Determination of the Selectivity of Printed Wearable Sweat Sensors
Alicia Zörner
1
, Susanne Oertel
1
, Björn Schmitz
2
, Nadine
Lang
2
,
Michael P. M. Jank
1
and Lothar Frey
1
1
Fraunhofer Institute for Integrated Systems and Device Technology IISB, Schottkystr. 10, 91058 Erlangen, Germany
2
Fraunhofer Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
Keywords: Screen-printed, Biosensor, Sweat, Ammonium, Exercise, Ion-Selective Electrode, Wearable Device,
Bluetooth, Selectivity, Wearable Healthcare.
Abstract: The characterization and system integration of a fully screen-printed electrolyte biosensor is described. The
purpose of this sensor is to determine the state of fitness during sports activity by measuring the ammonium
concentration in sweat. Focusing on the selectivity of the ammonium sensor against interfering sodium and
potassium ions, the separate solution method (SSM) and the fixed interference method (FIM) are compared
on the basis of a single sensing device. The latter is mainly supported by the excellent stability of the
sensors. For both interfering ions, the FIM analysis shows a sufficient margin for the operation of the sensor
in the desired application in wearable health and fitness monitoring from sweat. The selectivity coefficients
are better than 0.01 for sodium and still better than 0.1 for potassium. SSM delivers higher selectivity in
both cases, although the discrepancies in selectivity point towards further optimization potential in the
sensor architecture or materials combination.
1 INTRODUCTION
In recent years, many efforts were carried out for the
development of printed sensors that analyze ions in
bodily fluids. In latest publications from Dam et al.,
(2015) and Matzeu et al., (2016), ion-selective
electrodes (ISEs) are used for detection of chloride
and sodium concentrations in sweat. In our previous
work (Oertel et al., 2016) we already presented a
printed sensor for analysis of ammonium in sweat.
The content of ammonium in sweat is correlated
to physical overstrain as published by Ament et al.,
(1997), Alvear-Ordenes et al., (2005) and Meyer et
al., (2007). Therefore, using a sensor for the direct
monitoring of ammonium in sweat is very attractive
for fitness as well as for healthcare applications. A
combination with other body parameters, e.g.
cardiovascular signals, improves the assessment of
the body status in sports performance diagnostics as
well as the health status for diagnostics and therapy
of diseases.
The advantage of an ion-selective electrode and
its main feature is the selectivity for a primary ion
with respect to ions interfering from the background.
For all ion-selective membranes, interfering ions that
have similar size of the ionic radius and the same
ionic charge could create a distracting potential at
the working electrode. Therefore, it is important to
determine the selectivity coefficient of the ISE.
For an ammonium-selective electrode using
nonactin as ionophore, especially sodium and
potassium can act as interfering ions. Both ions have
the same charge as ammonium and nearly the same
ionic radius. Therefore, they can affect the proper
evaluation of the ammonium level. This is even
more critical as both ions are main components of
sweat, which are active in ranges between 25 mmol/l
and 60 mmol/l for sodium and from 5 mmol/l to 18
mmol/l for potassium (Meyer et al., 2007).
For the application-oriented evaluation of the
selectivity coefficient, it is very important to
consider the tolerated values of the specific area of
interest. Furthermore, for comparison against the
results of other groups it is necessary to take into
account the determination method of the selectivity
coefficient (Bakker et al., 2000 and Egorov et al.,
2014).
Until now, other works presenting printed
ammonium-selective electrodes use only a single
method for the determination of the selectivity
˝urner A., Oertel S., Schmitz B., Lang N., Jank M. and Frey L.
Determination of the Selectivity of Printed Wearable Sweat Sensors.
DOI: 10.5220/0006296400810087
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 81-87
ISBN: 978-989-758-216-5
Copyright
c
2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
81
coefficient. For example, Guinovart et al., (2013)
utilize the separate solution method (SSM), whereas
Koncki et al., (1999) apply the fixed interference
method (FIM) for the characterization of screen-
printed ammonium-selective electrodes. The SSM is
simple, but often is not representative for a real, i.e.
containing different ions, solution. In contrast, the
FIM agrees more with the experimental conditions
(Spichiger-Keller, 1998), but may offer a limited
range of evaluation.
In this work, we directly compare the determina-
tion of the selectivity coefficients by both respective
methods and discuss the differences of the
evaluation.
Measurements employing artificial sweat are
conducted to evaluate the sensor in an environment
close to the projected application. The data
collection is based on a fully integrated electronic
system transferring the derived data wirelessly to a
mobile device.
In the experimental part of this paper, the
fabrication of the sensor as well as the
characterization methods used for determination of
the selectivity of the sensor is described. Besides,
the data acquisition and transfer is shortly presented.
In the second part of the paper, the results of the
selectivity coefficient for one sensor using both
methods are shown for each interfering ion, sodium
and potassium. Furthermore, first results measuring
artificial sweat are presented. In the followed part
the difference of the resulting selectivity coefficient
are discussed.
2 EXPERIMENTS
The fabrication of the sensor using screen printing
and two different methods for characterization of the
sensor selectivity are described. A system for data
acquisition and transfer were also developed.
2.1 Sensor Fabrication by Screen
Printing
For screen-printing of the electrodes, the same
process as described previously (Oertel et al., 2016)
were used. However, the used screen-printed pastes
were changed for optimization of the printed design.
Silver-based (Loctite ECI 1006 E&C) and carbon
filled (Electrodag PF-407C) pastes for screen
printing were now acquired from Henkel AG & Co.
KGaA (Düsseldorf, Germany). The previously used
silver-silver chloride paste with a ratio of 65:35
(125-21) for the reference electrode was acquired
from Creative Materials (Ayer, MA, USA). For the
insulating layer, a Barium titanate (PE-BT 101)
paste from Conductive Compounds was used.
The pastes were printed on flexible polyethylene
naphthalate (PEN, QA65HA, 125µm, Teijin DuPont
Films Japan Limited) and polyimide (PI, Kapton
HN, 125µm, Müller GmbH) foils.
A stabilizing layer on top of the reference
electrode (Guinovart et al., 2013) was prepared from
a mixture of sodium chloride (NaCl, 99.5%,
BioXtra), methanol (99.8%, anhydrous), and
polyvinyl butyral (PVB, Butvar® B-98, all
purchased from Sigma Aldrich).
Different mixtures of the ion-selective membrane
were tested and the best result is presented. Here, the
Ammonium ionophore (nonactin in Cocktail B,
Fluka) was obtained from Sigma Aldrich for
fabrication of the ion-selective electrode. Cocktail B
consisted of the ionophore Nonactin (3.5 wt%),
polyvinyl chloride (0.9 wt% PVC) as polymer
matrix, tetrahydrofuran (62.3 wt% THF) as solvent,
dibutyl sebacate (32.95 wt% DBS) as plasticizer,
and potassium tetrakis(4-chlorophenyl)borate
(0.35 wt% KTClPB) as anionic sites.
Analytical grade salts of ammonium chloride,
potassium chloride, and sodium chloride were
purchased from Bernd Kraft for standard calibration
solutions.
The artificial sweat (1020-1-D and 1006-1-B)
was purchased from synthetic urine. The composite-
on of this artificial sweat corresponds to DIN ISO
9022-12 and DIN ISO 105-E04 norm and contains
typical pH value of sweat and typical concentrations
of sweat ingredients like urea, lactic acid, sodium
and ammonium chlorides, acetic acid etc., in case of
1006-1-B, it is without ammonium chloride. We
used this artificial sweat by addition a defined
ammonium ion concentration of 0.1 mol/l.
For the fabrication of the sensor, an automated
screen- and pattern printer from Ekra (series X1)
was used. The silver-silver chloride paste was
printed using a polyester screen (110 µm mesh - 34
µm wire thickness x 22.5° cover angle, 10 µm -15
µm emulsion over mesh, EOM). The other pastes,
silver, carbon as well as the insulator were printed
using a stainless steel screen (VA 270-0.036x22.5°,
5-10µm EOM).
The printed layers on foils were annealed at
150°C for different times (5 minutes up to
15 minutes) on a hot plate (PZ 28-2 EZ, Harry
Gestigkeit). For potentiometric measurements, a
2636B Sourcemeter from Keithley instruments
(Cleveland, OH, USA) was used.
BIODEVICES 2017 - 10th International Conference on Biomedical Electronics and Devices
82
The printed sensors were connected to a
commercial flexible flat connector (FFC) with a
pitch of 2.54 mm with seven contacts. The sensor
layout was designed based on the typical pitches of
commercial FFCs. The FFC was placed on a printed
circuit board (PCB). The analog circuit consists of
an instrumentation amplifier (G=1) and low pass
filter (G=1). For data acquisition and transfer, a
system on a chip (SOC) including an 8051 based
low power microcontroller and RF front end is
utilized. The MCU is featured by 12 bit successive
approximation ADC. Data transfer is performed via
Bluetooth Smart (BLE). To allow wired data
transfer, an additional connector is included in the
circuitry.
The data was transferred via Bluetooth Low
Energy to an Android (Google, Mountain View,
USA) device.
The sensor design was described in previous
work by Oertel et al., (2016) and consists of a
bielectrode system that combines a silver working
electrode with a silver-silver chloride reference
electrode. A schematic and a photograph of the
sensor can be seen in Figure 1.
Figure 1: (a) Schematic and (b) photograph of screen-
printed ammonium selective potentiometric sensor with
additionally drop-casted reference and ion-selective
membranes.
The sensor was printed on flexible PEN and PI
foils. Moreover, the working electrode was coated
with an additional carbon layer between the ion-
selective membrane and the silver electrode for
achieving chemical inertness. In a final printing step,
an insulating layer with contact openings for
electrical contacts and the active area for later
deposition of the ion-selective membrane was done.
After screen-printing, the electrodes were
modified (see Figure 1a). The reference electrode
was covered with a mixture of PVB, methanol, and
NaCl as it was published by Guinovart et al., (2013).
The mixture was drop-casted and dried for about 24
hours at room temperature. Furthermore, the ion-
selective membrane was produced by drop-casting a
cocktail of ammonium ionophore onto the active
area of the working electrode. It was again dried
overnight at room temperature.
2.2 Sensor Characterization
The selectivity coefficient is derived from the
response of the sensor to different ionic analytes and
their respective activities. It can be calculated from
the Nikolsky-Eisenman equation (Umezawa et al.,
2000 and Spichiger-Keller, 1998), an extension of
the Nernstian equation (Bakker et al, 2000) that
considers the presence of an interfering ion:
log




.
log
log
(1)
with K
ij
as the selectivity coefficient, E
j
the
potential of the interfering ion j, E
i
the potential of
the primary ion i, z
i
the valency of the primary ion i,
z
j
the valency of the interfering ion j, R the universal
gas constant, T the temperature, F the Faraday
constant, a
i
the activity of the primary ion i and a
j
the activity of the interfering ion j.
Using the separate solution method (SSM), the
calibration curves of separate standard solutions of
both, the primary ion and the respective interfering
ion, are prepared with varying ion activities.
(Umezawa et al., 2000). In our case the
concentration range of the solutions was between
10
-5
mol/l (pC 5) and 10
-1
mol/l (pC 1) for each of
the ions, ammonium, sodium, and potassium. The
range refers to the typical ammonium level in sweat
before and during physical overstrain (Czarnowski
et al., 1992, Guinovart et al., 2013).
The electromotive force (EMF) of the primary
ion and the interfering ions were recorded
successively starting from low concentrations. A
current-less measurement that does not distort the
output voltage is conducted applying the high-
impedance voltage measurement mode of the 2636B
source meter delivering 10
14
ohms of internal
resistance. Each data point was collected after 7 min
of settling time. After measurement, the sensor was
rinsed with deionized water and submerged into the
subsequent standard solution.
For determination of the selectivity coefficient
via SSM, the resulting EMFs of primary ions and
interfering ions are evaluated at a certain
concentration level. At equal a
i
and a
j
, equation 1
can be reduced to






.
(2)
Determination of the Selectivity of Printed Wearable Sweat Sensors
83
For the fixed interference method (FIM) the
electromotive force for solutions of constant ion
activity of the interfering ion and varying activity of
the primary ion is measured. For the determination
of the selectivity, the EMF is plotted versus the
logarithm of the ion activity of the primary ion
(Umezawa et al., 2000). If the effect of the
interfering ion on the sensor is dominant, typically at
low concentrations of the primary ion, the latter is
completely shielded, leading to a constant EMF.
However, when the concentration of the primary ion
is increased above a critical level, the EMF should
follow the concentration of the primary ion only,
leading to a linear increase of the EMF. Fitting the
constant branch depicting the EMF of the interfering
ion and the range, where the primary ion dominates,
delivers two lines that intersect at a characteristic
concentration a
i
of the primary ion. This value refers
to the minimum detectable concentration of the
primary ion with respect to the distorted
environment, i.e. the concentration a
j
of the
interfering ion. The respective selectivity coefficient
is calculated as follows (Umezawa et al., 2000):



(3)
2.3 Automated Calibration and Data
Acquisition via Mobile Device
The printed sensor circuit is attached to a
multifunctional electronic box.
The differential sensor voltage is converted to a
ground related and filtered voltage by the analog
front end. This ground related voltage is then
acquired by the ADC of the MCU at a sampling rate
of 2 Sa/s. To lower the power consumption for data
transmission, five samples are accumulated on the
MCU’s RAM before sending the data via BLE to the
mobile device. Thus a transmission rate of 0.4 Hz is
used.
The data received by the Android mobile device
can be used for further data handling and evaluation
(see Figure 2). When a calibration for the sensor is
available, the app calculates the corresponding
concentration and visualizes a range in which the
concentration lies.
For the calibration, the sensor is submerged into
the different calibration solutions (pC 5 to pC 1) for
7 minutes each to get constant values over time
(Oertel et al., 2016). This covers the typical
ammonium range in sweat before and during
physical strain.
Figure 2: Sensor system with circuit board and data
acquisition/transfer via Bluetooth 4.0 for the concentration
of pC 3, pC 2 and pC 1.
3 RESULTS
Selectivity coefficients of the ammonium sensor
were evaluated by separate solution method (SSM)
and fixed interference method (FIM) for the
interfering ions sodium and potassium.
3.1 Selectivity against Sodium
In Figure 3 the resulting calibration curves of the
two methods are shown for sodium as disturbing ion.
In the case of SSM (Figure 3a), the EMFs for the
concentrations of 0.1 mol/l (pC 1), 0.01 mol/l (pC 2)
and 0.001 mol/l (pC 3) of both ions were used for
calculation of the selectivity coefficient according to
equation 2. The coefficient for FIM (Figure 3b) was
calculated from the interpolated minimum detectable
concentration of the primary ion with respect to the
distorted environment. This is derived from the
intersection of the previously described fits of the
constant branch related to the interfering ion and the
linear range, where the primary ion dominates (see
dotted lines in Figure 3b).
The mean selectivity coefficient obtained from
SSM K

,

is 0.0034 ± 0.0003. The
coefficient at each concentration is also observable
in Figure 3a by the inverted triangles. It is shown
that the selectivity coefficient is nearly constant in
this region of concentration. Selectivity coefficient
determined by FIM K

,

is 0.0095. The
coefficients are comparable to the results published
by Guinovart et al., (2003, K

,

0.0013) and
Koncki et al., (1999, K

,

0.0079).
BIODEVICES 2017 - 10th International Conference on Biomedical Electronics and Devices
84
In both measurements, the same sensor was used.
Despite numerous measurements at full range of
concentrations during SSM, the EMF of the constant
branch in FIM (Figure 3b) is equivalent to the EMF
of pC 2 of sodium measured by SSM. Therefore, no
additional calibration of the sensor is necessary
before FIM as there is no aging of the sensor visible.
Nevertheless, the selectivity derived by FIM shows a
slightly increased value with a difference of 0.0061.
Figure 3: Measurement of ammonia concentration upon
interference with sodium for the determination of the
selectivity coefficients using (a) SSM and (b) FIM.
3.2 Selectivity against Potassium
For the determination of the selectivity coefficient
towards potassium, the resulting calibration curves
are shown in Figures 4a and b.
With respect to the measurements shown for
sodium interference, the EMF values of standard
solutions of potassium are closer to those for
ammonium at each respective concentration. From
the SSM calibration curve (Figure 4a), a reduced
selectivity against potassium can be derived. This is
due to the ionic radius of potassium, which is closer
to that of ammonium.
The mean selectivity coefficient via SSM
K
NH
4
,K
SSM
is 0.0832 ± 0.0086 (see inverted triangles
in Figure 4a) and is almost constant for the linear
range of the concentration as it is in the case of
sodium. Here, the sensor achieved a slightly increa-
sed selectivity compared to the result of Guinovart et
al., (2000) obtaining a selectivity coefficient of
0.0158 using SSM.
Figure 4: Calibration curves for determination of the
selectivity coefficient using (a) SSM and (b) FIM for the
interfering ion potassium.
Again, the EMF of the constant branch in the
FIM measurement corresponds to the potassium
concentration of pC 2 of the SSM calibration curve.
The increased minimum detectable concentration of
the primary ion (pC 2.65) is confirming the
observation of a reduced selectivity coefficient
derived from SSM method.
The resulting selectivity determined by FIM
K

,

is 0.0912. Again, a slightly increased
Determination of the Selectivity of Printed Wearable Sweat Sensors
85
coefficient for FIM can be recognized when
compared to SSM (with a difference of 0.008).
Compared to the published work by Koncki et al.,
(1999) with a selectivity coefficient of 0.0398, our
sensor achieved a slightly increased selectivity.
3.3 Artificial Sweat
Two compositions of artificial sweat were measured
to test the selectivity of the sensor in more realistic
environments. After recording the ammonium
calibration curve, the sensor was dipped into artifi-
cial sweat with specific ammonium concentrations
of 0.065 mol/l (1020-1-D) and 0.1 mol/l (1006-1-B).
The measured EMF values of both artificial sweat
samples as well as the EMFs of 0.065 mol/l and 0.1
mol/l ammonium standard solutions are listed in
Table 1. The shift of the EMF for the ammonium
standard solution compared to the calibration curves
of the SSM is due to the long shelf time between the
measurements. However, the slope of the calibration
curve remains constant. For the measurement of the
artificial sweat, a slight shift of 40 mV (1020-1-D)
and 32 mV (1006-1-B) compared to ammonium
standard solution is observable, which is due to the
influence of the contained ions, especially the high
amount of sodium as well as the influence of the pH.
Table 1: Resulted EMFs for ammonium standard solution
and artificial sweat (1020-1-D and 1006-1-B).
EMF in mV
c in
mol/l
Ammonium
Standard
Solution
1020-1-D
1006-1-B
0.1 422 454
0.065 400 440
4 DISCUSSION
Compared to already published selectivity
coefficients of printed ammonium-selective sensors
(Guinovart et al., 2013 and Koncki et al., 1999), our
fully-printed sensor shows similar selectivity and for
the first time compares FIM to SSM selectivity data
using same devices for both techniques.
The comparison of the selectivity coefficients
determined via SSM and FIM shows for both,
sodium and potassium, a higher value using FIM
technique. This is contradictory to the theory,
because the determination of the selectivity
coefficient based on ideal Nernstian behavior should
be independent on the method (Bakker et al., 2000).
However, Figures 3a and 4a already feature a
non-ideal behavior expressed by an increase of the
EMF that is roughly +35 mV/pC higher than the
expected Nernstian slope of 59 mV in the
ammonium, sodium, and potassium standard solu-
tions. According to Bakker et al., (2000), possible
reasons for this deviation can be long-term degrada-
tion of the selective membranes or electrodes or
sample-to-sample variations.
The printed sensor in this work was tested over a
period of a few months. The repeated measurement
of calibration curves after treatment in different ion
environment showed the same slope and potentials
without shift within one month, so the sensors show
sufficient long-term stability without aging effects.
Considering the reproducibility of the calibration
curve, the fabrication of sensors after three printing
runs yields sensors with equivalent behavior. The
sensors showed also no hysteresis in the calibrations
curves of ammonium ions from low to high and
from high to low concentrations. Furthermore, the
utilization of a single sensor for both extraction
techniques rules out sample variations in this work.
Besides, further work is in progress testing the
reproducibility of the sensor selectivity prepared
with the same composition of the ion-selective
membrane. So far, the sensors show promising
results.
The stability of the reference electrode was
proven by means of cyclic voltammetry (not shown
here). By comparing cyclic voltammetry data of
fresh and used reference electrodes, no aging of the
reference electrodes could be observed.
Since most of the possible reasons for the deviant
value of the Nernstian slope are already disproved,
other reasons like effects caused by the solid-state
multilayer construction of the sensor and enhanced
diffusion of ammonium through the membrane
(Nery et al., 2016) need to be analyzed in greater
detail.
However, the measurement via FIM represents in
general more realistic conditions when aiming at
human sweat than the measurement via SSM
(Spichiger-Keller, 1998).
5 CONCLUSIONS
In this work, we reported data on the selectivity of a
fully-screen printed ammonium sensor for analysis
of human sweat against the main interfering ions,
sodium and potassium.
Within physiologically reasonable concentrations
of the species under consideration, the sensor
BIODEVICES 2017 - 10th International Conference on Biomedical Electronics and Devices
86
delivers excellent selectivity against the secondary
ions. The more application-related fixed-interference
method (FIM) yields selectivity coefficients of
0.0095 and 0.0912 against sodium and potassium,
respectively.
However, further investigations are necessary to
understand the non-ideal Nernstian behavior, which
is revealed by the difference of the selectivity
coefficients derived from FIM and the
complementary separate solution method.
Furthermore, the proper detection of ammonium
activity in artificial sweat is reported giving reason
for a viability of the approach even in harsh but
realistic environments. These results encourage
further research on the integration of sensors and
read-out electronics with textiles for wearable
functional sports clothing. Moreover, on body tests
are of particular interest for future work. Here,
considering possible motion artefacts are important.
ACKNOWLEDGEMENTS
This contribution was supported by the Bavarian
Ministry of Economic Affairs and Media, Energy
and Technology as a part of the Bavarian project
”Leistungszentrum Elektroniksysteme (LZE)”.
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