅𝑠 ):
𝜀=
0.5𝑡
𝑅𝑠
(12)
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Where 𝑡 is the thickness of the sensor. The value of second derivative of deflection (equation (10)) is
determined by first finding the force due to flow from equations (3)(4), where 𝜃 is determined
experimentally for the flow rate in question. In this case ℎ = 11.7 mm and 𝑑 = 0.17 mm at 𝑄 = 120 mL/hr.
The second derivative of deflection can then be calculated at any point along the sensor. In this case, it
was calculated at the edge of the gold light spot where the radius of curvature would be maximum (𝑥 =
1.4 mm). The radius of curvature was found to be 1.24 m at the max flow rate of 𝑄 = 120 mL/hr. The
thickness (𝑡 ) of the cantilever is 100 micrometers. Using equation (12) the strain at the surface of the
sensor was calculated and found to be 4.03 × 10−5 . This indicates that curvature of the sensor will not
cause the detectable effects of the wrinkles on the generated light spot.
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Figure 4: Top: Light spot profile and light spot for the sensor at resting position. Bottom: Light spot profile
and light spot for sensor displaced by a flow rate of 120 ml/hr.
3. Experimental Setup
The experimental setup consists of the input light source, optical system, flow sensor, and camera
aligned as seen in Figure 4. At the input of the optical system and the output of the flow sensor
compartment, 3 pieces of PDMS are placed to simulate the optical properties of the skin [16,17]. The
artificial CSF is pumped through the device using a syringe pump (Harvard Apparatus, Holliston, MA)
controlled by a MATLAB program.
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The 3 pieces of PDMS represent the 3 layers of human skin: the dermis, epidermis, and the
subcutaneous fat layer. Each layer is constructed from Sylgard PDMS mixed in a 10:1 ration of PDMS to
curing agent, in addition to an absorbing agent and a scattering agent (titanium oxide). The epidermis
target thickness is 50 micrometers, and the absorbing agent is freeze-dried coffee, which has been
shown to have very similar optical properties to skin [16,17] The coffee was ground and mixed with the
PDMS and titanium oxide in a ratio of 10mg coffee per milliliter PDMS. The dermis target thickness is 1.3
mm, and the absorbing agent is Nigrosin, which was added to the PDMS in a ration of .05 mg/ml. The
subcutaneous fat layer target thickness is 1.9 mm, and the absorbing agent is India ink, which was added
in a ratio of .2mg/ml. This process is described fully in [17]. After mixing, the appropriate amounts were
poured into a petri dish and allowed to cure for 24 hours in a 30-degree Celsius oven. Artificial cerebral
spinal fluid (ACSF) was purchased from Ecocyte Bioscience US LLC (Austin, TX). This was pumped
through the shunt for all experiments discussed further. The final experimental set up consisted of the
skin phantoms, optical system and flow sensor, pump, artificial cerebral spinal fluid, LED input, and
camera, as shown in Figure 4. It is possible that the difference in optical properties between CSF and the
artificial CSF used in the experiments could lead to a reduction in net intensity of the light spot when
implemented in vivo. However, the flow measurements are based on relative changes in light intensity,
rather than the absolute intensity value, and therefore this would not impact the overall effectiveness of
the device. In addition, the scattering coefficient of CSF can be estimated as 3 mm−1 [21], whereas the
scattering coefficient of human skin is around 9 mm−1 [22]. In the near infrared range, light penetration of
up to 3 cm has been reported [23], indicating the high transmission capabilities of NIR. With this in mind,
it is unlikely that an increase in scattering coefficient between the artificial CSF used in this experiment
and CSF in vivo would have a significant impact on the results.
Gold tip of
PDMS
membrane
Input Light
Source
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Camera
Direction of
CSF Flow
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Fiber optic
Mirror and ball
lens
PDMS “skin”
10mm
(a)
5mm
(b)
5mm
(c)
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Figure 5: (a) picture of experimental setup including input light, skin, and camera. (b) picture of optical
system used in experiments. (c) picture of flow sensor compartment.
4.Results
The deflection of the light spot could be measured in two ways. First, it could be measured by the
amount of deflection in terms of distance. This was obtained by measuring the physical location of the
brightest spot of the output light. Alternatively, the deflection could be measured as a function of light
intensity at one specific location. Because the distance of deflection is small, this is a feasible method of
measuring the deflection of the flow sensor. When the light intensity is measured from one location on the
skin, the intensity will fluctuate as the light spot is deflected across the surface of the skin. The change in
light intensity is therefore measured by a camera and used to calculate the flow of CSF.
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To determine the change in light spot displacement for each flow rate, the light spot intensity
profile was obtained (Figure 6). A region of interest (30 × 30 pixel) was selected and the light spot
intensity of that area was collected across the horizontal profile of the light spot (Figure 6). The change in
intensity over distance was determined and used to relate the change in intensity of the light spot to the
displacement. Because the sensor moves throguh a small distance, it is reasonable to assume that the
intensity change is linearly related. While collecting data, the same 30 × 30 pixel region of interest was
used to measure light intensity in a specific spot. This spot was chosen to be the region where the light
spot intensity changed linearly over distance. In this case, we chose the far right side of the light spot.
The change in light intensity for 10 peaks was averaged for each flow rate. The changes in intensity for
the peaks were then calculated and averaged, and the standard deviation was calculated. The average
amplitude of the peaks changed depending on the flow through the sensor. So, the light spot
displacement increased depending on the amount of flow. Figure 6 shows two graphs generated at 60
and 70 ml/hr, respectively.
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Figure 6: Left: light spot profile. Right: graph of light spot intensity across red arrow.
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The first experiment measured the amplitude of deflection of the light spot at varying flow rates.
As CSF generally flows at rates of 20-90 mL/hour, flow was measured in increments of 10 ml/hour
starting from 20 ml/hour and going up to 90 ml/hour. The second experiment tested the stability of the
PDMS membrane sensor by pumping artificial CSF through the device continuously for two weeks. The
fluid was automatically pumped through the device and controlled by a MATLAB program that allows for
manual input of flow rate and duty cycle; in this case the flow rate was modulated by a square wave with
a 7 second duty cycle at a rate of 90 ml/hr. The third experiment measured the flow rate from 20 to 90
ml/hour to verify that there were no significant changes in sensor performance after two weeks of
continuous flow. The resulting graphs of light spot displacement versus flow rate at the start and after two
weeks of continuous activity are shown in Figure 7. The graph shows light intensity increasing linearly
with flow rate before and after two weeks of running the sensor continuously. The slope for both lines is
1.9 x 10 ^-3 mm/(ml/hr).
60 ml/hour
Light Intensity (a.u.)
205.5
205.0
∆Light
Intensity
= 1.02
204.5
204.0
203.5
203.0
0
5
10
15
20
25
30
Time (seconds)
70 ml/hour
of
∆Light
Intensity
= 1.28
205.0
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204.5
204.0
203.5
0
5
10
15
20
25
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Time (seconds)
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Light Intensity (a.u.)
205.5
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Figure 7: Light intensity at a pulse rate of 70 and 60 ml/hour. The change in light intensity depends on the
flow rate over the sensor.
Flow Rate vs. Light Spot Displacement 1
y = 0.0017x - 0.0596
r² = 0.9639
0.15
0.10
0.00
0
20
40
60
80
100
-0.05
140
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Flow Rate (ml/hr)
Flow Rate vs. Light Spot Displacement 2
0.20
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y = 0.002x - 0.0585
r² = 0.9901
0.15
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0.10
0.05
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Light Spot Displacement (mm)
120
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0.05
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Light Spot Displacement (mm)
0.20
0.00
0
-0.05
20
40
60
80
100
120
140
Flow Rate (ml/hr)
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Figure 8: Graph of light spot displacement at various flow rates. Top: at the start of the study, Bottom:
After two weeks of running the sensor continuously.
As previously mentioned, the relationship between the light spot displacement 𝑑 and the flow rate 𝑄 is
given by equation (9):
𝑑=
3𝑅𝑓 𝐴ℎ
2𝐾𝐿
∙ 𝑄 (9)
Flow resistance 𝑅 is found by rearranging the above equation:
𝑅=
2𝐾𝐿
3𝐴ℎ
∙
𝑑
𝑄
(13)
Now we apply the experimental values to this equation.
The spring constant 𝐾 of the cantilever was measured using a calibrated force sensor following the
protocol described in [24], and was found to be
𝐾 = 0.059 N/m (14)
From this experimental value, we used equation (8) and design values of sensor length 𝐿 = 0.002 m,
sensor width 𝑊 = 0.002 m, and sensor thickness 𝑡 = 0.001 m to calculate the Young’s modulus of our
sensor to be 𝐸 = 9.45 × 105 Pa. This fits within the range of the Young’s modulus expected for a PDMS
film with a thickness of 1000 micrometers (8 × 105 Pa < 𝐸 < 2 × 106 Pa) [25,26].
The area (𝐴) was equivalent to the area of the hole through which CSF flows in the sensor compartment.
𝐴 = 1.76 × 10−6 m2 (15)
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The height ℎ between the flow sensor and the surface of the skin from which the light spot was measured
was found to be:
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ℎ = 11.7 mm (16)
When the maximum light spot displacement of 𝑑 = 0.18 mm is obtained at flow rate of 𝑄 = 120 ml/hr,
the flow resistance is calculated to be:
Pa
m3 /𝑠
(17)
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𝑅𝑓 = 3.22 × 107
The maximum pressure drop occurs at the same condition. By plugging (17) and 𝑄 = 120 ml/hr to
equation (8), we find:
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∆𝑃 = 𝑄 ∙ 𝑅𝑓 = 1.1 Pa (18)
5.Discussion
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Typical shunt valves are designed to operate within the range of 0.3 to 2.0 kPa (30 to 200 mm H 2O) [4].
The estimated maximum pressure drop of 1.1 Pa is small enough not to interfere with the operation of
currently available shunt valves.
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We have investigated a novel method of measuring the flow in a cerebral spinal fluid shunt. It was
shown that the biocompatible PDMS flow sensor is capable of measuring flow rates from 20 to 120
ml/hour and its performance remains fairly consistent over time. It is possible to further optimize the
sensitivity by tuning the sensor thickness.
Future work includes the determination of the optimal light source and intensity for this
application. It is important to note that in a clinical setting, the depth of the shunt beneath the skin will vary
depending on patient weight and other factors. Therefore, determining the optimal light intensity for
varying thicknesses would be useful in optimizing the device performance. The use of infrared light, which
shows a higher transmission for tissues, is an option [23].
The size of the flow sensor (w × l × h = 1.2 mm × 2.5 mm × 0.1 mm) is smaller than the device
used in the in vivo study discussed in [12], and is sufficiently compact. The device is capable of being
miniaturized to dimensions of roughly 5 mm x 15 mm x 5 mm, well below the standard sizes of
commercially available shunts. In addition, all the materials used in the device are biocompatible and
suitable for animal tests and clinical studies.
6.Conclusion
We have developed a novel flow sensor that is capable of measuring the low flow rates of
cerebral spinal fluid. The sensor is biocompatible and suitable for implantation in conjunction with typical
cerebral spinal fluid shunts. The sensor system is made from biocompatible polydimethylsiloxane, is fully
passive, and does not require implanted electronics for its function. Changes in sensor position due to
flow was measured as the displacement of a reflected light spot. We tested the system using artificial
cerebral spinal fluid and skin phantoms made of 3 layers of PDMS. The results demonstrate that the
sensor is capable of measuring flow rates from 20 ml/hr to 120 ml/hr, suitable for measuring the flow rates
of cerebral spinal fluid.
Author statement
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Ariane Garrett: Investigation, Formal analysis, Validation. Garrett Soler: Investigation,
Formal analysis, Validation. Michael Diluna: Conceptualization, Methodology. Ryan
Grant: Conceptualization, Methodology, Funding acquisition. Hitten Zaveri:
Conceptualization, Methodology, Validation, Funding acquisition. Kazunori Hoshino:
Conceptualization, Methodology, Formal analysis, Investigation, Validation, Resources,
Funding acquisition.
Declaration of interests
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The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
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Author biographies
Ariane Garrett (AG) is a senior undergraduate Biomedical Engineering major with a
concentration in Bioinstrumentation at the University of Connecticut. She has focused
on the development of a flow sensor for use in a cerebrospinal fluid shunt throughout
her undergraduate career. She was named a University Scholar in April 2019, a
prestigious degree program that allows students to pursue an independent research
project in their final three semesters. She was selected as a Goldwater scholar in April
2019.
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Garrett Soler (GS) received the bachelors (B.S.) degrees in biomedical and electrical
engineering from the University of Connecticut in 2019. GS is currently pursuing the
PhD in biomedical engineering with a masters (M.S.) in electrical engineering from the
University of Southern California. His research interests include biomedical sensors and
neural interfacing devices, analog and mixed signal integrated circuits for low power
biomedical applications, and wirelessly implantable medical devices for therapeutic
application.
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Michael Diluna (MD) is an Assistant Professor of Neurosurgery and Pediatrics at Yale
University and Chief of Pediatric Neurosurgery at Yale-New Haven Hospital. He joined
Yale University in 2010 after completing medical school and a neurosurgical residency
at Yale and a fellowship in pediatric neurosurgery at The Children’s Hospital of
Philadelphia. Dr. DiLuna is an attending physician at Yale-New Haven Hospital and the
Yale-New Haven Children's Hospital and a consultant in neurosurgery at the West
Haven Medical Center.
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Ryan Grant (RG) received his B.S. in Cellular & Molecular Biology, Biopsychology, and
Neurosciences from the University of Michigan, Ann Arbor, MI, USA in 2005, followed
by a M.S. in Cellular & Molecular Biology from the University of Michigan, Ann Arbor,
MI, USA with highest honors in 2006. Dr. Grant then received his M.D. from the
Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
in 2010 and was elected into the Alpha Omega Alpha (AOA) Honors Society. He went
on to train in neurosurgery for 7 years at Yale University, New Haven, CT, USA,
finishing his training in 2017. Dr. Grant subsequently completed a complex and
minimally invasive spine fellowship at Yale University, New Haven, CT, USA in 2018. In
2018, he completed his M.B.A. at the Quantic School of Business and Technology,
Washington D.C., USA. He currently serves as an Associate in the Department of
Neurosurgery at Geisinger Medical Center. In addition to being a practicing
neurosurgeon, he has a strong focus on healthcare entrepreneurship and medical
device development. He holds several patents and co-founded an award winning
venture-capital funded company in New York City called Nomad Health
(www.nomadhealth.com). Dr. Grant is a member of the American Association of
Neurological Surgeons and the Congress of Neurological Surgeons.
Hitten Zaveri (HZ) has received formal training in electrical engineering (B.S., M.S.),
computer engineering (B.S.), biomedical engineering (M.S., Ph.D.), neurology and
epilepsy. He is an Assistant Professor of Neurology and the director of the
Computational Neurophysiology Laboratory at Yale University. Research in this
laboratory lies at the intersection of engineering, mathematics and neuroscience. Dr.
Zaveri has a strong interest in the application of engineering, in general, and time-series
analysis, in particular, to problems in neuroscience. His specific research interests
include the generation of seizures, seizure prediction and the design and testing of
neurotechnology..
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Kazunori Hoshino (KH) obtained a Ph.D. from the University of Tokyo, Tokyo, Japan
in 2000. He worked for the University of Tokyo from 2003 to 2006 as a full-time lecturer
in the Department of Mechano-Informatics, School of Information Science and
Technology. From 2006 to 2013, he worked for the University of Texas at Austin as a
Senior Research Associate in the Department of Biomedical Engineering. In 2014, he
joined the University of Connecticut, where he currently works as an Assistant Professor
of Biomedical Engineering. His research interests include (1) micro-electro-mechanical
systems (MEMS) based detection and analysis of microtissues, and (2) nano and micro
scale sensing and imaging for biomedical applications. As the principal investigator, Dr.
Hoshino received several research grants including NSF-CCSS (Communications,
Circuits, and Sensing-Systems) award, NEDO (the New Energy and Industrial
Technology Development Organization, Japan) Young Investigator Award, MEXT
(Ministry of Education, Culture, Sports, Science and Technology, Japan) grant for young
investigators. He has more than 120 peer reviewed publications, and is the inventor of 6
US patents and 12 Japanese patents.