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ArticleDiscriminating BTX Molecules by the Nonselective Metal Oxide
Sensor-Based Smart Sensing System
Hongyu Liu, Gang Meng,* Zanhong Deng, Kazuki Nagashima, Shimao Wang, Tiantian Dai, Liang Li,*
Takeshi Yanagida, and Xiaodong Fang*

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sı Supporting Information*

ABSTRACT: Discriminating structurally similar volatile organic compounds (VOCs)
molecules, such as benzene, toluene, and three xylene isomers (BTX), remains a signiﬁcantchallenge, especially, for metal oxide semiconductor (MOS) sensors, in which selectivity isa long-standing challenge. Recent progress indicates that temperature modulation of asingle MOS sensor oﬀers a powerful route in extracting the features of adsorbed gas
analytes than conventional isothermal operation. Herein, a rectangular heating waveform isapplied on NiO-, WO3-, and SnO2-based sensors to gradually activate the speciﬁc gas/oxideinterfacial redox reaction and generate rich (electrical) features of adsorbed BTX molecules.
Upon several signal preprocessing steps, the intrinsic feature of BTX molecules can beextracted by the linear discrimination analysis (LDA) or convolutional neural network(CNN) analysis. The combination of three distinct MOS sensors noticeably beneﬁts therecognition accuracy (with a reduced number of training iterations). Finally, a prototype ofa smart BTX recognition system (including sensing electronics, sensors, Wi-Fi module, UI,
PC, etc.) based on temperature modulation has been explored, which enables a prompt, accurate, and stable identiﬁcation of xylene
isomers in the ambient air background and raises the hope of innovating the future advanced machine olfactory system.
KEYWORDS: BTX molecules, xylene isomer classiﬁcation, temperature modulation, deep learning algorithm, smart sensing system

Bforthcoming trillion sensor society calls for smart sensing
systems, which could not only promptly warn the risk of
exceeding PEL concentrations but also help to identify thepollution source, with the assistance of Internet of Things(IoT). In this regard, semiconductor (carbon nanotubes, metal
sulﬁde andoxides) sensors5,7−9 outperform the other kinds ofportable sensors (optical waveguide/ﬂuorescence,10,11 quartz
crystal microbalance (QCM)12), in terms of excellent siliconchip compatibility, direct electrical readout, ultralow cost anddimension.13 Nevertheless, till now, it remains a signiﬁcantchallenge to discriminate ﬁve BTX molecules by semiconductor (as well as other portable) sensors, arising fromboth intrinsically poor selectivity of semiconductor sensors andquite similar physical properties (including kinetic diameter,14
ionization potential,15 dipole moment, etc.) of BTX molecules,
as listed in Table S1.
Semiconductor sensors were usually operated at an elevated(isothermal) temperature to observe a clear resistance/
conductance variation upon exposure to target molecules;

TX, referring to benzene, toluene, and three xylene
isomers (including m-xylene, o-xylene, and p-xylene), arevery important petrochemical agents used as either raw
material or solvent. With the accelerated consumption of
BTX aromatic hydrocarbons in the printing, paint, rubber,polymer, leather, and pharmaceuticals industries, as well as
rapid popularization of vehicles, a large amount of BTX-based
volatile organic compounds (VOCs) have been emitted into
the environments and constitute one kind of most alarming
indoor and outdoor air pollution sources.1,2 Apart from mosthazardous benzene (class 1 human carcinogen), exposure totoluene and xylene also severely impairs the human blood
production process and nervous system, depending on the
exposure concentration and time. The permissible exposure
limit (PEL), recommended by US National Institute for
Occupational Safety and Health, for benzene, toluene, andxylene as the 8 h time-weighted average (TWA) is 0.1, 100,
and 100 ppm, respectively.3 The toxicity eﬀects (as well as
applications) of BTX closely relate with their slight structural
diﬀerence, even for xylene isomers.4,5 Thus, exploring a facile
distinction methodology is of utmost importance.
Though gas chromatography−mass spectrometry (GC-MS)or gas chromatography-ﬂame ionization detector (GC-FID)
enables the discrimination of BTX species, high cost, loweﬃciency of these bulk instruments, and high requirement forthe operators hinder the widespread applications.6 The© XXXX American Chemical Society

Received: August 11, 2021
Accepted: October 22, 2021

A

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ArticleFigure 1. Isothermal responses of NiO, WO3, and SnO2 sensors to o-xylene, m-xylene, p-xylene, toluene, and benzene vapors. Variations of (a)NiO, (b) WO3, and (c) SnO2 sensors response upon successive exposure to various BTX vapors (30−80 ppm) at a working temperature of 250 °C.
(d−f) Histogram and (g−i) radar plots of NiO, WO3, and SnO2 sensor sensitivity to 100 ppm various BTX vapors, as a function of the working
temperature ranging from 100 to 300 °C.

isothermal sensitivity was typically extracted as one “feature” oftarget molecules. Resembling with face/ﬁngerprint recognition,
extracting suﬃcient features of target molecules from electricalresponse signals is necessary.16,17 Modifying the surface
properties of sensors by doping,18,19 decoration,8,20 or
hybridization oﬀers an apparent approach of expanding
features of target molecules by constructing sensor arrays.21−24
Wang et al. reported an eﬃcient approach to extract moleculefeatures by analyzing multiple ﬁeld-eﬀect transistor (FET)
characteristics of an ambipolar diketopyrrolopyrrole (DPP)
copolymer sensor, and discrimination of three xylene isomers(a ﬁxed concentration of 40 ppm in the inert N2 background)
was ﬁrst demonstrated.5 However, insuﬃcient stabilities in the
structure (i.e., swelling) and electrical properties of DPP in theambient air atmosphere hinder its practical applications.25 A
robust smart sensing system enabling fast identiﬁcation of BTX
molecules in an air atmosphere is still lacking.
Instead of limited features extracted from isothermal(resistance or FET) measurements,6,8,11,26 transient response
signals from a temperature-modulated sensor were adopted.27−29 As the binding states of diﬀerent adsorbed moleculeson the speciﬁc facets of a sensor surface are diﬀerent andpossess unique thermodynamic characteristics, the temperature-modulated transient signals probably contain both
unique thermodynamic and kinetic information of adsorbed
molecules. Herein, a rectangular heating waveform (from 150
to 250 °C) was applied on generic, nonselective MOS (NiO-,

WO3-, and commercial SnO2-based TGS 2602) sensors.
Through necessary signal preprocessing steps, the intrinsic
features of exposure gas analytes can be extracted either by thelinear discrimination analysis (LDA) or convolutional neuralnetwork (CNN) analysis. By virtue of the distinct surface
chemical properties of three oxides, the entire sensors possess
better discrimination capability (suggested by the LDA) andfast training convergence characteristics (revealed by CNN).
Finally, a smart sensing system, including a compact temperature modulation and resistance reading module (6 × 6 cm2),
Wi-Fi module, user interface (UI), and PC server, has been
constructed, which enables a prompt (∼5 s) discrimination of
xylene isomer vapors with the same molecular weight andfunctional group. Moreover, owing to the excellent stability ofthese MOS sensors in the ambient air atmosphere below 250°C, the present thermally modulated sensing electronics
exhibit robust discrimination capability (a slight shift of baseresistance does not account for the shift of a feature space).
This work highlights that appropriate thermal modulationoﬀers a powerful route in extracting the feature diﬀerences ofhighly similar molecules like BTX, including xylene isomers,
and the recent advancement in deep learning algorithms in thearea of artiﬁcial intelligence (AI) could substantially empower
the recognition capability of nonselective semiconductor
sensors. The combination of two strengths promises theinnovation of intelligent molecule recognition chips for the
future machine olfaction.
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RESULTS AND DISCUSSION
The TEM images of as-synthesized NiO and WO3 products
are shown in Figure S1a,b, and both materials reveal
homogeneous nanoparticles with diﬀerent size ranges. The
grain size of NiO nanoparticles was 21.3 ± 6.0 nm (inset of
Figure S1a) and that of WO3 nanoparticles was 43.4 ± 12.5
nm (inset of Figure S1b). SAED (inset of Figure S1c,d) andEDS (Figure S1e,f) characterization conﬁrms the pure cubic
phase of NiO nanoparticles and the monoclinic phase of WO3nanoparticles, respectively. To compare the electrical responsesof NiO, WO3, and SnO2 sensors to ﬁve BTX vapors and gain
an insight into the diﬀerences of BTX molecules/MOS
interfacial charge interaction, the isothermal response characteristics are ﬁrst investigated. The successive sensitivity (Rgas/
Rair for p-type NiO, Rair/Rgas for n-type WO3 and SnO2)
response and recovery curves of the three sensors to the tested
BTX vapors (30−80 ppm) at an operating temperature of 250°C are shown in Figure 1a−c. All of the MOS sensors show thecross-sensitivity to o-xylene, m-xylene, p-xylene, toluene, andbenzene vapors, and their sensitivity response curves exhibit asimilar response proﬁle, except for the benzene response of the
SnO2 sensor. The appearance of oscillation at an elevatedoperating temperature (250 °C) probably arises from the local
temperature increase of the SnO2 sensor induced byexothermic oxidation of exposure to benzene.28 The three
sensors exhibit diﬀerential sensitivities and response kinetics,
which should be due to the distinct surface chemical propertiesof the sensing material. As shown in Figure 1a−c, thesensitivity response values of NiO, WO3, and SnO2 sensors invarious BTX atmospheres range from 1.08 to 1.76, 1.21 to
7.90, and 2.41 to 6.00, respectively, which suggest thenonspeciﬁc nature of present sensors.
Because the resistance (R) of the sensor gradually varies(increase for NiO, decrease for WO3 and SnO2) with theconcentration or partial pressure (P) variation of BTX gas, thesensor sensitivity usually increases gradually with the increaseof gas concentration. The sensitivity response follows thepower law, and its constant relates to the interaction mode
between the target gas and the reactive oxygen species on the
MOS surface. The p-type NiO sensor possesses lower
sensitivity than WO3 and SnO2 sensors because of its low
carrier mobility and hole accumulation conﬁguration.26 For allof the sensors, the operating temperature plays an importantrole in modulating the electrical responses, as shown by thetemperature-dependent responses to 100 ppm BTX vapors in
Figure 1d−f. The response of the sensor typically increases ﬁrst
and then decreases with the increase of the operating
temperature, as shown in Figure 1d. The main reason forthis phenomenon is that the rate constant (kR) of the reaction
between the gas and the adsorbed oxygen increases
exponentially with the increase of temperature, while thechange of the Knudsen diﬀusion coeﬃcient (DK) of the target
gas is sublinear. The rate constant kR is less than DK in thelower temperature region and greater than DK in the higher
temperature region. With the increase of temperature, the gas
inside the porous sensing layer is consumed in large quantities.
The eﬀective partial pressure (Pin) of gas inside the material issigniﬁcantly lower than that outside (Pout), and the actual
sensitivity response decreases with increasing temperature athigh temperatures. Therefore, the aperture size and thediﬀusion length of the MOS are also factors that aﬀect thegas sensitivity response in the high-temperature region. In

Articleaddition, the adsorption oxygen content of the MOS surface
decreased with the increase of temperature. When theconcentration (partial pressure) of the target gas is too high,the adsorption oxygen is consumed and the gas response
decreases with the increase of temperature. In summary, thesensitivity at diﬀerent operating temperatures is aﬀected not
only by the type of target gas and MOS but also by the device
structure, sensing material morphology and surface reactive
oxygen species. The diﬀerent temperature-dependent response
characteristics in the radar plot (Figure 1g−i) suggest thediﬀerences in terms of BTX molecular diﬀusion, molecularadsorption, and high-temperature activation at diﬀerent
operating temperatures. The formula of the Knudsen diﬀusion
coeﬃcient (DK) of gas is as follows (r ̅ is the aperture, T is thetemperature, and MA is the molecular weight of the gas)
DK = 9700 r ̅

T
MA

(1)

which is diﬀerent for benzene, toluene, and xylene with
diﬀerent molecular weights. Meanwhile, overcoming activationenergy (Ea) is the premise for the redox process on the surfacesof MOS and gas. The formula of activation energy is as follows(Ns is the density of negative surface charge, Nd is the densityof positive charge, εr is the dielectric constant of thesemiconductor, ε0 is the dielectric constant of gas, and q is
the basic charge)
Ea =

q2Ns2
2εrε0Nd(2)

The chemical reaction rate (kR) is closely related to theactivation energy (the lower the activation energy, the fasterthe reaction rate). The formula of the reaction rate constant kR
is as follows (R is the gas constant, T is the temperature, and A
is the pre-exponential factor)
kR = Ae−Ea / RT(3)

According to Table S1, the dielectric constant of BTX gases
is diﬀerent, and the redox rate constant kR of diﬀerent BTX
gases on the MOS surface is diﬀerent. By changing theoperation temperature of the sensor, the features of temperature-dependent rate constants such as the rate constant (kR)of the redox reaction and the Knudsen diﬀusion coeﬃcient(DK) of the gas can be determined to show a promising avenue
in the exploration of BTX molecule discrimination.
The parameters of thermal modulation, such as waveform,
range, and period, play a crucial role on the amount ofmolecular information contained in the dynamic response
curves. A simple rectangular heating wave (red line in Figure2) is applied to achieve the fastest recognition at a moderate
operation condition. Considering low sensitivity at low
temperature and larger energy consumption at high temperatures of the sensors (Figure 1d−f), the temperature of thesensor was modulated from 150 to 250 °C by increasing theheating voltage. Note that there is a thin ceramic insulator (1.5
× 1.5 × 0.25 mm3) between the top MOS sensing layer andbottom heating layer, and the temperature of the top sensing
layer gradually increases to 250 °C (to gradually activate thesurface redox reaction). Using the inherent temperaturecoeﬃcient of resistance/resistivity (TCR) characteristics ofthe MOS channel, the transient resistance of the three sensors
under the rectangular wave heating waveform was monitored
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Article(Figure 4a−c), overlapping of features occurs for all of theindividual sensors, for example, partial overlapping of benzene
and toluene for the NiO sensor, mixing of benzene and toluene
or o-xylene and m-xylene for the WO3 sensor, and overlappingof m-xylene, p-xylene, and toluene for the SnO2 sensor. Basedon the 70 sets of validation responses recorded at the parallel
independent experimental sessions for all BTX vapors (ﬁlled
markers in Figure 4), the accuracy of NiO, WO3, and SnO2
sensors in the identiﬁcation of BTX vapors is 97.14, 90.00, and97.14%, respectively. The discrepancy of recognition capability
agrees with the fact that MOS sensors belong to chemicalsensors, in which the electrical response arises from the speciﬁc(temperature-dependent) surface redox reactions at the gas/
MOS interface. A slight variation of the sensing material may
degrade the discrimination capability even using thermal
modulation, as indicated by the results of SnO2-based TGS
2600, 2602, and 2603 (Figure S5). The selection of theappropriate sensing material toward the speciﬁc target analyte
is another interesting work beyond the scope of this work.
Enlarging the number of distinct MOS sensors or combining
the temperature-modulated features of NiO (p-type 3d oxide,with outstanding VOCs catalytic oxidation properties30,31),
WO3 (n-type 5d oxide), and commercial SnO2-based VOCs
sensors (TGS 2602) can clearly discriminate the ﬁve BTX
molecules, with 100% recognition and the classiﬁcation rate, as
shown in Figure 5.
As for the core of thermal modulation, the feature extraction
method has always been reported using time−frequency
transformation technology,29,32,33 transforming the signals
from the time domain to the frequency domain, in which
localized frequency signals have been extracted as thedistinguishable features related to the gas molecules. However,the selection of “localized frequency signal” is always resultoriented and subjectively selected, thus hinders the way todesign the smart gas identiﬁcation system. To use a moreobjective denoising method to achieve BTX gas classiﬁcation

Figure 2. Rectangular thermal modulation waveform and transient
sensor normalized resistances (with respect to the resistance at 150°C) of NiO, WO3, and SnO2 sensors (in dry air).

under dry air atmosphere, and the thermal response times (thetime for reaching 90% of temperature increase) of NiO, WO3,and SnO2 sensors were about 2.9, 1.7, and 3.5 s, respectively.
The diﬀerence in the thermal response time arises from
diﬀerent speciﬁc heats and thermal conductivities of the MOS
sensing layer. A thermal modulation period of 5 s (Figure 2)
was used for subsequent experiments.
After the thermal modulation testing, the original signal(response resistance) and the subsequent signal processing
procedure of NiO, WO3, and SnO2 sensors can be seen in
Figures S2−S4, respectively. A signal preprocessing method
described in the SI and our previous work28 was applied toeliminate redundant signals from the original signals. As shownin Figure 3, after getting rid of TCR interferences, removing
concentration-related information and suppressing noise
information step by step, the extracted signal proﬁles wereanalyzed by the LDA (Figure 4). Though using the discrete
wavelet transform (DWT) derived signals (Figure 4d−f)
allows for better discrimination than the untreated signals

Figure 3. DWT response of (a−e) NiO, (f−j) WO3, and (k−o) SnO2 sensors to ﬁve kinds of BTX vapors each at diﬀerent concentrations in therange of 30−90 ppm.
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ArticleFigure 4. LDA feature space of (a) NiO, (b) WO3, and (c) SnO2 sensor response patterns using the normalized thermal modulation sensitivity
response signals. LDA mapping of (d) NiO, (e) WO3, and (f) SnO2 sensor response patterns using the DWT dirived signals. Filled markers present
veriﬁcation tests carried out in the independent experimental session.

First, the learning rate of the CNN is investigated because it
determines the distance of weight moving in the gradientdirection in each batch. With a high learning rate, training may
not converge at all, or even diverge. The change in weight canbe so large that the optimization oversteps the minimum valueof the loss function. When the learning rate is low, each step
approaching the minimum value of the loss function is small.
Although the training becomes more reliable, the optimization
will take a long time. The cross entropy of CNN for diﬀerent
learning rates is shown in Figure S7. The learning rate starts at
a value like 0.1 and then drops exponentially, such as 0.01 and0.001, to estimate the learning rate that optimizes the value ofthe loss function without losing the training speed. The results
show that the learning rate of 0.01 compromises between theminimum cross entropy and the training speed. Figure S7cillustrates the cross entropy of CNN with a diﬀerent number ofneurons in the fully connected layers and the error bars
demonstrate that the CNN was trained at diﬀerent initialstates. For simplicity, the two fully connected layers have an
equal number of neurons, and the results show that the crossentropy decreases with the increasing number of neurons andsubsequently levels oﬀ after the number of neurons above 200.
Thus, 200 neurons will be the optimal number of nodes in the
fully connected layers of CNN.
For the validation process, 105 data sets for training and 70
data sets for testing of each sensor were projected on the
BPNN and the CNN, and the results are displayed in Figure 6.
The cross entropy decreases with the iteration of neuralnetwork training rounds, and the accuracy of BTX identiﬁcation increases with the iteration of the training. It isremarkable that compared with BPNN, the loss of CNN
decreases faster, and it requires fewer training rounds to reachthe maximum recognition rate. Furthermore, CNN has thebest recognition accuracy by comparing the results of species
identiﬁcation of the four algorithms mentioned in our paper, as
shown in Table 1 and Figure S8. The BTX recognition

Figure 5. 3D feature space of LDA-derived scatter plot using
preprocessed response signals of NiO, WO3, and SnO2 sensors
together. Filled markers present veriﬁcation tests carried out in the
independent experimental session.

and identiﬁcation only by a single sensor, a deep learningbased conceptual design, CNN, was explored to analyze andrecognize BTX vapors (Figure S6). It is known the BPNN has
not only good noise tolerance but also excellent robustness.34,35 CNN is developed on the basis of back-propagationneural network (BPNN), which consists of two parts: feature
extraction and data dimensionality reduction neural network
composed of a convolutional layer, a pooling layer, and aclassiﬁcation neural network composed of BPNN.36,37 With
the characteristics and advantages of BPNN, the CNN model
should also be used as a direct learning ﬁlter instead of DWT
for feature extraction, transforming the artiﬁcial feature
extraction into automatic feature extraction with implicit selflearning.
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ArticleFigure 6. Comparison of cross entropy between BPNN and CNN for individual (a) NiO, (b) WO3, (c) SnO2, and (d) three sensors together. (e−h) Comparison of species identiﬁcation accuracy.

amount of complex gases with the increase of CNN depth andscale and the number of sensor arrays using the thermal
modulation method.
Finally, a mini smart (temperature modulation) sensing
system was designed to recognize multiple xylene isomers
using a single SnO2 gas sensor (Figure 7). The temperature
modulation circuit board (Figure S9a), controlled by the PC
user interface, was placed in a glass chamber (Figure S9b).
Under the ambient humid air atmosphere background (relative
humidity of 50−60%), three kinds of saturated xylene vapors
were injected into the chamber. The temperature-modulated
response signal of the sensor was simultaneously sent to the
PC through a Wi-Fi module. After signal processing, as shownin Figure S10, the normalized response signal was fed into the
CNN model, and the forward propagation of the parameters
after training was used to identify the xylene types. As shown in
Figure 8, 21 data sets of each xylene vapor were trained, and 14
data sets of each xylene vapor were fed into the CNN modelfor testing. The recognition accuracy of the smart sensing
system was as high as 95.24%. Relative humidity is one of the
crucial factors aﬀecting the practical application of MOS
sensors. Although relative humidity may aﬀect the baseline
resistance and sensitivity of MOS sensors, the inﬂuence ofTable 1. Accuracy of Species Identiﬁcation with Four
Algorithms
algorithm

NiO (%)WO3 (%)

SnO2 (%)

LDA
DWT + LDA
BPNN
CNN

90.00
97.14
98.57
98.57

75.71
90.00
94.29
95.71

97.14
97.14
100
100

accuracy of NiO, WO3, and SnO2 sensors under the CNN
algorithm is 98.57, 95.71, and 100%, respectively. Therefore,robust prompt discrimination of BTX molecules could be
successfully achieved by the CNN analysis of a single sensor. In
addition, it is worth noting that the recognition andclassiﬁcation rate of 100% BTX vapors can be achieved by
combining the normalized sensitivity response signals of thethree sensors, whether using BPNN or CNN. As shown in
Figure 6h, when the signals of the three sensors are fed into theneural network for training together, the decreasing speed ofcross entropy and the increasing speed of recognition rate are
greatly improved, and it requires fewer training rounds to reachthe maximum recognition rate. Therefore, it is hopeful torealize the qualitative and quantitative identiﬁcation of a huge

Figure 7. Schematic diagram of the smart sensing system using a single SnO2 sensor to identify multiple xylene isomers.
F

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ArticleFigure 8. Smart sensing system: signal preprocessing, training, and recognition. (a−c) Normalized response signals of the SnO2 sensor in the smart
sensing system to three kinds of xylene isomer vapors (30−90 ppm). (d) Cross entropy of the system CNN changes with the increase of thenumber of training iterations. (e) Identiﬁcation accuracy of the system CNN changes with the increase of the number of training iterations. (f)Confusion matrix of the smart sensing system using a single SnO2 sensor to identify multiple xylene isomer vapors.

■

relative humidity on R(t) and S(t) of the sensor issynchronous, and therefore, it has negligible inﬂuence on therecognition features (Figure S11). Moreover, because of thematerial robustness of MOS in the ambient (humid) air
atmosphere and the response signal preprocessing method, aslight shift of base resistance and the response of MOS sensors
would not aﬀect the extraction of intrinsic molecule featurescontained in thermal modulation response signals. We havemonitored the feature pattern of xylene acquired by thepresent smart sensing system, and the absence of a signiﬁcantfeature shift and overlapping within 4 months (Figure S12)
indicates excellent long-term discrimination stability. Together
with good selectivity of xylene isomers with respect to threekinds of common VOCs (Figure S13), temperature modulation of sensor arrays raises the hope of the smart VOCs
recognition system for diverse emerging applications.

The Supporting Information is available free of charge athttps://pubs.acs.org/doi/10.1021/acssensors.1c01704.
Signal preprocessing method; methods/experimental
section; characterization of materials; cross entropy;
confusion matrix; network structure; diagram of themeasurement setup and smart machine olfactory system;
and additional graphs (PDF)■

AUTHOR INFORMATION

Corresponding Authors

Gang Meng − Anhui Institute of Optics and Fine Mechanics,
and Key Lab of Photovoltaic and Energy Conservation
Materials, Chinese Academy of Sciences, Hefei 230031,
China; orcid.org/0000-0002-3365-8963;
Email: menggang@aiofm.ac.cn
Liang Li − School of Physical Science and Technology, Jiangsu
Key Laboratory of Thin Films, Center for Energy Conversion
Materials & Physics (CECMP), Soochow University, Suzhou
215006, China; orcid.org/0000-0003-0708-7762;
Email: lli@suda.edu.cn
Xiaodong Fang − College of New Materials and New Energies,
Shenzhen Technology University, Shenzhen 518118, China;
Email: fangxiaodong@sztu.edu.cn■

CONCLUSIONS
Transient electrical response signals of generic nonselective
MOS sensors under an appropriate temperature modulation
waveform contain rich features of adsorbed gas molecules.
Structurally similar BTX molecules (including three kinds ofxylene isomers) can be readily discriminated by either single ormultiple nonselective MOS sensor(s) with the assistance ofdeep learning algorithms. Together with excellent chemical
and thermal stabilities of MOS in the ambient humid air
atmosphere, direct electrical readout, and silicon chipcompatibility, a smart sensing system based on temperature
modulation is demonstrated, which enables a prompt andaccurate discrimination of xylene isomer molecules and raises
the hope of exploring the future smart machine olfactory
system.

■ASSOCIATED CONTENT

sı Supporting Information*

Authors

Hongyu Liu − College of New Materials and New Energies,
Shenzhen Technology University, Shenzhen 518118, China;
Guangdong Key Laboratory for Biomedical Measurements
and Ultrasound Imaging, School of Biomedical Engineering,
Shenzhen University Health Science Center, Shenzhen
518060, China
Zanhong Deng − Anhui Institute of Optics and Fine
Mechanics, and Key Lab of Photovoltaic and Energy
Conservation Materials, Chinese Academy of Sciences, Hefei
230031, China; orcid.org/0000-0003-3487-9167

METHODS/EXPERIMENTAL SECTION

Experimental details are provided in the Supporting Information(SII).
G

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Kazuki Nagashima − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan; orcid.org/0000-0003-0180816X
Shimao Wang − Anhui Institute of Optics and Fine Mechanics,
and Key Lab of Photovoltaic and Energy Conservation
Materials, Chinese Academy of Sciences, Hefei 230031,
China; orcid.org/0000-0002-4495-6130
Tiantian Dai − Anhui Institute of Optics and Fine Mechanics,
and Key Lab of Photovoltaic and Energy Conservation
Materials, Chinese Academy of Sciences, Hefei 230031, China
Takeshi Yanagida − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan; orcid.org/0000-0003-48375701(10) Kadir, R.; Yimit, A.; Ablat, H.; Mahmut, M.; Itoh, K. Optical
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Complete contact information is available at:https://pubs.acs.org/10.1021/acssensors.1c01704
Notes

The authors declare no competing ﬁnancial interest.

■

ACKNOWLEDGMENTS
This research was ﬁnancially supported by the CAS Pioneer
Hundred Talents Program from Chinese Academy of Sciences,
National Natural Science Foundation of China (52025028,
11674324, and 62075223), Natural Science Foundation of
Top Talent of SZTU (2020101), and CAS-JSPS Joint
Research Projects (GJHZ1891). The authors thank Mr. Ning
Pan and Liming Fan for partial support in the circuit design.

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