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Novel Gas Sensor Arrays Based on High-Q SAM-Modified Piezotransduced Single-Crystal Silicon Bulk Acoustic Resonators

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This paper demonstrates a novel micro-size (120 µm × 200 µm) piezoelectric gas sensor based on a piezotransduced single-crystal silicon bulk acoustic resonator (PSBAR). The PSBARs operate at 102 MHz and possess high Q values (about 2000), ensuring the stability of the measurement. A corresponding gas sensor array is fabricated by integrating three different self-assembled monolayers (SAMs) modified PSBARs. The limit of detection (LOD) for ethanol vapor is demonstrated to be as low as 25 ppm with a sensitivity of about 1.5 Hz/ppm. Two sets of identification code bars based on the sensitivities and the adsorption energy constants are utilized to successfully discriminate isopropanol (IPA), ethanol, hexane and heptane vapors at low and high gas partial pressures, respectively. The proposed sensor array shows the potential to form a portable electronic nose system for volatile organic compound (VOC) differentiation.
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sensors
Article
Novel Gas Sensor Arrays Based on High-Q
SAM-Modified Piezotransduced Single-Crystal
Silicon Bulk Acoustic Resonators
Yuan Zhao , Qingrui Yang , Ye Chang, Wei Pang, Hao Zhang and Xuexin Duan *
State Key Laboratory of Precision Measuring Technology & Instruments, Tianjin University, Tianjin 300072,
China; zhaoyuan@tju.edu.cn (Y.Z.); yangqingrui@tju.edu.cn (Q.Y.); cy0803@tju.edu.cn (Y.C.);
weipang@tju.edu.cn (W.P.); haozhang@tju.edu.cn (H.Z.)
*Correspondence: xduan@tju.edu.cn; Tel.: +86-022-2740-1002
The authors contributed equally to this work.
Received: 4 April 2017; Accepted: 14 June 2017; Published: 26 June 2017
Abstract:
This paper demonstrates a novel micro-size (120
µ
m
×
200
µ
m) piezoelectric gas sensor
based on a piezotransduced single-crystal silicon bulk acoustic resonator (PSBAR). The PSBARs
operate at 102 MHz and possess high Qvalues (about 2000), ensuring the stability of the measurement.
A corresponding gas sensor array is fabricated by integrating three different self-assembled
monolayers (SAMs) modified PSBARs. The limit of detection (LOD) for ethanol vapor is demonstrated
to be as low as 25 ppm with a sensitivity of about 1.5 Hz/ppm. Two sets of identification code
bars based on the sensitivities and the adsorption energy constants are utilized to successfully
discriminate isopropanol (IPA), ethanol, hexane and heptane vapors at low and high gas partial
pressures, respectively. The proposed sensor array shows the potential to form a portable electronic
nose system for volatile organic compound (VOC) differentiation.
Keywords:
e-nose system; piezotransduced single-crystal silicon bulk acoustic resonators; gas
sensing; MEMS
1. Introduction
Volatile organic compounds (VOCs) are hazardous materials that have proven to have negative
effects on the environment and human health. Concurrently, as sensitive biochemical markers,
VOCs are widely used as analytes in the realm of environment protection [
1
], food testing [
2
4
],
early diagnosis [
5
9
], and so forth. A successful platform for VOC detections is the electronic nose
(e-nose) system [
10
,
11
] which consists of several sensors modified with different gas-sensitive materials.
Numerous gas sensor types have been demonstrated to meet various measurement requirements.
Gas sensors based on the metal oxide semiconductor possess a wide range of target gases with
satisfactory sensitivity and selectivity, which makes them the most commonly used gas sensors [
12
].
Nano-size gas sensors, such as carbon nanotubes and graphene, can detect ultra-low concentrations
of vapors due to their high surface-area-to-volume ratio [
13
,
14
]. Optical gas sensors benefit from
the high fidelity of optical signals, making them suitable for remote detections [
15
]. Microwave gas
sensors are emerging as cheap and label-free techniques, and the lack of selectivity can be overcome
by combining with highly selective materials [
16
20
]. Among different types of gas sensors, MEMS
piezoelectric gas sensors, such as surface acoustic wave (SAW) resonators [
21
], Lamb wave resonators
(LWR) [
22
] and film bulk acoustic resonators (FBAR) [
23
25
] have triggered a lot research interest due
to their low power consumption, micrometer-scaled sizes, and relatively high sensitivities. Compared
with quartz crystal microbalance (QCM), however, they suffer from relatively low Qvalues, which
may result in poor limit of detection (LOD), large phase noise, and instability when integrating with
oscillating circuits.
Sensors 2017,17, 1507; doi:10.3390/s17071507 www.mdpi.com/journal/sensors
Sensors 2017,17, 1507 2 of 12
As a special acoustic device, the piezotransduced silicon bulk acoustic resonator (PSBAR) not
only inherits the characteristics of MEMS acoustic wave devices, but exhibits significant advantages in
the aspect of superior Qvalue due to the existence of the single-crystal silicon substrate. Because of
their high Qfactor and relatively low motional resistance, PSBARs have been used as critical elements
in high-performance oscillators [
26
]. Moreover, studies regarding their biochemical sensing capability
in liquid environment have been reported [
27
]. However, the investigations of PSBARs for gas sensing
applications are less covered.
In this work, we designed and fabricated high performance PSBARs operating at the first and
third order width-extensional mode (WE mode) with Qvalues up to 12,000 and 2000, respectively.
Three kinds of self-assemble monolayers (SAMs), including (3-glycidyloxypropyl) trimethoxysilane
(GPTES), trimethoxy (octadecyl) silane (OTES), and (3-bromopropyl) trichlorosilane (BPTS) are
functionalized onto the surface of the PSBARs to compose a novel gas sensor array. The comparative
detections of the two modes towards low concentration ethanol vapors demonstrate the superior LOD.
Finally, the PSBAR array is used to successfully discriminate ethanol, IPA, heptane and hexane at low
and high gas partial pressures, showing a great potential as a promising method for VOCs detections.
2. Materials and Methods
2.1. Materials
(3-glycidyloxypropyl) trimethoxysilane (GPTES), trimethoxy (octadecyl) silane (OTES) and
(3-bromopropyl) trichlorosilane (BPTS) are purchased from Aladdin Industrial Corporation (Shanghai,
China) without further purification. VOCs (ethanol, IPA, heptane and hexane) utilized in this work are
purchased from Tianjin Real & Lead Chemical Corporation and the purity all reached HPLC.
2.2. PSBAR Fabrication
PSBARs are fabricated using SOI wafer by a standard semiconductor processing flow. The device
layer of the SOI wafer is 25
µ
m n-type low-resistivity, single-crystal silicon. To fabricate PSBARs,
0.2
µ
m molybdenum (Mo), 1
µ
m c-axis oriented aluminum nitride (AlN) and 0.2
µ
m molybdenum (Mo)
were deposited and patterned as bottom electrodes, piezoelectric layer and top electrodes, respectively.
Bottom cavities and the trenches on both sides of a resonator were fabricated by means of the DRIE
process. 0.3
µ
m gold (Au) pads were fabricated using lift-off process. A buried silicon oxide layer was
released by a BOE solution to suspend the silicon block in the last step. The length of the resonator
was aligned along <110> crystal orientation. The detailed schematic of the PSBAR fabrication is shown
in Supplementary Figure S1.
2.3. Device Functionalization
To form OTES and BPTS membranes on the surface, PSBARs were rinsed using deionized (DI)
water followed by drying in nitrogen. The devices were oxidized in air plasma for 5 min with
plasma cleaner (YZD08-2C, SAOT, Beijing, China), and silanization was accomplished by vapor phase
deposition of a silylating reagent in a low-pressure heated chamber (YES-LabKote, Yield Engineering
Systems, Livermore, CA, USA). The functionalization process of GPTES was the same with OTES and
BPTS apart from further reaction with aqueous ethanolamine solutions (20%) for 2 h to form hydroxyl
membrane. All the devices were preserved in the nitrogen environment (e.g., glove box) to protect
SAMs from oxidation and hydrolysis damage.
2.4. Surface Characterization
The characterization of different SAMs were employed by contact angle measurement (JC2000DM,
Zhongchen, China). As shown in Supplement Figure S3, the contact angle of the bare silicon substrate
is 35.25
. After being functionalized with OTES, GPTES and BPTS, the contact angles increase to
73.17
, 63.04
and 92.56
respectively, which means SAMs were successfully coated. The contact
Sensors 2017,17, 1507 3 of 12
angles of OTES- and BPTS-modified surfaces are larger than GPTES-modified surface due to the higher
hydrophobicity of the terminated chemical groups.
2.5. VOC Detection Setup
The VOCs detection setup consists of a dual-line VOC generation system and a frequency record
system, as shown in Figure 1a. In the VOC generation system, an organic solution was added into a
bubbler, and pure carrier nitrogen gas was guided into the liquid to generate saturated VOC vapors.
Then, VOC vapors with different ratios of partial pressures to saturated vapor pressure (P/P
0
) were
realized by diluting the saturated vapor using pure nitrogen in another channel. The real-time flow
velocity was monitored by mass flow controllers (MFC, 5850e, Brooks, Hatfield, PA, USA) through a
computer program. The VOC vapors were guided to an evaluation board with functionalized PSBARs
wire-bonded onto it. The board was epoxied with two plastic cavities in order to confine VOC vapors,
as shown in Supplementary Figure S4. A VOC absorber was placed behind the evaluation board to
prevent the diffusion of harmful VOCs. In the frequency record system, a vector network analyzer
(VNA, E5071C, Agilent, Santa Clara, CA, USA) was connected to the evaluation board. The two-port
S-parameter data of each PSBAR were recorded by a program.
Sensors 2017, 17, 1507 3 of 12
2.4. Surface Characterization
The characterization of different SAMs were employed by contact angle measurement
(JC2000DM, Zhongchen, China). As shown in Supplement Figure S3, the contact angle of the bare
silicon substrate is 35.25°. After being functionalized with OTES, GPTES and BPTS, the contact angles
increase to 73.17°, 63.04° and 92.56° respectively, which means SAMs were successfully coated. The
contact angles of OTES- and BPTS-modified surfaces are larger than GPTES-modified surface due to
the higher hydrophobicity of the terminated chemical groups.
2.5. VOC Detection Setup
The VOCs detection setup consists of a dual-line VOC generation system and a frequency record
system, as shown in Figure 1a. In the VOC generation system, an organic solution was added into a
bubbler, and pure carrier nitrogen gas was guided into the liquid to generate saturated VOC vapors.
Then, VOC vapors with different ratios of partial pressures to saturated vapor pressure (P/P0) were
realized by diluting the saturated vapor using pure nitrogen in another channel. The real-time flow
velocity was monitored by mass flow controllers (MFC, 5850e, Brooks, Hatfield, PA, USA) through a
computer program. The VOC vapors were guided to an evaluation board with functionalized
PSBARs wire-bonded onto it. The board was epoxied with two plastic cavities in order to confine
VOC vapors, as shown in Supplementary Figure S4. A VOC absorber was placed behind the
evaluation board to prevent the diffusion of harmful VOCs. In the frequency record system, a vector
network analyzer (VNA, E5071C, Agilent, Santa Clara, CA, USA) was connected to the evaluation
board. The two-port S-parameter data of each PSBAR were recorded by a program.
(a)
(b) (c)
Figure 1. (a) Schematic of the gas sensing setup and a piezotransduced silicon bulk acoustic resonator
(PSBAR) sensor array; (b) Schematic of the PSBAR structure; (c) An optical microscope graph of a
PSBAR 120 μm in width and 200 μm in length.
2.6. Finite Element Analysis Model
Due to the symmetry of the PSBAR structure, a quarter of a 3D model was constructed to reduce
the consumption of calculation resources, as shown in Supplementary Figure S2. The piezoelectric
transducer, the anisotropic single crystal silicon block, and the centrally located tether were built up.
The support tether was clamped with perfect match layer (PML) to simulate the adsorption of
acoustic waves by the silicon substrate.
Figure 1.
(
a
) Schematic of the gas sensing setup and a piezotransduced silicon bulk acoustic resonator
(PSBAR) sensor array; (
b
) Schematic of the PSBAR structure; (
c
) An optical microscope graph of a
PSBAR 120 µm in width and 200 µm in length.
2.6. Finite Element Analysis Model
Due to the symmetry of the PSBAR structure, a quarter of a 3D model was constructed to reduce
the consumption of calculation resources, as shown in Supplementary Figure S2. The piezoelectric
transducer, the anisotropic single crystal silicon block, and the centrally located tether were built up.
The support tether was clamped with perfect match layer (PML) to simulate the adsorption of acoustic
waves by the silicon substrate.
Sensors 2017,17, 1507 4 of 12
2.7. Principal Component Analysis
PCA is a robust pattern recognition tool for classification of multivariate data. It provides an
efficient approach to reduce the dimensionality of a data matrix. The first two eigenvalues of the data
matrix are calculated as new coordinate bases, which are called the first principal component (PC1)
and the second principal component (PC2).
3. Results and Discussions
3.1. PSBAR Performance Simulations and Device Selections
A PSBAR comprises two parts: a sandwich-form transducer and an attached suspending
single-crystal silicon substrate. The transducer consists of a thin-film piezoelectric layer, top and
bottom metallic electrodes, as shown in Figure 1b,c. When stimulated by an alternating voltage,
the AlN layer produces alternating stress due to its piezoelectric effect, which leads to mechanical
waves propagating in the silicon substrate. Owing to the finite size, the mechanical waves form
standing waves at special stimulating frequencies, which results in resonant peaks in the frequency
spectrum. When gas molecules are absorbed on the device surface, the resonant peaks shift downwards
due to the mass loading effect. The relation between the absorbed mass and the frequency shifts can
be described by the Sauerbrey equation [28] as following:
f=2f2
0
Aµe f f ρ
m=2f2
0
Aρva,e f f
m, (1)
where
fdenotes the measured frequency shift; f
0
is the intrinsic resonant frequency of each mode;
mis the mass change; Ais the effective sensing area;
µeff
is the effective Young’s modulus of
the resonator along the direction of acoustic wave propagation;
ρ
is the density of the material.
Alternatively, the equation can be written as a function of v
a,eff
(effective acoustic phase velocity).
Therefore, by detecting the frequency shifts, the amount of adsorbed gas can be extracted.
A vital parameter for a mass sensor is the LOD, which is closely related to the minimum detectable
resonant frequency change (
f
min
).
f
min
is influenced by multiple factors, such as resonator Qvalues,
sensing membranes, ambient environment conditions, and the system noise of the measurement
equipment. In practice, the
f
min
can be calculated in terms of Qand minimum detectable phase shift
of impedance (φmin) as follows:
fmin =φmin
2Q. (2)
Hence, a high Qfactor can reduce the LOD of the sensor. Moreover, when integrating sensors
with measurement circuits, a high Qfactor can reduce the noise and enhance the stability. Therefore,
to get better sensing performance, a high Qvalue of the PSBAR is desired.
In order to determine the optimum size of PSBAR sensors, a finite element analysis model was
built up. The width of the PSBAR model is 120
µ
m. By sweeping the length of the PSBAR model, a set
of Qfactors of the first and third order WE mode can be calculated, as shown in Figure 2.
It shows that the Qfactor of the first order WE mode reaches maximum (12,927) when the length
is 200
µ
m. Although 320
µ
m length PSBARs possess the highest Q(6129) of the third order WE mode,
the Qvalue of its first order WE mode is rather low. Therefore, the 200
µ
m length PSBAR is preferable.
To verify the simulation results, PSBARs with different length were fabricated and their statistical
Qvalues are plotted in the same figure. The variation trend of the statistical Qvalues is in accordance
with simulation results except that they are slightly smaller than the theoretical calculations, which is
due to the loss of the materials, lattice defect, and electrode resistivity in practice.
Three PSBARs (200
µ
m in length) with similar performances were selected to compose a gas
sensor array. Their performances are shown in Supplementary Figure S5. The operating frequencies
of the first order WE mode are about 35.6 MHz, and the frequencies of the third order WE mode are
Sensors 2017,17, 1507 5 of 12
around 102 MHz. The Qvalues of the first order WE mode are larger than 12,000, and the Qvalues
of the third order WE mode are around 2000. The high Qfactors ensure their outstanding detection
capability when exposed to VOCs.
Sensors 2017, 17, 1507 5 of 12
around 102 MHz. The Q values of the first order WE mode are larger than 12,000, and the Q values
of the third order WE mode are around 2000. The high Q factors ensure their outstanding detection
capability when exposed to VOCs.
(a) (b)
Figure 2. Simulated and measured Q values of PSBARs with different length. The width is 120 μm. Q
values of (a) first order width-extensional mode (WE mode) and (b) third order WE mode. The
triangle markers represent simulation results. The insets are the displacements of PSBARs at the first
and third order WE modes.
3.2. Comparative Detections of Low-Concentration Ethanol Vapor
In order to compare the sensing capability of the first and third order WE modes, a PSBAR
modified with BPTS was used to detect low concentration ethanol vapor. A 2000 ppm standard
ethanol gas was prepared and connected to the VOC channel. By diluting the standard ethanol gas
with pure nitrogen, 500 ppm, 250 ppm, 125 ppm, 50 ppm and 25 ppm ethanol gases were generated
and detected sequentially with the PSBAR sensor. The real-time sensing results are shown in
Figure 3a.
(a) (b)
Figure 3. (a) Real-time responses of the first and third order WE modes of the trimethoxy (octadecyl)
silane (OTES)-modified PSBAR to low-concentration ethanol vapors; (b) Sensitivities of the two
sensing modes. R2 is the correlation coefficient.
The results show that, when nitrogen is guided to the sensor, the resonant frequencies reach
stable baselines. When the sensor is exposed to ethanol vapors, the resonant frequencies decreases
immediately, indicating a quick adsorption of ethanol molecules. After flushing with nitrogen, the
resonant frequencies recover rapidly, indicating the full desorption of ethanol molecules. The fast
adsorption and desorption processes demonstrate the good repeatability and stability of PSBAR
sensors.
The frequency shifts of the third order WE mode are always larger than that of the first order
mode because of the higher working frequency, which is in accordance with Equation (1). When the
sensor is exposed to 25 ppm ethanol gas, the resonant frequency of the third order WE mode still
Figure 2.
Simulated and measured Qvalues of PSBARs with different length. The width is 120
µ
m.
Qvalues of (
a
) first order width-extensional mode (WE mode) and (
b
) third order WE mode.
The triangle markers represent simulation results. The insets are the displacements of PSBARs at
the first and third order WE modes.
3.2. Comparative Detections of Low-Concentration Ethanol Vapor
In order to compare the sensing capability of the first and third order WE modes, a PSBAR
modified with BPTS was used to detect low concentration ethanol vapor. A 2000 ppm standard ethanol
gas was prepared and connected to the VOC channel. By diluting the standard ethanol gas with pure
nitrogen, 500 ppm, 250 ppm, 125 ppm, 50 ppm and 25 ppm ethanol gases were generated and detected
sequentially with the PSBAR sensor. The real-time sensing results are shown in Figure 3a.
Sensors 2017, 17, 1507 5 of 12
around 102 MHz. The Q values of the first order WE mode are larger than 12,000, and the Q values
of the third order WE mode are around 2000. The high Q factors ensure their outstanding detection
capability when exposed to VOCs.
(a) (b)
Figure 2. Simulated and measured Q values of PSBARs with different length. The width is 120 μm. Q
values of (a) first order width-extensional mode (WE mode) and (b) third order WE mode. The
triangle markers represent simulation results. The insets are the displacements of PSBARs at the first
and third order WE modes.
3.2. Comparative Detections of Low-Concentration Ethanol Vapor
In order to compare the sensing capability of the first and third order WE modes, a PSBAR
modified with BPTS was used to detect low concentration ethanol vapor. A 2000 ppm standard
ethanol gas was prepared and connected to the VOC channel. By diluting the standard ethanol gas
with pure nitrogen, 500 ppm, 250 ppm, 125 ppm, 50 ppm and 25 ppm ethanol gases were generated
and detected sequentially with the PSBAR sensor. The real-time sensing results are shown in
Figure 3a.
(a) (b)
Figure 3. (a) Real-time responses of the first and third order WE modes of the trimethoxy (octadecyl)
silane (OTES)-modified PSBAR to low-concentration ethanol vapors; (b) Sensitivities of the two
sensing modes. R2 is the correlation coefficient.
The results show that, when nitrogen is guided to the sensor, the resonant frequencies reach
stable baselines. When the sensor is exposed to ethanol vapors, the resonant frequencies decreases
immediately, indicating a quick adsorption of ethanol molecules. After flushing with nitrogen, the
resonant frequencies recover rapidly, indicating the full desorption of ethanol molecules. The fast
adsorption and desorption processes demonstrate the good repeatability and stability of PSBAR
sensors.
The frequency shifts of the third order WE mode are always larger than that of the first order
mode because of the higher working frequency, which is in accordance with Equation (1). When the
sensor is exposed to 25 ppm ethanol gas, the resonant frequency of the third order WE mode still
Figure 3.
(
a
) Real-time responses of the first and third order WE modes of the trimethoxy (octadecyl)
silane (OTES)-modified PSBAR to low-concentration ethanol vapors; (
b
) Sensitivities of the two sensing
modes. R2is the correlation coefficient.
The results show that, when nitrogen is guided to the sensor, the resonant frequencies reach
stable baselines. When the sensor is exposed to ethanol vapors, the resonant frequencies decreases
immediately, indicating a quick adsorption of ethanol molecules. After flushing with nitrogen,
the resonant frequencies recover rapidly, indicating the full desorption of ethanol molecules.
The fast adsorption and desorption processes demonstrate the good repeatability and stability of
PSBAR sensors.
Sensors 2017,17, 1507 6 of 12
The frequency shifts of the third order WE mode are always larger than that of the first order
mode because of the higher working frequency, which is in accordance with Equation (1). When the
sensor is exposed to 25 ppm ethanol gas, the resonant frequency of the third order WE mode still
decreases by 46 Hz, while the response of the first order WE mode is hardly to be discerned. This is
mainly due to the fact that, although the first order WE mode possesses higher Qvalue, the responses
are limited by the resolution of the VNA. The third order WE mode, however, can still be detected due
to the higher sensitivity. To further investigate the sensitivity of each mode, the frequency shifts versus
concentrations are depicted in Figure 3b. It shows that the third order WE mode has a sensitivity about
1.52 Hz/ppm, which is almost three times higher than that of the first order WE mode. Therefore,
the third order WE mode is used as sensing mode in the following VOC detections.
3.3. Discriminations for Different VOCs at Low Gas Partial Pressures
To realize the VOC differentiations, the three selected PSBARs were modified with OTES, GPTES
and BPTS, respectively, to form a gas sensor array. The sensor array was exposed to four kinds of
VOCs (ethanol, IPA, heptane, hexane) with gas partial pressures varying from 0.05 to 0.01. Figure 4
shows real-time frequency responses of the PSBAR sensor array. It is intuitive to note that different
SAM-modified PSBARs have different responses towards each VOC, which mainly results from the
discrepancies of the amphipathicity between VOCs molecules and the three SAMs. For polar VOCs
(ethanol and IPA), the OTES-modified PSBAR shows maximum responses at about 5.3 kHz and 5.8 kHz,
respectively, under 0.05 gas partial pressure, while the maximum frequency shifts of OTES-modified
PSBAR to nonpolar vapors (hexane and heptane) are only 2.4 kHz and 3.1 kHz, respectively, which
means OTES has higher adsorption volume to polar vapors at low gas partial pressures.
Figure 4.
Real-time responses of the PSBAR gas sensor array to (
a
) ethanol, (
b
) IPA, (
c
) heptane and
(d) hexane.
Sensors 2017,17, 1507 7 of 12
To calculate the sensitivity of each PSBAR and generate the code bars for VOC differentiation,
the concentrations of VOC vapors in parts per million (ppm) were calculated by the following equation:
C(ppm) = 106×(Psf/P(f+F)), (3)
where fand Fare the flow rates (in sccm) of saturated VOCs and dilution nitrogen, respectively; Pis
the standard atmospheric pressure (760 mmHg). P
S
is the saturated partial vapor pressure obtained
using the Antoine equation [29]:
log Ps=AB
t+C, (4)
where t(C) is the measurement temperature. A,B,Care empirical coefficient related to the detected
vapors. By referring to the chemical handbook, the Ps of ethanol, IPA, heptane and hexane are
calculated to be 36.48, 33.44, 36.48, and 121.6 mmHg, respectively. Therefore, the concentrations under
0.05 gas partial pressure of ethanol, IPA, heptane, and hexane are 2950, 2200, 2400, and 8000 ppm,
respectively. The sensitivities of PSBARs to four VOCs can be depicted as Figure 5.
Sensors 2017, 17, 1507 7 of 12
6
()10(/( ))
s
Cppm Pf Pf F=× +, (3)
where f and F are the flow rates (in sccm) of saturated VOCs and dilution nitrogen, respectively; P is
the standard atmospheric pressure (760 mmHg). PS is the saturated partial vapor pressure obtained
using the Antoine equation [29]:
log s
B
PA
tC
=−
+, (4)
where t (°C) is the measurement temperature. A, B, C are empirical coefficient related to the detected
vapors. By referring to the chemical handbook, the Ps of ethanol, IPA, heptane and hexane are
calculated to be 36.48, 33.44, 36.48, and 121.6 mmHg, respectively. Therefore, the concentrations
under 0.05 gas partial pressure of ethanol, IPA, heptane, and hexane are 2950, 2200, 2400,
and 8000 ppm, respectively. The sensitivities of PSBARs to four VOCs can be depicted as Figure 5.
(a) (b)
(c) (d)
Figure 5. Linear fitting of sensitivities of three self-assembled monolayers (SAM)-modified PSBARs
to (a) ethanol, (b) IPA, (c) heptane and (d) hexane. R2 is the correlation coefficient.
Least square method is used to linearly fit the data. Figure 5 shows that the sensitivities of three
SAM-modified PSBARs to each VOC are distinctive from each other, which represents three non-
redundant variables. As a result, the sensitivities can be used to form identification code bars for VOC
differentiations. The code bars for four VOCs are shown in Figure 6a.
It clearly shows that the code bars for four VOCs have obvious dissimilarity. For polar vapors
(ethanol and IPA), the sensitivity of OTES-modified device is the highest, which is in agreement with
the real-time sensing results. Furthermore, ethanol and IPA can be differentiated by comparing the
magnitude of sensitivities of BPTS- and GPTES-modified sensors: if the sensitivity of GPTES-
modified PSBAR is larger, the analyte is IPA, otherwise, it is ethanol. Although the code bars of
hexane and IPA share the similar pattern, the differences between sensitivities of OTES- and BPTS-
modified sensors can still be used to realize the differentiation. For heptane, the maximum response
occurs at GPTES-modified sensor. Hence, it is the most recognizable vapor among detected VOCs.
0 500 1000 1500 2000 2500
4000
1000
5000
Concentration (pp m)
Frequency Shift (Hz)
03000 3500
2000
3000
6000
Ethanol
OTES
GPTES
BPTS
0 500 1000 1500 2000 2500
4000
1000
5000
Concentration (ppm)
Frequency Shift (Hz)
0
2000
3000
6000
7000
IPA
OTES
GPTES
BPTS
0 500 1000 1500 2000 2500
2000
500
2500
Concentration (pp m)
Frequency Shift (Hz)
03000
1000
1500
3000
Heptane
OTES
GPTES
BPTS
0 1000 200 0 3000 4000 5000
2000
500
2500
Concentration (ppm)
Frequency Sh ift (Hz)
06000
1000
1500
3000
7000 8000 9000
3500
Hexane
OTES
GPTES
BPTS
Figure 5.
Linear fitting of sensitivities of three self-assembled monolayers (SAM)-modified PSBARs to
(a) ethanol, (b) IPA, (c) heptane and (d) hexane. R2is the correlation coefficient.
Least square method is used to linearly fit the data. Figure 5shows that the sensitivities of
three SAM-modified PSBARs to each VOC are distinctive from each other, which represents three
non-redundant variables. As a result, the sensitivities can be used to form identification code bars for
VOC differentiations. The code bars for four VOCs are shown in Figure 6a.
It clearly shows that the code bars for four VOCs have obvious dissimilarity. For polar vapors
(ethanol and IPA), the sensitivity of OTES-modified device is the highest, which is in agreement with
the real-time sensing results. Furthermore, ethanol and IPA can be differentiated by comparing the
magnitude of sensitivities of BPTS- and GPTES-modified sensors: if the sensitivity of GPTES-modified
PSBAR is larger, the analyte is IPA, otherwise, it is ethanol. Although the code bars of hexane and
IPA share the similar pattern, the differences between sensitivities of OTES- and BPTS-modified
Sensors 2017,17, 1507 8 of 12
sensors can still be used to realize the differentiation. For heptane, the maximum response occurs at
GPTES-modified sensor. Hence, it is the most recognizable vapor among detected VOCs.
Sensors 2017, 17, 1507 8 of 12
(a) (b)
Figure 6. (a) Identification code bars for detected volatile organic compounds (VOCs); (b) Score plots
of detected VOCs calculated by Principal Component Analysis (PCA) method.
In order to quantitatively assess the discriminations towards different VOCs, Principal
Component Analysis (PCA) algorithm was applied to process the data. A 21 × 3 data matrix is built
up as shown in Supplementary Table S1. The row variables are four VOC species under five gas
partial pressures, and the column variables are the three SAMs. Zeros are added to the last row in
order to represent the blank responses. The transformation results are plotted in Figure 6b.
The black point in the figure is the blank responses. The results show that the four different
vapors form individual response directions, which means the PSBAR sensor array successfully
differentiates between the four VOCs. Besides, the data points of each VOC arrange in a linear format
from 0.01 to 0.05 gas partial pressures and radiate from the blank point, illustrating the superior
linearity of the PSBAR sensor array. In short, the code bars and PCA results prove the preferable
discrimination capability and linearity of PSBAR sensor array for VOC sensing at low gas partial
pressures.
3.4. Differentiations for Different VOCs at High Gas Partial Pressures
As demonstrated above, the sensitivity-based code bars can successfully differentiate between
VOCs within a narrow range of gas partial pressures. When differentiating between VOCs within a
large range of gas partial pressures, however, such code bars are ineffective due to the nonlinearity
of the PSBAR responses. Thus, to differentiate between VOCs at high gas partial pressures,
concentration-independent code bars are desired. Here, we use the fitting results from the adsorption
isotherms to generate the unique concentration-independent code bars for detected VOCs.
Figure 7 shows the real-time responses of the PSBAR array to four VOCs (ethanol, IPA, heptane
and hexane). It clearly shows that the adsorption and desorption of VOCs on the SAM-modified
PSBAR array are reversible processes, even at high gas partial pressures. Moreover, with the increase
of the gas partial pressures, the amount of the VOCs’ adsorptions grows. Among the three SAM-
modified sensors, OTES-modified PSBAR possesses the highest magnitude of responses when gas
partial pressures are greater than 0.2, which may result from the longer chain length of OTES
molecules. This effect is not obvious at low gas partial pressure due to the relatively low
concentrations. With the increase of gas partial pressures, however, chain length becomes a dominant
factor, which, together with the amphipathicity between VOC molecules and modified SAMs,
ultimately contributes to the disparate responses of the three sensors. Moreover, it seems that when
gas partial pressures are low, the adsorption responses do not follow exponential patterns. This might
be due to the flow fluctuations of the gas sensing setup. The response time and recovery time were
defined as the time required to change the frequency after exposure to VOCs or nitrogen in a specific
range of 90%, as illustrated in Figure S7a. At 0.8 gas partial pressure, GPTES-modified PSBAR exhibits
the shortest response time, while BPTS-modified PSBAR owns the longest response time. All the
response and recovery times for VOCs at 0.8 gas partial pressures are given in Table S2. Additionally,
during the measurement, the Q values of the PSBARs have relatively small fluctuations as shown in
Figure S8, which ensures the high performance when integrating with oscillator circuits.
Figure 6.
(
a
) Identification code bars for detected volatile organic compounds (VOCs); (
b
) Score plots
of detected VOCs calculated by Principal Component Analysis (PCA) method.
In order to quantitatively assess the discriminations towards different VOCs, Principal Component
Analysis (PCA) algorithm was applied to process the data. A 21
×
3 data matrix is built up as shown
in Supplementary Table S1. The row variables are four VOC species under five gas partial pressures,
and the column variables are the three SAMs. Zeros are added to the last row in order to represent the
blank responses. The transformation results are plotted in Figure 6b.
The black point in the figure is the blank responses. The results show that the four different vapors
form individual response directions, which means the PSBAR sensor array successfully differentiates
between the four VOCs. Besides, the data points of each VOC arrange in a linear format from 0.01
to 0.05 gas partial pressures and radiate from the blank point, illustrating the superior linearity of
the PSBAR sensor array. In short, the code bars and PCA results prove the preferable discrimination
capability and linearity of PSBAR sensor array for VOC sensing at low gas partial pressures.
3.4. Differentiations for Different VOCs at High Gas Partial Pressures
As demonstrated above, the sensitivity-based code bars can successfully differentiate between
VOCs within a narrow range of gas partial pressures. When differentiating between VOCs within a
large range of gas partial pressures, however, such code bars are ineffective due to the nonlinearity
of the PSBAR responses. Thus, to differentiate between VOCs at high gas partial pressures,
concentration-independent code bars are desired. Here, we use the fitting results from the adsorption
isotherms to generate the unique concentration-independent code bars for detected VOCs.
Figure 7shows the real-time responses of the PSBAR array to four VOCs (ethanol, IPA, heptane
and hexane). It clearly shows that the adsorption and desorption of VOCs on the SAM-modified
PSBAR array are reversible processes, even at high gas partial pressures. Moreover, with the increase of
the gas partial pressures, the amount of the VOCs’ adsorptions grows. Among the three SAM-modified
sensors, OTES-modified PSBAR possesses the highest magnitude of responses when gas partial
pressures are greater than 0.2, which may result from the longer chain length of OTES molecules.
This effect is not obvious at low gas partial pressure due to the relatively low concentrations. With the
increase of gas partial pressures, however, chain length becomes a dominant factor, which, together
with the amphipathicity between VOC molecules and modified SAMs, ultimately contributes to the
disparate responses of the three sensors. Moreover, it seems that when gas partial pressures are low,
the adsorption responses do not follow exponential patterns. This might be due to the flow fluctuations
of the gas sensing setup. The response time and recovery time were defined as the time required to
change the frequency after exposure to VOCs or nitrogen in a specific range of 90%, as illustrated in
Figure S7a. At 0.8 gas partial pressure, GPTES-modified PSBAR exhibits the shortest response time,
while BPTS-modified PSBAR owns the longest response time. All the response and recovery times for
Sensors 2017,17, 1507 9 of 12
VOCs at 0.8 gas partial pressures are given in Table S2. Additionally, during the measurement, the Q
values of the PSBARs have relatively small fluctuations as shown in Figure S8, which ensures the high
performance when integrating with oscillator circuits.
Sensors 2017, 17, 1507 9 of 12
(a) (b)
(c) (d)
Figure 7. Real-time responses of the PSBAR array for detections of (a) ethanol, (b) IPA, (c) heptane
and (d) hexane with gas partial pressures varying from 0.1 to 0.8.
The adsorption isotherms of each VOC on different sensors can be obtained according to the
frequency shifts at different partial pressures, as shown in Figure 8. It shows that the adsorptions of
different VOCs fit different adsorption types according to their polarities, which is particularly
obvious on the OTES-modified PSBAR. Brunauer-Emmett-Teller (BET) formula with finite adsorbed
layers is used to fit the adsorption isotherms, which is the typical model of multilayer gas physical
adsorption:
1
1
1( 1)
11(1)
nn
m
n
vcx nxnx
fv
x
cxcx
+
+
−+ +
Δ∝ =
−+, (4)
where v is the total gas volume adsorbed; Δf is the frequency shift of each mode, which is linearly
proportional to v; vm is the monomolecular layer adsorption capacity; x is the gas partial pressure; c
is the adsorption energy constant; and n is the maximum number of layers that can be reached. In the
BET model, the constant c describes the adsorption energy difference between the first layer and the
subsequent layers, which is approximately given by
1
()/
L
qq RT
ce
, (5)
where q1 is the heat of adsorption in the first layer on the surface, which represents the interaction
force between the SAMs and VOC molecules. While the qL is the condensation heat of subsequent
layers, which represents the interaction forces between the VOC molecules. The fitting curves of four
VOCs are shown in Figure 8.
0 1020304050
10
0
-10
-20
-30
Time (min)
Frequency Shift (kHz)
-40
60 70 80 90
-50
-60
-70
Ethanol
-80
-90
-100
N
2
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
N
2
N
2
N
2
N
2
N
2
N
2
N
2
0 1020304050
10
0
-10
-20
-30
Time (min)
Frequency Shift (kHz)
-40
60 70 80 90
-50
-60
-70
IPA
-80
-90
-100
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
N
2
N
2
N
2
N
2
N
2
N
2
N
2
N
2
0 1020304050
10
0
-10
-20
-30
Time (min)
Frequency Shift (kHz)
-40
60 70 80 90
-50
-60
-70
Heptane
-80
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
N
2
N
2
N
2
N
2
N
2
N
2
N
2
N
2
0 1020304050
10
0
-10
-20
-30
Time (min)
Frequency Shift (kHz)
-40
60 70 80 90
-50
-60
-70
Hexane
-80
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
N2
N2
N2
N2
N2
N2
N2
N2
Figure 7.
Real-time responses of the PSBAR array for detections of (
a
) ethanol, (
b
) IPA, (
c
) heptane and
(d) hexane with gas partial pressures varying from 0.1 to 0.8.
The adsorption isotherms of each VOC on different sensors can be obtained according to the
frequency shifts at different partial pressures, as shown in Figure 8. It shows that the adsorptions of
different VOCs fit different adsorption types according to their polarities, which is particularly obvious
on the OTES-modified PSBAR. Brunauer-Emmett-Teller (BET) formula with finite adsorbed layers is
used to fit the adsorption isotherms, which is the typical model of multilayer gas physical adsorption:
fv=vmcx
1x·1(n+1)xn+nxn+1
1+ (c1)xcxn+1, (5)
where vis the total gas volume adsorbed;
fis the frequency shift of each mode, which is linearly
proportional to v;v
m
is the monomolecular layer adsorption capacity; xis the gas partial pressure; cis
the adsorption energy constant; and nis the maximum number of layers that can be reached. In the
BET model, the constant cdescribes the adsorption energy difference between the first layer and the
subsequent layers, which is approximately given by
ce(q1qL)/RT, (6)
where q
1
is the heat of adsorption in the first layer on the surface, which represents the interaction
force between the SAMs and VOC molecules. While the q
L
is the condensation heat of subsequent
layers, which represents the interaction forces between the VOC molecules. The fitting curves of four
VOCs are shown in Figure 8.
Sensors 2017,17, 1507 10 of 12
Sensors 2017, 17, 1507 10 of 12
(a) (b)
(c) (d)
Figure 8. Adsorption isotherms of VOCs: (a) ethanol, (b) IPA, (c) heptane and (d) hexane.
After extracting the c values of each isotherms, concentration-independent code bars for four
VOCs can be depicted as Figure 9. It shows that when detected by OTES-modified PSBAR, c values
of polar VOCs (ethanol and IPA) are larger than 1, suggesting that the q1 is much greater than the qL.
For nonpolar VOCs (heptane and hexane), q1 is closed to qL making c values approximate 1. The
difference is likely due to the fact that interactions between polar molecules and OTES monolayer are
larger than that between nonpolar molecules and OTES monolayer. It results that the adsorbed gas
molecules increased quickly at low gas partial pressure (typically below 0.1) in adsorption isotherms
of polar VOCs, as shown in Figure 8. The concentration-independent code bars for four VOCs are
distinctive, which means by simply diluting an unknown VOC target, the absorption isotherms can
be obtained and the code bars based on the c can be constructed to realize VOC differentiations.
Figure 9. Concentration-independent code bars.
4. Conclusions
In this work, high-Q PSBARs modified with SAMs are applied as high-performance gas sensors.
The influence of length-width ratios on Q values is discussed to obtain the optimum size for the
PSBAR sensor. The detection of 25 ppm ethanol vapor is realized by the third order WE mode of an
OTES-modified PSBAR. A gas sensor array consists of three PSBARs functionalized with three SAMs
0 0.1 0.2 0.3 0.4 0.5
80
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
Ethanol
OTES
BPTS
GPTES
IPA
0 0.1 0.2 0.3 0.4 0.5
80
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
OTES
BPTS
GPTES
Heptane
0 0.1 0.2 0.3 0.4 0.5
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
OTES
BPTS
GPTES
Hexane
0 0.1 0.2 0.3 0.4 0.5
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
OTES
BPTS
GPTES
GPTESOTES BPTS
0
2
4
6
8
10
12
Ethanol IPA Heptane Hexane
C
Figure 8. Adsorption isotherms of VOCs: (a) ethanol, (b) IPA, (c) heptane and (d) hexane.
After extracting the cvalues of each isotherms, concentration-independent code bars for four
VOCs can be depicted as Figure 9. It shows that when detected by OTES-modified PSBAR, cvalues
of polar VOCs (ethanol and IPA) are larger than 1, suggesting that the q
1
is much greater than the
q
L.
For nonpolar VOCs (heptane and hexane), q
1
is closed to q
L
making c values approximate 1.
The difference is likely due to the fact that interactions between polar molecules and OTES monolayer
are larger than that between nonpolar molecules and OTES monolayer. It results that the adsorbed gas
molecules increased quickly at low gas partial pressure (typically below 0.1) in adsorption isotherms
of polar VOCs, as shown in Figure 8. The concentration-independent code bars for four VOCs are
distinctive, which means by simply diluting an unknown VOC target, the absorption isotherms can be
obtained and the code bars based on the ccan be constructed to realize VOC differentiations.
Sensors 2017, 17, 1507 10 of 12
(a) (b)
(c) (d)
Figure 8. Adsorption isotherms of VOCs: (a) ethanol, (b) IPA, (c) heptane and (d) hexane.
After extracting the c values of each isotherms, concentration-independent code bars for four
VOCs can be depicted as Figure 9. It shows that when detected by OTES-modified PSBAR, c values
of polar VOCs (ethanol and IPA) are larger than 1, suggesting that the q1 is much greater than the qL.
For nonpolar VOCs (heptane and hexane), q1 is closed to qL making c values approximate 1. The
difference is likely due to the fact that interactions between polar molecules and OTES monolayer are
larger than that between nonpolar molecules and OTES monolayer. It results that the adsorbed gas
molecules increased quickly at low gas partial pressure (typically below 0.1) in adsorption isotherms
of polar VOCs, as shown in Figure 8. The concentration-independent code bars for four VOCs are
distinctive, which means by simply diluting an unknown VOC target, the absorption isotherms can
be obtained and the code bars based on the c can be constructed to realize VOC differentiations.
Figure 9. Concentration-independent code bars.
4. Conclusions
In this work, high-Q PSBARs modified with SAMs are applied as high-performance gas sensors.
The influence of length-width ratios on Q values is discussed to obtain the optimum size for the
PSBAR sensor. The detection of 25 ppm ethanol vapor is realized by the third order WE mode of an
OTES-modified PSBAR. A gas sensor array consists of three PSBARs functionalized with three SAMs
0 0.1 0.2 0.3 0.4 0.5
80
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
Ethanol
OTES
BPTS
GPTES
IPA
0 0.1 0.2 0.3 0.4 0.5
80
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
OTES
BPTS
GPTES
Heptane
0 0.1 0.2 0.3 0.4 0.5
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
OTES
BPTS
GPTES
Hexane
0 0.1 0.2 0.3 0.4 0.5
70
60
50
40
P/P0
Frequency Shift (kHz)
30
0.6 0.7 0.8 0.9
20
10
0
OTES
BPTS
GPTES
GPTESOTES BPTS
0
2
4
6
8
10
12
Ethanol IPA Heptane Hexane
C
Figure 9. Concentration-independent code bars.
Sensors 2017,17, 1507 11 of 12
4. Conclusions
In this work, high-QPSBARs modified with SAMs are applied as high-performance gas sensors.
The influence of length-width ratios on Qvalues is discussed to obtain the optimum size for the
PSBAR sensor. The detection of 25 ppm ethanol vapor is realized by the third order WE mode of an
OTES-modified PSBAR. A gas sensor array consists of three PSBARs functionalized with three SAMs
(OTES, BPTS and GPTES) has been fabricated. By means of extracting the different sensitivities and
adsorption energy constant of PSBARs towards different VOCs at low and high gas partial pressures,
unique identification code bars for VOCs discriminations can be obtained. Four VOCs (ethanol, IPA,
hexane and heptane) have been successfully differentiated, demonstrating SAM-modified PSBAR
sensors as promising candidates in VOC detections.
Supplementary Materials:
The following are available online at http://www.mdpi.com/1424-8220/17/7/1507/s1,
Figure S1: schematic of the PSBAR fabrication process flow, Figure S2: finite element model to simulate the Q
values of different size PSBARs, Figure S3: contact angles of four kinds of interfaces, Figure S4: assembled PSBAR
evaluation board, Figure S5: electrical performances of the first and third order WE mode of the three selected
PSBARs used in the e-nose system, Figure S6: SEM picture of a side wall of a PSBAR, , Figure S7: Adsorption and
desorption response time for (a) ethanol, (b) IPA, (c) heptane and (d) hexane at 0.8 gas partial pressure, Figure S8:
Qvariations when detecting IPA at gas partial pressures from 0.1 to 0.5, Table S1: frequency shifts matrix for PCA
transformation, Table S2: Adsorption and desorption response time at 0.8 gas partial pressure.
Acknowledgments:
The authors gratefully acknowledge financial support from the Natural Science Foundation
of China (NSFC No. 61674114), Tianjin Applied Basic Research and Advanced Technology (14JCYBJC41500),
and the 111 Project (B07014).
Author Contributions:
Xuexin Duan conceived and supervised the project. Yuan Zhao and Qingrui Yang
designed the experiments and wrote the manuscript. Yuan Zhao, Qingrui Yang and Ye Chang performed the
experiments. Hao Zhang and Wei Pang fabricated the device. All authors discussed the results and commented
on the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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Chem. C 2011,115, 8466–8474. [CrossRef]
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2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
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... Sn x S y films should be prepared by low-cost techniques such as solution processes to further reduce the production cost of TFPV devices. The preparation of Sn x S y thin films via chemical methods, especially by chemical bath deposition(CBD), includes a slightly [4][5][6][7][8][9][10][11][12]. Solar cell (o-SnS, c-SnS, Sn 2 S 3 as light absorber and SnS 2 as buffer); photodetector (o-SnS, c-SnS, Sn 2 S 3 as light absorber and SnS 2 as buffer); Li-and Na-ion batteries (o-SnS, c-SnS, and SnS 2 as anode materials); gas-and bio sensors (o-SnS, c-SnS, and SnS 2 as sensing materials); tunnel field-effect transistors (TFET) (o-SnS, c-SnS, and SnS 2 as top or back gates); electrochemical and super capacitors (o-SnS, c-SnS, and SnS 2 as electrode materials); capacitor; thermoelectrics (o-SnS, c-SnS, and Sn 2 S 3 as grids); and water-splitting (o-SnS, c-SnS, and SnS 2 as photocathodes). ...
... Applications of SnxSy[4][5][6][7][8][9][10][11][12]. Solar cell (o-SnS, c-SnS, Sn2S3 as light absorber and SnS2 as buffer); photodetector (o-SnS, c-SnS, Sn2S3 as light absorber and SnS2 as buffer); Li-and Na-ion batteries (o-SnS, c-SnS, and SnS2 as anode materials); gas-and bio sensors (o-SnS, c-SnS, and SnS2 as sensing materials); tunnel field-effect transistors (TFET) (o-SnS, c-SnS, and SnS2 as top or back gates); electrochemical and super capacitors (o-SnS, c-SnS, and SnS2 as electrode materials); capacitor; thermoelectrics (o-SnS, c-SnS, and Sn2S3 as grids); and water-splitting (o-SnS, c-SnS, and SnS2 as photocathodes). ...
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The rapid research progress in tin-based binary sulfides (SnxSy = o-SnS, c-SnS, SnS2, and Sn2S3) by the solution process has opened a new path not only for photovoltaics to generate clean energy at ultra-low costs but also for photocatalytic and thermoelectric applications. Fascinated by their prosperous developments, a fundamental understanding of the SnxSy thin film growth with respect to the deposition parameters is necessary to enhance the film quality and device performance. Therefore, the present review article initially delivers all-inclusive information such as structural characteristics, optical characteristics, and electrical characteristics of SnxSy. Next, an overview of the chemical bath deposition of SnxSy thin films and the influence of each deposition parameter on the growth and physical properties of SnxSy are interestingly outlined.
... This unique characteristic, combined with compatible manufacturing process with ICs, has rendered LVR a viable candidate for future integrated resonant sensors with miniaturized dimensions, high performance and low power consumption. Over the last two decades, a significant number of high-performance resonant sensors based on LVRs have been demonstrated, such as chemical sensors [16,17], thermal detectors [9,18], infrared sensors [19], biological sensors [20,21], inertial sensors [22,23], magnetometers [24,25] and miniaturized acoustic antennas [26]. ...
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