Article
pubs.acs.org/ac
Online Sample Conditioning for Portable Breath Analyzers
Amlendu Prabhakar,† Rodrigo A. Iglesias,† Xiaonan Shan,† Xiaojun Xian,† Lihua Zhang,† Francis Tsow,†
Erica S. Forzani,†,‡ and Nongjian Tao*,†,§
†
Center for Bioelectronics & Biosensors, Biodesign Institute, ‡School for Engineering of Matter, Transport and Energy, and §School
of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
ABSTRACT: Various innovative chemical sensors have been
developed in recent years to sense dangerous substances in air
and trace biomarkers in breath. However, in order to solve real
world problems, the sensors must be equipped with efficient
sample conditioning that can, e.g., control the humidity, which
is discussed much less in the literature. To meet the demand, a
miniaturized mouthpiece was developed for personal breath
analyzers. A key function of the mouthpiece is to condition the
humidity in real breath samples without changing the analyte
concentrations and introducing substantial backpressure, which is achieved with optimized packing of desiccant particles.
Numerical simulations were carried out to determine the performance of the mouthpiece in terms of various controllable
parameters, such as the size, density, and geometry of the packing. Mouthpieces with different configurations were built and
tested, and the experimental data validated the simulation findings. A mouthpiece with optimized performance reducing relative
humidity from 95% (27 000 ppmV) to 29% (8000 ppmV) whereas retaining 92% nitric oxide (50 ppbV to 46 ppbV) was built
and integrated into a hand-held exhaled nitric oxide sensor, and the performance of exhaled nitric oxide measurement was in
good agreement with the gold standard chemiluminescence technique. Acetone, carbon dioxide, oxygen, and ammonia samples
were also measured after passing through the desiccant mouthpiece using commercial sensors to examine wide applicability of
this breath conditioning approach.
W
the nafion approach is that it not only removes unwanted
humidity but also partially or completely (75% to >90%)
removes many wanted analytes, such as low-molecular-weight,
polar, oxygenated compounds, including some ketones,
alcohols, aldehydes, and water-soluble ethers.14 These analytes
are of high clinical significance for different diseases. Real time
breath sample measurement without removal of humidity has
been done using mass spectrometric platforms including
selected ion flow tube (SIFT)17,18 and proton transfer reaction
(PTR)19−21 mass spectrometry. These techniques employ
special handling of breath sample to avoid humidity
condensation and require long heated tubes and capillaries
heated up to 100 °C.22−24 In addition to conditioning the
humidity of a breath sample, another critical requirement for a
breath analyzer is to provide an appropriate volumetric flow
rate and back pressure. The flow rate and back pressure
requirements differ depending on specific guidelines for the
analyte being measured. For example, in the case of breath
nitric oxide, a biomarker for inflammation, the American
Thoracic Society recommends that the back pressure should be
at least 5 cm H2O.25
As an effort to overcome the difficulties discussed above, we
introduce here a breath sample conditioning approach based on
desiccant particles packed in tubing, which can be integrated
hile most works on chemical sensors published to date
are devoted to detection, sample collection and
conditioning that often determine whether a sensor can solve
a real world problem or not are much less emphasized. This is
especially the case for breath analyzers. Human breath contains
a variety of chemical signatures that are attractive for early
detection and noninvasive management of diseases.1,2 Some of
these chemicals, such as nitric oxide, hydrogen, and 13C urea,
have already been used in clinical settings,3−7 and many others
have been studied and identified as potential biomarkers for
different diseases and health conditions.8,9 A difficult challenge
in developing breath analyzers is to accurately measure a trace
amount of analytes in the presence of not only hundreds of
interfering gases but also highly concentrated water vapor.
Human breath is nearly saturated with water vapor (>95%
relative humidity, RH)10,11 which coming out at body
temperature condenses in the sensor and often leads to the
failure of the breath analyzer, which requires proper sample
conditioning before detection.12,13
A common solution to condition a high humidity sample is
to introduce nafion tubing in the sampling line to reduce
humidity. However, the reported efficiency of humidity
reduction by nafion tubing is highly variable, ranging from
58% to 98% depending on ambient humidity.14,15 For this
reason, many applications must flow additional drying gas into
the nafion tubing in order to maintain the efficiency,16 which
adds complexity into the device and also makes it unsuitable for
personal use that requires portability. A more serious issue with
© 2012 American Chemical Society
Received: June 5, 2012
Accepted: July 18, 2012
Published: July 18, 2012
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were used to model the flow of breath through this medium,
where ρ denotes the density of humid air (1.15 kg·m−3), u
represents the velocity, and p refers to the pressure. Equation 4
above implies that the fluid flow is incompressible in the
subdomain. Since Mach number for the flow at 6.67 L·min−1
through a typical mouthpiece geometry is less than 0.3, the only
appreciable fluid density change resulted from change in
temperature of the breath due to rise in desiccant temperature.
The increase in breath temperature was measured to be less
than 3 °C resulting in ∼1% increase in density for which the
assumption of incompressible flow is valid. Boundary
conditions for the flow were set as follows: Boundary 1: u·n
= u0 (inlet), where u0 is the linear flow velocity at the inlet;
Boundary 2 and Boundary 3: u = 0 (wall); Boundary 4: p = 0
(outlet). With these subdomain and boundary settings, the
velocity field was determined and the solution obtained was
further used to solve the mass transport process using
COMSOL 3.5.
Mass transport of water within the desiccant tube was
described by the diffusion−convection equations
into the inlet of existing breath monitoring devices. We further
establish the relationships of the output humidity, flow rate, and
pressure in terms of controllable parameters, such as particle
size and tubing geometry. The relationships are established
based on numerical simulation and validated experimentally.
Using this approach, we have designed mouthpieces for nitric
oxide detection using a hand-held device.26
EXPERIMENTAL AND SIMULATION METHODS
Simulation Methods. Numerical simulation of the
desiccation process in the mouthpiece was performed using
finite element method software COMSOL multiphysics 3.5.
The simulation included models for flow and mass transport in
the porous medium of calcium chloride, which was used as a
desiccant material to adsorb water and control humidity.
Temperature change during the desiccation process was not
taken into account for simplifying the model. It was
experimentally observed that the temperature increased by
about 20 °C at the mouthpiece inlet for 1 L of breath sample
whereas the outlet temperatures increased by 1−2 °C. This rise
in temperate did not have considerable effect on the working
efficiency of the overall desiccant tube (Table 2) since enough
material in the tube was far away from saturation. A 2dimensional rectangular geometry with rotational symmetry, as
shown in Figure 1, was used to simulate the cylindrical tubing.
■
∂C i
+ ∇·(Di ∇C i + C i u i) = R
∂t
where Ci denotes the concentration of the species, D is the
diffusion coefficient, u represents the velocity, and R refers to
the rate of consumption of species i. These equations were
applied to the two components of the desiccation process, viz.,
humidity in the breath (i = 1) and the surface binding sites
available on desiccant calcium chloride for capture of humidity
(i = 2). For breath, the diffusion coefficient of water vapor was
set to be 4.6 × 10−7 m2·s−1.30 The boundary conditions were
set as follows: Boundary 1: C1 = Cin1 (1 − e−t/2) (inlet, allows
humidity to rise from 0 to within 1% of the maximum breath
humidity Cin1 in 10 s compensating for time lag due to sampling
of nonalveolar dead space air); Boundary 2 and Boundary 3:
n·(D∇C1 + C1u) = 0 (wall); Boundary 4: n·(D∇C1) = 0
(outlet, no convective flux).
For binding sites on the solid calcium chloride, diffusion was
neglected and all the boundaries were set as wall for mass
transfer [i.e., n·(D∇C2 + C2u) = 0] assuming no inflow or
outflow of the desiccant material through any boundary. The
rate of water vapor consumption was given by the linear driving
force approximation31−33
Figure 1. Representation of the modeling domain representing the
cylindrical desiccation tube in two dimensions assuming a rotational
symmetry of packing.
The tubing, defined as a subdomain, was packed with the
desiccant particles of different diameters (d) into a porous
structure, with porosity, εp, varying from 0.25 to 0.65. The
permeability (κ) of the system for a given porosity and particle
size was estimated by Kozeny’s relation27
κ = constant ×
R = k 0(C* − C s)
d2εp3
(1 − εp)2
κ ΔP
η Δx
C* =
(2)
(3)
∇·u = 0
(4)
C20k1C1
1 + k 2C1
(7)
where C02 represented the initial concentration of binding sites
on calcium chloride available for humidity capture and k1 and k2
were equilibrium parameters obtained from fitting, which were
0.33 m3·mole−1 and 0.01 m3·mole−1, respectively. The surface
humidity concentration at any time was represented as
where η is the dynamic viscosity (1.74 × 10−5 Pa·s) of humid
air at physiological temperature.29
Brinkman equations given by
⎤
⎡ η
ρ ∂u
η
+ ∇·⎢ − (∇u + (∇u)T ) + pI⎥ = − u
εp ∂t
κ
⎦⎥
⎣⎢ εp
(6)
where k0 is the mass transfer coefficient, obtained from
parameter fitting to be 5.5 × 10−3 s−1, Cs represents the surface
concentration of water on the calcium chloride surface at any
given time, and C* is its equilibrium value. Equilibrium water
concentration was modeled through the Dubinin−Astakhov
equation approximated as34−36
(1)
The constant in the equation was determined experimentally to
be 984 by measuring the sample flow rate (v) and pressure
difference across a known length (Δx) of the mouthpiece using
Darcy’s law28
v=
(5)
C s = C20 − C2
(8)
Finally, the rate of consumption was obtained as
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Figure 2. (A) Simulated velocity field along the tube shows uniform flow field established at a given flow rate of 6.67 L·min−1, particle size of 1.15
mm, and porosity of 0.425. (B) Simulated pressure profile along the tube showing increasing back pressure with tube length assuming uniform
packing density.
⎤
⎡ C 0*k C
R = k 0⎢ 2 1 1 − (C20 − C2)⎥
⎦
⎣ 1 + k 2C1
RESULTS AND DISCUSSIONS
Simulation. Flow and mass transfer simulations were
carried for several mouthpiece configurations. Figure 2A
■
(9)
These mass balance equations with the appropriate boundary
conditions described above were solved using COMSOL 3.5
coupled with the velocity field obtained earlier with the flow
simulation to generate a concentration profile of breath
humidity introduced into the desiccation tube.
Experimental Validation of Mouthpiece Performance.
In order to experimentally validate the simulation results,
several mouthpieces were prepared by packing desiccant
particles into cylindrical tubes. Different particle sizes of the
desiccant were obtained by refining anhydrous calcium chloride
pellets (Fisher Scientific, 4−20 mesh). These refined particles
were size selected by sieving through wire meshes of predefined
sizes. Average particle sizes of 1.15 mm and 0.65 mm were
chosen for use. A cylindrical plastic mouthpiece (VacuMed,
Part# 1018-22) with internal diameter of 22 mm was used for
packing these particles at porosity values of 0.425 and 0.365,
respectively. Mouthpieces with three different lengths (12, 24,
and 46 mm) were tested.
Humidity levels of the breath sample before and after passing
through the mouthpiece were measured using a selected ion
flow tube mass spectrometer (SIFT-MS) (Instrument science
Ltd.) operating in multiple ion monitoring mode with H3O+ as
the precursor ion.17 The backpressure generated by the
mouthpiece was measured using a pressure sensor (Freescale,
Part# MP3 V5004G) at a fixed sample flow rate. Sample flow
rate from pressurized gas container (Praxair, Breathing grade
air) was controlled with pressure regulators and monitored with
a mass flow meter (Sensirion, EM1).
Integration of the Mouthpiece with Breath Analyzers.
The mouthpiece and a non-rebreathing T-valves (VacuMed,
Part# 1464) were integrated into a portable breath nitric oxide
sensor developed in our lab. The breath sensor was based on
selective colorimetric change due to redox chemistry of
phenylenediamine derivatives with the analyte.26 Subjects
blew directly into the mouthpiece for online measurement.
The readings from the portable nitric oxide sensor were
compared and correlated with chemiluminescence detection
(Sievers NOA), which is the gold standard for nitric oxide
measurement. Selective capture of humidity over some other
gases by the desiccant material was tested with samples
collected offline in metal laminated Tedlar bags at a flow rate of
6.7 L·min−1 using commercial sensors. Acetone and ammonia
were measured with SIFT-MS; carbon dioxide was measured
using an absorption infrared based hand-held monitor (Telaire
7000 Series), and oxygen was measured using a portable
electrochemical sensor (Vascular technologies).
Table 1. Simulated Values of Back Pressure in cm H2O
Generated in the Desiccant Mouthpiece for Varying
Porosities and Particle Sizesa
porosity
a
particle size (mm)
0.25
0.35
0.45
0.55
0.65
0.35
0.70
1.05
1.40
243.50
60.91
27.07
15.22
66.68
16.67
7.41
4.17
22.46
5.61
2.49
1.40
8.23
2.05
0.91
0.52
3.01
0.75
0.33
0.18
Simulation is for 5 grams of calcium chloride.
Figure 3. Pressure drop as a function of tube geometry for a given
volumetric flow rate (6.67 L/min).
Figure 4. Simulation result of breath humidity concentration along the
desiccation tube. Result is for a volumetric flow rate of 6.67 L/min and
sampling time of 30 s through a desiccant tube (15 mm diameter, 30
mm length, 5 g of calcium chloride, 1.15 mm particle diameter,
porosity of 0.425).
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channel. Figure 2B shows the simulated pressure profile along
the tube. The pressure drop increases with the increasing length
of the packing material at a given packing density. Values for
back pressure resulting from different particle sizes and
porosities of packing for a given amount (5 g) of calcium
chloride were also calculated as shown in Table 1.
The data from Table 1 provides guidelines on packing of the
desiccant material to achieve the desired back pressure range by
changing either or both the particle size and the porosity of the
mouthpiece. It is evident that back pressure at a given flow rate
can be reduced by increasing either the particle size or the
porosity of packing for a given mass of desiccant and
mouthpiece geometry. Simulations were also carried out to
obtain the effect of mouthpiece geometry (diameter and
length) for a fixed particle size and porosity of packing
assuming uniform packing density. Figure 3 plots pressure drop
as a function of tube geometry with particles 1.15 mm in
diameter packed with a porosity of 0.425. It is evident from the
plot that the pressure drop decreases with increasing diameter
and decreasing length of the mouthpiece for a given volumetric
sample flow rate.
While providing an appropriate backpressure with the
mouthpiece is an important requirement for many breath
analyzers, other important parameters include the desiccation
efficiency, which should be considered together with the
backpressure. For this reason, the desiccation process was
simulated. The desiccation of the breath along the tube (15 mm
diameter, 30 mm length, 5 g of calcium chloride, 1.15 mm
particle diameter, porosity of 0.425) is shown in Figure 4.
Humidity of the sample decreases along the tube resulting in
dryer output of the sample. Humidity levels at boundary 1
(inlet) and boundary 4 (outlet) were integrated for 30 s in
order to calculate of the efficiency (output/input %) of the
desiccation process.
Desiccation efficiencies with different particle sizes of the
desiccant particles were simulated for a given flow and amount
of desiccant. Figure 5 shows the desiccation efficiency
decreasing with increasing particle size for 5 g of calcium
chloride. The efficiency was found to be independent of the
packing porosity under these conditions. Desiccation efficiencies were also simulated for a fixed particle size and packing
porosity with changing mouthpiece geometry (length and
diameter). Figure 6 shows a plot of desiccation efficiency as a
function of mouthpiece geometry with 1.15 mm wide particles
packed with a porosity of 0.425. It can be seen from the plot
that the efficiency of desiccation improves with increasing
length and diameter (i.e., volume) of the mouthpiece.
These simulation results are useful in choosing the best
parameters for preparing a customized mouthpiece for any
breath analyzers. These parameters include mouthpiece
geometry (length and diameter), particle size, and packing
porosity. It is also clear that if any of these parameters are
constrained based on particular needs of a certain device then
other parameters can be varied to achieve the desired
performance.
Experimental Validation of Mouthpiece Performance.
In order to validate the flow simulation, mouthpieces with three
different lengths and 22 mm diameter were prepared. This
geometry was chosen for easy integration with our device.
Figure 7 shows the comparison of simulated to measured
pressure difference across the tube for the three chosen lengths.
Both results correlate well showing that the pressure drop
increases with increasing length of the mouthpiece.
Figure 5. Desiccation efficiency for different particle sizes at a fixed
geometry of the mouthpiece (15 mm long, 22 mm diameter) using 5 g
of calcium chloride.
Figure 6. Desiccation efficiency simulated as a function of tube
geometry for a given volumetric flow rate (6.7 L/min).
Figure 7. Comparison of simulated and experimentally measured
pressure difference across the mouthpiece packed with 1.15 mm
particles with a porosity of 0.425.
shows the flow profile obtained from a 30 mm long desiccant
tube, 15 mm in diameter, packed with calcium chloride particles
of 1.15 mm average diameter with a porosity of 0.425. For this
porosity and particle size at a flow rate of 6.67 L·min−1, the
velocity field is homogeneous due to porous properties of the
structure, which is in contrast to parabolic velocity fields
generally obtained under similar conditions in a nonporous free
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Table 2. Comparison of Simulated and Measured Desiccation Efficiencies with Different Parameters of Mouthpiece
Construction
length (mm)
diameter (mm)
particle size (mm)
porosity
simulated efficiency (%)
measured efficiency (%)
difference in efficiency (%)
12
25
49
12
15
22
22
22
22
22
1.15
1.15
1.15
0.65
0.65
0.425
0.425
0.425
0.365
0.365
44.1
68.8
89.3
58.5
69.6
43.6
66.49
81.54
61
68.4
0.5
2.31
7.76
2.5
1.2
Figure 8. Humidity output of the mouthpiece (15 mm diameter, 30
mm length, 5 g of calcium chloride, 1.15 mm particle diameter,
porosity of 0.425) for ten successive breathings. The baseline was
obtained with dry air purging.
Figure 10. Analysis of nitric oxide levels in a breath sample using a
colorimetric optical sensor integrated with the desiccation mouthpiece
for online sample conditioning. A linear response is obtained toward
nitric oxide.
exhaustion and heating although the average humidity
remained within the desired noncondensing levels.
Integration with Portable Breath Sensors. A desiccant
mouthpiece with an average efficiency of 70% (30 s sampling at
6.67 mL·min−1) was used to sample breath in a colorimetric
optical sensor developed in our lab. Figure 9A shows the
response of the sensor to breath sampling without the
mouthpiece. A jump in the intensity of signal can be observed
in the sensing photodiode due to humidity condensation on the
substrate affecting the transmittance.37 Also, the reference
photodiode shows random fluctuations in the signal. Response
of the same sensor after integration of the mouthpiece is shown
in Figure 9B. A linear decrease in intensity due to color
development is observed without any spike due to humidity on
the sensing photodiode. The reference photodiode also shows a
Table 2 shows a comparison of simulated and measured
desiccation efficiencies for different mouthpiece geometries,
packing, and particle sizes. Deviations in the results increase at
high desiccation efficiencies. With increasing desiccation, the
experimental efficiency is lower than the predicted efficiency
from the model. This could be attributed to the exothermic
nature of the desiccation process which starts to affect the
efficiency for high humidity capture. Due to higher surface
temperature, the actual efficiency of capture is lower as
observed in the experimental results.
Figure 8 shows humidity output of the mouthpiece over ten
successive breathings measured by SIFT-MS. Each exhalation
was followed by purging with dry air sample. It can be observed
that the output was much drier initially and the efficiency of the
mouthpiece decreased with successive breathing cycles due to
Figure 9. Optical response from photodiodes used for detection of color change (sampling) and correction (reference) in intensity during breath
test (A) without the use of a desiccant mouthpiece and (B) after integration of the desiccant mouthpiece.
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Figure 11. (A) Selective removal of humidity by the desiccant mouthpiece over other components of interest. (B) Absolute value of concentrations
for different compounds tested before and after passing through the mouthpiece.
Figure 12. (A) Efficiency and reusability of one mouthpiece with a 10 min gap between successive tests. Mean desiccation efficiency (%) = 67.3%
and variation from the mean is 5.6% (B) Reusability of one mouthpiece over a week measured two times each day and stored in ambient lab
conditions in a zip-lock bag. Mean desiccation efficiency (%) = 67.17% and variation from the mean is 3.4%.
min between successive tests. Each sample was collected at a
flow rate of 6.7 L/min to fill a 4-L Tedlar bag. A gap of 10 min
was given between each collection which was necessary to avoid
efficiency loss due to overheating of the tube. The desiccation
tube was also used for routine testing over a week. A single
mouthpiece was used for sample collection, two times a day
separated by 6 to 8 h for six consecutive days. Figure 12B shows
the desiccation efficiency of each test. The mean efficiency for
the six day test was 67.17% with a variation of 3.4%. The
mouthpiece was stored in a regular zip-lock bag after each test.
The storage was necessary because calcium chloride being
hygroscopic adsorbs water continuously from the atmosphere.
The zip-lock bag insulated the mouthpiece from the environment and allowed excessive humidity capture to be avoided,
which slowed the exhaustion of the mouthpiece.
stable signal, and fluctuations due to humidity are not observed.
After integration of the mouthpiece, real breath samples could
be analyzed for nitric oxide using the portable device as shown
in Figure 10.
Selectivity of the Desiccation Material. The desiccation
mouthpiece made of calcium chloride was tested for capture of
some other gases including acetone, carbon dioxide, nitric
oxide, and oxygen for which the mouthpiece showed a capture
efficiency of less than 5% for a 70% removal of humidity
(Figure 11). These results show that the desiccant material can
be used for analysis of these gases in conditioned breath by
suitable sensors. However, there are some gases which can be
captured by calcium chloride along with humidity. Ammonia is
known to form a complex with calcium chloride.38 10 ppmV
input ammonia reduced to 0.8 ppmV output resulting in 92%
ammonia removal efficiency of the calcium chloride mouthpiece under similar configuration.
Reusability of the Mouthpiece. Reusability of the
mouthpiece was tested for conditioning of real samples. Figure
12A shows the efficiency of desiccation with variation of 5.6%
at a mean efficiency level of 67.3% using a single mouthpiece
(23 cm length, 22 mm diameter, particle size of 1.15 mm,
porosity of 0.425) for collection of ten samples with gap of 10
CONCLUSIONS
A miniaturized mouthpiece was developed to efficiently remove
humidity and condition real breath samples in real time without
affecting target analyte concentrations. The mouthpiece
consists of packed desiccant particles in a tube. Numerical
simulation of the desiccation process was carried out by taking
into account various processes, including diffusion, mass
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transport, and water absorption, described by differential
equations with appropriate boundary conditions. The performance the mouthpiece in terms of humidity control and
backpressure minimization depends on the size and packing
density of the particles, geometry of the tube, and flow rate. On
the basis of the simulation, mouthpieces with different
configurations were built and tested, and the experimental
results validated the simulation findings. The findings provide
guidance for those who wish to design efficient sample
conditioning systems for practical chemical sensors, particularly
breath analyzers. The mouthpiece was integrated into a handheld sensor for exhaled nitric oxide detection, and the results
are in excellent agreement with gold standard methods. The
miniaturized mouthpiece has great applicability for the new
generation of portable breath analyzers, which require easy,
efficient, and reproducible removal of high humidity for
seamless device functioning.
■
AUTHOR INFORMATION
Corresponding Author
*E-mail: nongjian.tao@asu.edu.
Notes
The authors declare no competing financial interest.
ACKNOWLEDGMENTS
This work has been supported by NI BI B/NIH
(#1R21EB014219-01) through the Technologies for Health
Independent Living Program (Director: Dr. Brenda Korte). We
are thankful to collaborators Rui Wang and Di Zhao from
Center for Bioelectronics and Biosensors, Biodesign Institute,
who have contributed to this work with suggestions and ideas
about different modeling approaches and applications of use.
■
■
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