Articleshttps://doi.org/10.1038/s41587-019-0321-x
A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweatYiran Yang 1,7, Yu Song 1,2,7, Xiangjie Bo1,7, Jihong Min1, On Shun Pak3, Lailai Zhu4, Minqiang Wang1, Jiaobing Tu1, Adam Kogan1, Haixia Zhang2, Tzung K. Hsiai5, Zhaoping Li6 and Wei Gao 1*
1Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA. 2National Key Lab of Micro/Nano Fabrication Technology, Peking University, Beijing, China. 3Department of Mechanical Engineering, Santa Clara University, Santa Clara, CA, USA. 4Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA. 5Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA. 6Division of Clinical Nutrition, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA. 7These authors contributed equally: Yiran Yang, Yu Song, Xiangjie Bo. *e-mail: [email protected]
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NATure BiOTecHNOLOGY | www.nature.com/naturebiotechnology
Supplementary Fig. 1. Fabrication process of the laser-enabled patch.
Medical tape Laser-cut microfluidics PET attachment Another medical tape
PI cleaning Vector mode laser cutting Raster mode laser cutting Multimodal sensing patch
PI Chemical sensor Strain sensor Temperature sensorMedical tape PET
Supplementary Fig. 2. Vector and raster modes of the laser cutting process. a, A CO2 laser
cutting machine. b, Optical image of laser-scribed graphene patterns via vector mode and raster
mode, respectively. Scale bar, 1 cm. c,d, SEM images of vector mode-scribed graphene structure.
Scale bars represent 5 µm (c) and 2 µm (d), respectively. e,f, SEM images of raster mode-scribed
graphene structure. Scale bars represent 5 µm (e) and 2 µm (f), respectively.
a c
b d
e
f
Supplementary Fig. 3. Microscopic images showing the resolution of the laser engraving. a–
d, a graphene microelectrode fabricated by the raster mode (a), a graphene micropattern fabricated
by vector mode (b), and a microfluidic channel fabricated by vector mode under top view (c) and
cross-sectional view (d). Scale bars, 100 µm.
a b
dc
Supplementary Fig. 4. Low-cost and mass-production of the flexible multimodal sensor
arrays. Scale bar, 1 cm.
a b
Supplementary Fig. 5. Characterization of the LEG-based chemical, temperature, and strain
sensors using Raman spectroscopy. Three experiments were performed independently with
similar results.
800 1400 2000 2600 3200
Inte
nsity
(a.u
.)
Strain sensor Temperature sensor Chemical sensor
D G2D
Raman shift (cm-1)
Supplementary Fig. 6. Characterization of the LEG-based chemical, temperature, and strain
sensors using X-ray photoelectron spectroscopy (XPS). Three experiments were performed
independently with similar results.
0 200 400 600 800 1000
Strain sensor
Temperature sensor
O 1sN 1s
C 1sIn
tens
ity (a
.u.)
Binding energy (eV)
Chemical sensor
280 284 288 292
Inte
nsity
(a.u
.)
Binding energy (eV)
C 1s
Chemical sensor
Strain sensor
Temperature sensor
526 532 538 544
Strain sensor
Temperature sensor
Chemical sensorInte
nsity
(a.u
.)
O 1s
Binding energy (eV)
cba
Supplementary Fig. 7. Circuit diagram of the multiplexed sensing system. a, An FPCB next
to a quarter dollar coin. b, Circuit diagram of the voltage dividers for driving physical sensor
measurements. c, Circuit diagram of analog front-end potentiostat for voltammetric sensing. VR
and VW correspond to the reference potential and the working potential, respectively.
a b
ADC 2
VDD
Strainsensor
30 kΩ
ADC 3
VDD
Temperaturesensor
30 kΩ
cVoltage dividers
.
DAC VR
C W
R
DAC VW
ADC 1
Fourth order low pass filter Potentiostat interface Transimpedance amplifier
1.2 kΩ 16.2 kΩ
0.1 μF
10 nF
845 Ω 29.4 kΩ
0.1 μF
10 nF
0.1 μF
20 kΩ
Supplementary Fig. 8. Optimization of the LEG-based chemical sensor under different laser
parameters. Condition, 50 µM UA and 50 µM Tyr. Five experiments were performed
independently with similar results.
0.2 0.4 0.6 0.8C
urre
nt (m
A cm
-2)
Potential (V)
0.5 mA cm-2
Electrode #1
Electrode #12
Supplementary Fig. 9. Electrochemical impedance spectroscopy (EIS) for LEG-based
electrodes prepared with different laser-engraving parameters. EIS tests were performed in a
solution containing 0.2 M KCl and 5 mM [Fe(CN)6]3- at open circuit potentials with an AC
amplitude of 5 mV in the range of 0.1–1000000 Hz. All conditions were tested for once at the
same time.
0 600 1200 18000
600
1200
1800
ΩΩ
No.1 No.2 No.3 No.5 No.6 No.11
Supplementary Fig. 10. Reproducibility of the LEG-based chemical sensors for Tyr and UA
sensing. a, Continuous successive detection of UA and Tyr using an LEG-CS for 16 cycles. I and
I0 represent the peak amplitudes of the DPV plot obtained from a give scan cycle and the first cycle,
respectively. b, Batch to batch variation of the LEG-based chemical sensor performance (10
batches). I and I0 represent the peak amplitude of the DPV plot, and the average peak amplitude
obtained from the first batch, respectively. Measure of the centre is the mean value of each batch.
Error bars represent the standard deviations of the peak amplitudes measured from 16 sensors.
Conditions: 50 and 100 µM of UA and Tyr.
1 2 3 4 5 6 7 8 9 100
20
40
60
80
100
120
UA Tyr
I/I0 (
%)
BatchScan cycles1 4 7 10 13 16
0
20
40
60
80
100
120
UA Tyr
I/I0 (
%)
ba
Supplementary Fig. 11. The selectivity of the LEG-CS to UA over other analytes in human
sweat. I and I0 represent the peak amplitude of the DPV plot of an LEG-CS, and the average peak
amplitude obtained from the initial UA solution, respectively. The concentrations of the initial UA
and other analytes are based on Supplementary Table 2. UA concentration was increased by 50%
in the end. Error bars represent the standard deviations of 4 independent measurements; measure
of the centre is the mean of the 4 independent measurements.
0
50
100
150
200I/I
0 (%
)
Sample
+ Tyro
sine
+ Uric
acid
+ Vali
ne
+ Tryp
topha
n
+ Thre
onine
+ Tha
imine
+ Seri
ne
+ Ribo
flavin
+ Pyri
doxin
e
+ Phe
nylal
anine
+ Pan
tothe
tic ac
id
+ Orni
thinin
e
+ Nico
tinic
acid
+ Meth
ionine
+ Lys
ine
+ Leu
cine
+ Iso
leucin
e
+ Ino
sitol
+ Hist
idine
+ Glyc
ine
+ Glut
amic
acid
+ Foli
c acid
+ Deh
ydroa
scorb
ic ac
id
+ Crea
tinine
+ Crea
tine
+ Citru
lline
+ Cho
line c
hlorid
e
+ Asp
artic
acid
+ Asc
orbic
acid
+ Argi
nine
+ Alan
ine
Uric ai
cd
Supplementary Fig. 12. The selectivity of the LEG-CS to Tyr over other analytes in human
sweat. I and I0 represent the peak amplitude of the DPV plot of an LEG-CS, and the average peak
amplitude obtained from the initial Tyr solution, respectively. The concentrations of the initial Tyr
and other analytes are based on Supplementary Table 2. Tyr concentration was increased by 50%
in the end. Error bars represent the standard deviations of four measurements. Error bars represent
the standard deviations of 4 independent measurements; measure of the centre is the mean of the
4 independent measurements.
0
50
100
150
200I/I
0 (%
)
Sample
+ Uric
acid
+ Tyro
sine
+ Vali
ne
+ Tryp
topha
n
+ Thre
onine
+ Tha
imine
+ Seri
ne
+ Ribo
flavin
+ Pyri
doxin
e
+ Phe
nylal
anine
+ Pan
tothe
tic ac
id
+ Orni
thinin
e
+ Nico
tinic
acid
+ Meth
ionine
+ Lys
ine
+ Leu
cine
+ Iso
leucin
e
+ Ino
sitol
+ Hist
idine
+ Glyc
ine
+ Glut
amic
acid
+ Foli
c acid
+ Deh
ydroa
scorb
ic ac
id
+ Crea
tinine
+ Crea
tine
+ Citru
lline
+ Cho
line c
hlorid
e
+ Asp
artic
acid
+ Asc
orbic
acid
+ Argi
nine
+ Alan
ine
Tyrosin
e
Supplementary Fig. 13. The selectivity of the LEG-CS to Tyr over tryptophan. The DPV plots
were obtained from solutions containing physiologically relevant levels of Tyr (170 µM) and
tryptophan (20, 40 and 60 µM). The experiment was repeated 5 times independently with similar
results.
0.5 0.6 0.7 0.8 0.960
65
70
75
80
μ
Tyr μ μ μ
Supplementary Fig. 14. The dependence of sensor response on the solution pH levels. a,b, The
DPV responses (a) and the corresponding peak amplitudes (b) of the LEG-based chemical
sensors (3 mm in diameter) for UA sensing in solutions with different pHs. c,d, The DPV
responses (c) and the corresponding peak amplitudes (d) of the LEG-based chemical sensors for
Tyr sensing in solutions with different pHs. To prepare the artificial sweat samples with different
pH, lactic acid was used to adjust the pH of the PBS buffer (0.3X) solutions to physiologically
relevant pHs. The UA and Tyr concentrations are 100 and 100 µM, respectively. Throughout the
figure, five independent experiments were performed with similar results, and the mean values
were taken as measure of the centre. Error bars represent the standard deviations of measurements
from 5 sensors.
0.5 0.6 0.7 0.8 0.93
5
7
9
11 pH 4.2 pH 4.5 pH 4.9 pH 5.2 pH 5.6
Potential (V)
μ
0.2 0.3 0.4 0.50
5
10
15
20 pH 4.2 pH 4.5 pH 4.9 pH 5.2 pH 5.6
Potential (V)
μ
pH4.1 4.5 4.9 5.3 5.7
0
5
10
15
20
Cur
rent
(μ)
pH4.1 4.5 4.9 5.3 5.7
0
2
4
6
8
Cur
rent
(μ)
ba
c d
Tyr Tyr
Supplementary Fig. 15. The selectivity of the LEG-CS to lactate. Physiologically relevant
levels of lactic acid (10, 20, 30, and 40 mM) were added to 0.1 M ABS (pH 4.6) containing 50 µM
UA and 50 µM Tyr. The experiment was repeated 5 times independently with similar results.
0.1 0.3 0.5 0.7 0.930
35
40
45
50
Cur
rent
(μ)
Potential (V)
0 10 30 400
4
6
8
k(μ
)
ba
Supplementary Fig. 16. Characterization and calibration of LEG-CS using the FPCB. a,b,
DPV curves in ABS solutions containing varied UA concentrations (a) their corresponding
calibration curve (b). c,d, DPV curves of Tyr in ABS solutions containing varied Tyr
concentrations (c) and their corresponding calibration curve (d). The experiment was repeated 5
times independently with similar results.
0.35 0.40 0.45 0.50240
270
300
330
Pote
ntia
l (m
V)
Potential (V)
μμ
μ μ
μ
0.6 0.7 0.8 0.9 1.0250
275
300
325
350
Pote
ntia
l (m
V)
Potential (V)
μ μ μ
μ
0 10 20 30 400
24
48
72
Peak
am
plitu
de (m
V)
μ
0 100 200 300 400 5000
30
60
90
Peak
am
plitu
de (m
V)
μ
ba
dc
Supplementary Fig. 17. Control of the DPV scan cycle lengths by varying the scan steps.
Varied incremental potentials (4, 8, and 16 mV) were used to achieve the scan cycle lengths of 90,
45, 23 seconds. Conditions, 50 µM UA and 250 µM Tyr. The experiment was repeated 5 times
independently with similar results.
0.2 0.4 0.6 0.8100
200
300
400
500
Pote
ntia
l (m
V)
Potential (V)
90 s 45 s 23 s
Supplementary Fig. 18. Sensor performance for detection of ultra-low levels of other
electroactive compounds. a,b, the LEG electrodes show excellent sensitivity to AA (40, 80, and
120 µM) (a) and DA (0.5, 1, and 2 µM) (b). A commercial Ag/AgCl was used as the reference
electrode. The experiment was repeated 5 times independently with similar results.
-0.2 -0.1 0.0 0.1 0.20.64
0.66
0.68
0.70
0.72
0.74
Cur
rent
(mA
cm-2
)
Potential (V)
Blank μ μ μ
0.1 0.2 0.3 0.4 0.50.48
0.52
0.56
0.60
0.64
0.68
Cur
rent
(mA
cm-2
)
Potential (V)
Blank μ μμ
ba
Supplementary Fig. 19. Stability of the LEG-CS during the bending tests. a,b, The long term
stability of an LEG-based sensor during the bending test. 0–0.25 % strains were applied for both
x (a) and y (b) axis. I and I0 represent the current amplitude of the DPV plot during the bending,
and the average current amplitude without bending, respectively. The error bars represent the
standard deviation of 10 measurements, the mean of the 10 measurements is measure of the centre.
Conditions, 50 µM UA and 100 µM Tyr.
0.00 0.05 0.10 0.15 0.20 0.250
20
40
60
80
100
120
I/I0 (
%)
Strain (%)
UA Tyr
0.00 0.05 0.10 0.15 0.20 0.250
20
40
60
80
100
120
UA Tyr
I/I0 (
%)
Strain (%)
ba
x
y
x
y
Supplementary Fig. 20. Porous fiber-like graphene structure for strain sensing. a, Cross-
section SEM image of the fiber-like LEG. Scale bar, 100 µm. b, Magnified view of the fiber-like
LEG structure. Scale bar, 10 µm.
a b
Supplementary Fig. 21. Characterization of the LEG-based temperature sensors. a, Batch to
batch variation of the LEG-based temperature sensor performance (10 batches). Error bars
represent the standard deviations of 10 measurements from a temperature sensor; the mean of the
10 measurements is measure of the centre. b, The response of an LEG-based temperature sensor
to small temperature changes. The experiment was repeated 5 times independently with similar
results.
0 30 60 90 120 15098.44
98.46
98.48
98.50
98.52
kΩ
33.6 ºC
33.8 ºC
34 ºC
34.2 ºC
34.4 ºC
Temperature1 2 3 4 5 6 7 8 9 10
-0.08
-0.07
-0.06
-0.05
Sens
itivi
ty (%
°C-1
Batch
ba
Supplementary Fig. 22. Batch to batch variation of the LEG-based strain sensors prepared
with varied laser scanning speeds. Error bars represent the standard deviations of 10
measurements from a strain sensor under 0.25% strain; the mean of the 10 measurements is the
center of measure.
1 2 3 4 5 6 7 8 9 10
-10
-5
0
Batch
Supplementary Fig. 23. Heart rate (HR) monitoring using an LEG-based strain sensor. a,b,
The resistive response of an LEG-based strain sensor (a) and extracted heart rate information (b)
during long term continuous pulse monitoring on a healthy subject. c,d, The resistive responses of
the LEG-based strain sensor at 1–3 minutes and at 15–17 minutes during this test.
1.0 1.5 2.0 2.5 3.031.7
31.9
32.1
Time (min)
kΩkΩ
15.0 15.5 16.0 16.5 17.031.4
31.6
31.8
kΩ
Time (min)
0 5 10 15 2031.4
31.6
31.8
32.0
32.2kΩ
Time (min)0 5 10 15 20
55
60
65
70
75
Hea
rt ra
te (b
pm)
Time (min)
a
c d
b
Supplementary Fig. 24. On-body sensor validation using an FDA-approved vital sign
monitor during one session. a, Validation of an LEG-PS for heart rate monitoring using an
FDA-approved heart rate monitor. b, Validation of an LEG-PS for respiration rate monitoring
using an FDA-approved respiration rate monitor.
0 200 400 600 800 1000 120050
60
70
80
90
Hea
rt ra
te (b
pm)
Time (s)
Reference Strain sensor
0 200 400 600 800 1000 120010
15
20R
espi
ratio
n ra
te (b
pm)
Time (s)
Reference Strain sensor
a
b
Supplementary Fig. 25. The long term stability of the LEG-based strain sensor over one
bending session. a, The measurement was performed during a bending test of a strain sensor
(10,000 cycles with strains alternated between 0% and 0.1%). b,c, The resistive sensor response
during 910–915 cycles (b) and 9610–9615 cycles (c) of bending.
9610 9611 9612 9613 9614 961530
31
32
33
kΩCycle
910 911 912 913 914 91530
31
32
33
kΩ
Cycle
a
cb
0 2000 4000 6000 8000 1000030
31
32
33
kΩ
Cycle
Supplementary Fig. 26. Characterization and calibration of the temperature and strain
sensors using the FPCB. a, Calibration curve for the temperature sensor. b,c, Characterization in
response to pulsed strains (b) and the calibration plot of the strain sensor (c). Throughout the figure,
the experiments were repeated 3 times independently and similar results were obtained.
0.00 0.05 0.10 0.15 0.20 0.25-10
-8
-6
-4
-2
0
0 15 30 45 6029
30
31
32
33
kΩ
25 30 35 40 45 50-1.6
-1.2
-0.8
-0.4
0.0
°C
ba c
Supplementary Fig. 27. The numerical simulation showing the fluidic dynamics in the
reservoir of the lab-on-skin sensor patch. The dimension used here are based on actual sensor
design used in this work. Scale bar, 5 mm.
30 s 60 s 120 s 180 s 240 s
80
60
40
20
0
Concentration ( M)
Outlet
Supplementary Fig. 28. Inferred sweat rates from different locations of eight healthy subjects
obtained via optical image analysis. Sub., subject.
0.0
0.5
1.0
1.5
2.0
Sub. 7Sub. 6Sub. 5Sub. 4Sub. 3Sub. 1 Sub. 2 Sub. 8Fl
ow ra
te (μ
-1)
Forehead Neck
Supplementary Fig. 29. Characterization of the dynamics of the continuous microfluidic
sensing. a, Stable peak amplitudes of twelve successive DPV scans in a solution containing 80
µM UA and 200 µM Tyr with a fixed flow rate of 1 µL min-1. b, Dynamic DPV response of the
microfluidic sensing when the inflow solution was switched from 20 µM UA and 50 µM Tyr to
80 µM UA and 200 µM Tyr with a fixed flow rate of 1 µL min-1. A DPV scan was performed
every minute (with a scan step of 8 mV and a scan cycle of 45 seconds). Throughout the figure,
the experiments were repeated 3 times independently and similar results were obtained.
0.2 0.4 0.6 0.8100
135
170
205
240
Pote
ntia
l (m
V)
Potential (V)
1st scan 2nd scan 3rd scan 4th scan 5th scan 6th scan
1 3 5 7 9 11 13 1580
90
100
110
120
V/V 0
(%)
UA Tyr
Scan cycles
ba
Supplementary Fig. 30. Characterization of continuous microfluidic sensing performance
under different flow rates. a, The DPV plots of an LEG-CS for UA and Tyr sensing at different
flow rates (from 0.25 to 1.5 µL min-1). b, The peak amplitudes of UA and Tyr in 5 successive
DPV scans (2.5 minutes per scan with a 45-s scan cycle length) with different flow rates (from
0.25 to 1.5 µL min-1). Conditions, 80 µM UA and 200 µM Tyr. Throughout the figure, the
experiments were repeated 3 times independently and similar results were obtained.
0.2 0.4 0.6 0.8100
135
170
205
240
Pote
ntia
l (m
V)
Potential (V)
μ -1
μ -1
μ -1
μ -1
μ -1
μ -1
0.25 0.50 0.75 1.00 1.25 1.500
25
50
75
100
125
Tyr p
eak
ampl
itude
(mV)
Flow rate (μ -1)
1st scan 2nd scan 3rd scan 4th scan 5th scan
0
10
20
30
40
UA
peak
am
plitu
de (m
V)
ba
Supplementary Fig. 31. The DPV plots obtained by the sensor patch from a healthy subject’s
neck during the cycling exercise, in one biking session. Conditions, same as Fig. 5b.
0.2 0.4 0.6 0.8100
200
300
400
500
Pote
ntia
l (m
V)
Potential (V)
15 min 20 min 25 min 30 min 35 min
Supplementary Fig. 32. Data processing of the strain sensor involving data filtering and fast
Fourier transform (FFT) to obtain the heart rate (HR) and respiration rate (RR) values. a–
c, An example of the raw data (a), filtered data (b), and FFT extracted frequency data (c) toward
RR monitoring. The raw data was collected by a strain sensor from a healthy subject. d–f, An
example of the raw data (d), filtered data (e), and FFT extracted frequency data (f) toward HR
monitoring. HR and RR values were obtained from the frequencies that had highest intensity in
the given periods. The raw data was collected by a strain sensor from a healthy subject.
0.4 0.8 1.2 1.6 2.0
Inte
nsity
(a.u
.)
Frequency (Hz)
0 10 20 3032.6
32.8
33.0
33.2
kΩ
Time (s)0 10 20 30
32.6
32.8
33.0
33.2
kΩ
Time (s)0.2 0.3 0.4 0.5 0.6 0.7
Inte
nsity
(a.u
.)
Frequency (Hz)
0 5 10 15 2031.80
31.85
31.90
31.95
kΩ
Time (s)
ba
ed
c
f
Raw data Filtered data FFT processing
Raw data Filtered data FFT processing
0 5 10 15 2031.80
31.85
31.90
31.95
kΩ
Time (s)
Supplementary Fig. 33. The peak amplitude of DPV curves in Fig. 5c,d obtained during the
on-body UA and Tyr measurement from the neck (a) and the forehead (b) of a healthy
subject during one biking session.
15 20 25 30 350
10
20
30
UA Tyr
Peak
am
plitu
de (m
V)
Time (min)15 20 25 30 35
0
10
20
30
40
Peak
am
plitu
de (m
V)
Time (min)
UA Tyr
ba
Supplementary Fig. 34. HPLC analyses of the UA and Tyr in sweat. The experiments were
repeated 3 times independently and similar results were obtained.
0 5 10-4
0
4
8
12
Buffe
r abs
orba
nce
(mAU
)
Time (min)0 5 10
-4
0
4
8
12
Buffe
r abs
orba
nce
(mAU
)
Time (min)
0 5 10 15
0
20
40
60
80
Buffe
r abs
orba
nce
(mAU
)
Time (min)0 5 10 15
0
20
40
60
80
Buffe
r abs
orba
nce
(mAU
)
Time (min)
ba
ed
0 5 10-4
0
4
8
12
Swea
t abs
orba
nce
(mAU
)
Time (min)
0 5 10 15
0
20
40
60
80
Swea
t abs
orba
nce
(mAU
)
Time (min)
c
f
10 μ UA 15 μ UA
72 μ Tyr36 μ Tyr
Sweat sample
Sweat sample
UAUAUA
TyrTyr
Tyr
Supplementary Fig. 35. Selectivity of HPLC analyses of the UA (a) and Tyr (b) over common
sweat analytes. The concentrations of the initial UA/Tyr and added other analytes are based on
Supplementary Table 2. UA and Tyr levels were increased by 50% in the end for a and b,
respectively. Throughout this figure, the experiments were repeated 3 times independently and
similar results were obtained.
0
50
100
150
200
Nor
mal
ized
inte
nsity
(a.u
.)
Sample
+ Tyro
sine
+ Uric
acid
+ Vali
ne
+ Tryp
topha
n
+ Thre
onine
+ Tha
imine
+ Seri
ne
+ Ribo
flavin
+ Pyri
doxin
e
+ Phe
nylal
anine
+ Pan
tothe
tic ac
id
+ Orni
thinin
e
+ Nico
tinic
acid
+ Meth
ionine
+ Lys
ine
+ Leu
cine
+ Iso
leucin
e
+ Ino
sitol
+ Hist
idine
+ Glyc
ine
+ Glut
amic
acid
+ Foli
c acid
+ Deh
ydroa
scorb
ic ac
id
+ Crea
tinine
+ Crea
tine
+ Citru
lline
+ Cho
line c
hlorid
e
+ Asp
artic
acid
+ Asc
orbic
acid
+ Argi
nine
+ Alan
ine
Uric ai
cd
0
50
100
150
200
Nor
mal
ized
inte
nsity
(a.u
.)
Sample
+ Uric
acid
+ Tyro
sine
+ Vali
ne
+ Tryp
topha
n
+ Thre
onine
+ Tha
imine
+ Seri
ne
+ Ribo
flavin
+ Pyri
doxin
e
+ Phe
nylal
anine
+ Pan
tothe
tic ac
id
+ Orni
thinin
e
+ Nico
tinic
acid
+ Meth
ionine
+ Lys
ine
+ Leu
cine
+ Iso
leucin
e
+ Ino
sitol
+ Hist
idine
+ Glyc
ine
+ Glut
amic
acid
+ Foli
c acid
+ Deh
ydroa
scorb
ic ac
id
+ Crea
tinine
+ Crea
tine
+ Citru
lline
+ Cho
line c
hlorid
e
+ Asp
artic
acid
+ Asc
orbic
acid
+ Argi
nine
+ Alan
ine
Tyrosin
e
b
a
Supplementary Fig. 36. Validation of the accuracy of the LEG-CS for UA (a) and Tyr (b)
sensing using colorimetric assay kits. The data are based on 28 sweat samples collected from the
healthy subjects. UA and Tyr concentrations by the sensor were obtained from the sensor reading
and the linear fitting in Fig. 5e,f. The Pearson correlation coefficient was acquired through linear
regression.
0 50 100 150 2000
50
100
150
200
μ
μ
0 25 50 75 1000
25
50
75
100
μ
μ
ba
r = 0.965 r = 0.967
Supplementary Fig. 37. Dynamic Tyr monitoring using the sensor patch after Tyr
supplementation. The experiment was performed on a healthy subject over one iontophoresis-
induced sweat secretion process. The experiment was repeated twice independently with similar
results.
0 10 20 30 40 500
30
60
90
120
μ
0 10 20 30 40 500
5
10
15
20
μ
0 10 20 30 40 500
1
2
3
4
Flow
rate
(μ-1
ba c
Tyrosine intake
Supplementary Fig. 38. The influence of high and low protein intakes on sweat UA and Tyr.
0
40
80
120
160 Low protein High protein
μ
UA TyrSubject 2
μ
0
25
50
75
100
0
40
80
120
160 Low protein High protein
0
40
80
120
160
μ μ
UASubject 1
Tyr
ba c
0
10
20
30
40 Low protein High protein
μ
UA TyrSubject 3
0
40
80
120
160
μ
Supplementary Fig. 39. Investigation of the sweat and serum UA levels using a purine-rich
diet challenge. UA levels in sweat and serum of two heathy subjects under fasting condition, and
after a purine-rich diet on two different days. Measure of the center in a–d is the mean value of
the first two successive measurements.
0
30
60
90
120 Fasting Purine-rich
Subject 3 (male), day 1Sweat Serum
μ μ
300
400
500
600
700
0
30
60
90
120 Fasting Purine-rich
300
400
500
600
700
SweatSubject 3 (male), day 2
Serum
μμ
0
20
40
60
80 Fasting Purine-rich
μ μ
200
300
400
500
SweatSubject 4 (male), day 1
Serum0
20
40
60
80 Fasting Purine-rich
μ μ
200
300
400
500
SweatSubject 4 (male), day 2
Serum
dc
ba
Supplementary Fig. 40. HPLC analyses of the sweat (a,b) and serum (c,d) UA levels of a
healthy subject (a,c) and a patient with gout (b,d). The experiment was repeated 3 times
independently with similar results.
0 5 10
0
10
20
30
Swea
t abs
orba
nce
(mAU
)
Time (min)
0 5 10-5
0
5
10
Seru
m a
bsor
banc
e (m
AU)
Time (min)0 5 10
-5
0
5
10
Seru
m a
bsor
banc
e (m
AU)
Time (min)
0 5 10
0
10
20
30
Swea
t abs
orba
nce
(mAU
)Time (min)
ba
dc
Healthy subject Patient subject
Patient subjectHealthy subject
UAUA
UAUA
Supplementary Fig. 41. The influence of urate-lowering medication (allopurinol) on the sweat
and serum UA levels. The sweat and blood tests were performed on the same gout patient on two
days: on one day the patient received urate-lowering medication 2 hours before the test, and on the
other day the patient did not take medication 24 hours before the test. The center of measure is the
mean value of the first two successive measurements from one patient.
0
45
90
135
μ μ
W/ medication W/o medication
SerumSweat200
400
600
800
Supplementary Table 1. Optimization of the LEG-CS with different laser parameters.
Electrode # UA Peak (mA cm-2)
Optimization of chemical sensors under different laser parameters
Power (%) Speed (%) PPI Tyr Peak (mA cm-2)
1
2
3
4
5
6
7
8
9
12
11
10
6.3
6
6
6
6
5.5
6.5
7.5
6
6
6
6
5.5
5
6
7
8
5
5
5
5
5
5
5
1000
1000
1000
1000
1000
1000
1000
1000
250
1000
750
500
0.142
0.003
0.091
0.113
0.05
0
0.132
0.101
0.138
0.131
0.1
0.115
0.061
0
0.035
0.029
0.013
0
0.043
0.032
0.045
0.031
0.033
0.049
Supplementary Table 2. Sweat analytes and their concentrations used for the selectivity
studies. The values were chosen based on the mean physiological levels of the analytes46.
Constituents
Median values of human sweat constituents
Concentration (μ )
Alanine
Arginine
Ascorbic acid
Aspartic acid
Choline chloride
Citrulline
Creatine
Creatinine
Dehydroascorbic acid
Glycine
Glutamic acid
Folic acid
Histidine
Inositol
Isoleucine
Leucine
Number
1
2
3
4
5
6
7
8
9
12
11
10
13
14
15
16
360
780
10
340
50
400
15
84
11
390
370
50
520
1.6
16
210
Constituents Concentration (μ )Number
Lysine
Methionine
Nicotinic acid
Ornithine
Pantothetic acid
Phenylalanine
Pyridoxine
Riboflavin
Serine
Tryptophan
Threonine
Thiamine
Tyrosine
Uric acid
Valine
17
18
19
20
21
22
23
24
25
28
27
26
29
30
31
150
50
50
150
50
130
0.01
50
50
55
450
50
170
60
250
Supplementary Table 3. UA HPLC gradient method.
Eluent A Time (min)
Uric acid HPLC gradient method
Eluent B Flow rate Percentage eluent B (%)
0.34mL min-1
0
6
6.2
7.5
7.9
15
0
0
100
100
0
0
Acetonitrile100%
0.1%Formic acid
Supplementary Table 4. Tyr HPLC gradient method.
Eluent A Time (min)
Tyrosine HPLC gradient method
Eluent B Flow rate Percentage eluent B (%)
0.425
mL min-1
0
4
8
12
15
20
9.1
9.1
80
80
9.1
9.1
Methanol
100%
50 mM
KH2PO4-H3PO4
buffer
pH 3.0
Supplementary Note 1. The selectivity of the LEG-CS for sweat UA and Tyr sensing
Current wearable electrochemical sweat sensors are primarily focused on a limited number of
electrolytes (e.g., Na+, K+, Cl-) and metabolites (e.g., lactate and glucose) at high concentrations
(usually at mM levels) monitored via ion-selective sensors or enzymatic electrodes; biosensors
based on bioreceptors (e.g., antibodies) could be highly sensitive, but usually require multiple
washing steps and detection in standard buffer or redox solutions in order to transduce the
bioaffinity interactions.
The approach we utilize here for continuous monitoring of UA and Tyr is based on their selective
oxidation reaction at a specific potential. The selectivity is based on the oxidation peak position
for the LEG-CS. We evaluate here the selectivity of the LEG-CS over the common amino acids,
vitamins and other potential interfering chemicals at physiological relevant concentrations in
human sweat listed in Supplementary Table 2. Our electrochemical sensing data in
Supplementary Figs. 11 and 12 show that the physiological level sweat analytes have minimal
influence on the detection of UA and Tyr using the LEG-CS.
There are few electroactive molecules reported to be present in human sweat with comparable
concentrations with UA and Tyr. Dopamine is a well-known electroactive neurotransmitter present
at high concentration in cerebrospinal fluid. Our data in Fig. 2i shows that at the same conditions,
the dopamine oxidation peak appears at a more negative potential than UA and doesn’t affect the
UA monitoring. In fact, to our knowledge, there is no literature report on the presence of dopamine
in human sweat. Another electroactive molecule tryptophan has a close oxidation peak position to
Tyr. However, tryptophan is present at a much lower concentration compared to Tyr. Our
selectivity study (Supplementary Fig. 13) shows that physiologically relevant concentrations of
tryptophan do not significantly affect our Tyr measurement.
As pH is usually a key factor that could influence electrochemical sweat sensor performance, we
try to evaluate here the influence of pH on the sensor performance in artificial sweat (with pH
values adjusted by varying lactic acid as lactic acid is the main contributing factors for sweat pH).
Considering the normal sweat pH range in the literature and from our human subjects (pH ranges
from 4 to 6), the dependence of the chemical sensor performance in artificial sweat with pHs
between 4 to 6 was examined. Our results in Supplementary Fig. 14 show that, although the
oxidation peak positions slightly shift under different pHs, the peak amplitudes of UA and Tyr
sensing remain stable in the range of pH 4 to pH 6. Considering that we are essentially measuring
the peak heights, the influence of the pH is small to our sensors. Moreover, it is worth noting that
the variations of pH values of an individual’s sweat at different time points during biking session
and at different biking sessions are small.
To further validate the accuracy and selectivity of the LEG-CS, the sensor readings in human sweat
samples have been validated with two current gold standards – high performance liquid
chromatography (HPLC) (Fig. 5e,f and Supplementary Fig. 35) and commercial colorimetric
assay kits (Supplementary Fig. 36). Very high linear coefficients (0.963 and 0.965 for UA and
Tyr with HPLC, 0.965 and 0.967 for UA and Tyr with the commercial colorimetric assays) were
found, revealing the great reliability and accuracy of our sensor technology toward sweat UA and
Tyr sensing.