Research Article
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Quantitatively Discriminating Alcohol Molecules by Thermally
Modulating NiO-Based Sensor Arrays
Meng Li, Hongyu Liu, Junqing Chang, Tiantian Dai, Kazuki Nagashima, Zanhong Deng,
Takeshi Yanagida, Xiaodong Fang,* and Gang Meng*

Although temperature modulation of a single (nonselective) semiconductor
gas sensor has demonstrated its great capability on discriminating gas
molecules, quantitative analysis of the type and concentration of structurally
similar volatile organic compounds (VOCs) molecules remains a significant
challenge. In this work, as an alternative to a single oxide sensor, sensor
arrays composed of four types of NiO-based sensor synchronously performthermal modulation with a unique programmed cooling temperature profile.
Apart from improved category discrimination by using entire sensor arrays,
the peak temperature of the temperature profile plays an important role indistinguishing the concentration difference induced by subtle variationsof the electrical responses. A high peak temperature of ≈300 °C facilitatesconcentration discrimination (with an accuracy ratio of 85.9%). This work
highlights the importance of the peak temperature in (simultaneous)quantitative analysis of the types and concentrations of VOCs moleculeswith similar properties.

M. Li, H. Liu, J. Chang, T. Dai, Z. Deng, G. Meng
Anhui Provincial Key Laboratory of Photonic Devices and Materials
Anhui Institute of Optics and Fine Mechanics
Key Lab of Photovoltaic and Energy Conservation Materials
Hefei Institutes of Physical Science
Chinese Academy of Sciences
Hefei 230031, China
E-mail: menggang@aiofm.ac.cn
M. Li, J. Chang, T. Dai
Scinece Island Branch, Graduate School
University of Science and Technology of China
Hefei 230026, China
M. Li, Z. Deng, G. Meng
Advanced Laser Technology Laboratory of Anhui Province
Chinese Academy of Sciences
Hefei 230037, China
H. Liu, X. Fang
College of New Materials and New Energies
Shenzhen Technology University
Shenzhen 518118, China
E-mail: fangxiaodong@sztu.edu.cn
K. Nagashima, T. Yanagida
Department of Applied Chemistry
Graduate School of Engineering
The University of Tokyo
Tokyo 113-8656, JapanThe ORCID identification number(s) for the author(s) of this article
can be found under https://doi.org/10.1002/admt.202100762.

DOI: 10.1002/admt.202100762

Adv. Mater. Technol. 2021, 2100762

1. Introduction

Volatile organic compounds (VOCs) areubiquitous in human exhalation, housedecoration, industrial emissions, andtransportation. Exposure to VOCs may
make people dizzy or even poison them,
depending on the species and concentration of VOCs. Taking alcohol groups as an
example, methanol poisoning outbreaks
occur frequently in developing countries
because of adulterated alcohols.[1] The
widely used ethanol and isopropanol (IPA)
are classified as group 1 carcinogens by
the International Agency for Research
on Cancer of the World Health Organization.[2] The analysis of VOCs molecules
using metal oxide semiconductor (MOS)
gas sensors has attracted considerable
interest, because of their small size, lowpower consumption, and easy integration.[3] Persistent efforts
have been devoted to addressing the selectivity issue, a prerequisite for the real applications of MOS sensors.[4–6] Normally, it
is difficult to discriminate adsorbed molecules using a single
MOS sensor operated at isothermal (constant operation temperature) measurements, because of the limited extracted
features (i.e., temperature-dependent sensitivity[7] and adsorption/desorption time[8]), especially for chemically and structurally similar VOCs molecules, which possess similar (reducing
vapors) electrical responses.[9] Constructing electronic nose or
sensor arrays with distinct surface properties by adopting newmaterials, extrinsic doping,[10] noble metal modification,[11] andheterogenous or composite materials[12] has been widely used
to multiply the (isothermal) features, thus enriching the recognition capability of MOS sensors.[13,14] Various n-type (SnO2,[15]ZnO,[16,17] WO3,[18] In2O3[19]) based electronic noses have beenreported. For example, Kang et al.[7] fabricated electronic noses
with five different MOS sensors through high-resolution
nanolithography and successfully distinguished seven typesof hazardous vapor. Shi et al.[13] reported the precise discrimination of similar (mesitylene, o-xylene, and toluene) vapors
using cross-reactive arrays. However, isothermal features(temperature-dependent isothermal measurements) are either
insufficient or time-consuming to acquire (by minimizing thetemperature interval, increasing the number of isothermal
measurement cycles), which makes it difficult or insufficient to
extract the subtle features of different molecules with different
concentrations.

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In comparison with isothermal measurements, thermal
modulation varies the sensor temperature, a key parameterthat affects both the adsorption and the redox kinetics oftarget analyte molecules and the surface reactive oxygen species (ROS) of the oxide.[20] Rich rate or kinetics-related featuresof adsorbed molecules can be extracted from a single MOS
sensor to discriminate diverse (VOCs) molecules, even fora nonselective (commercial) n-type MOS sensor.[21] Our previous work indicated that p-type NiO outperforms commercial n-type SnO2 sensors in terms of VOCs discrimination.[22]Lately, we have demonstrated that aliovalent Sc[23] and Sc,
Li[24] (co)doping could drastically boost the electrical responseof NiO sensor to ethanol and several other VOCs molecules.
Apart from modified electrical properties (e.g., base resistanceof sensor), the surface adsorption affinity and reactivity properties of NiO-based sensors could be tuned via aliovalent doping,which offers a route to differentiate subtle features of adsorbed
VOCs species with different concentrations. Because MOS sensors are chemical sensors, and electrical responses (origin ofmolecular features) stem from the specific gas/MOS interfacial
charge interaction. In addition, though diverse kinds of heating
waveform have been reported,[25] some fundamental issues arestill unclear, such as how to set the temperature range to extract
the molecular features efficiently.
In this work, as an alternative to a single oxide sensor, sensor
arrays composed of p-type NiO-based sensors including pure
NiO, Sc-doped NiO (Sc:NiO),[23] Li-doped NiO (Li:NiO), andSc and Li co-doped NiO (Sc-Li:NiO)[24] with modified sensing
characteristics were constructed and performed thermal modulation. A programmed cooling temperature profile was synchronously applied to the sensor arrays. Probed by three kinds ofalcohols (with the same functional group of −OH), methanol,
ethanol, and IPA, the peak temperature of the heating waveform was systematically tuned from 50 to 300 °C to generate
a series of transient electrical response signals, which were
subsequently analyzed using machine learning algorithms,
including principal component analysis (PCA), linear discriminate analysis (LDA), and multilayer perceptron (MLP). Present
NiO-based electronic nose possesses excellent category discrimination capability and can discriminate three types of alcohol
molecule with 100% confidence by MLP at all peak temperature
modulations, even at a low peak temperature of 100 °C. However, concentration discrimination is sensitively dependent onpeak temperature. A high peak temperature of ≈300 °C offers
sufficient surface ROS to differentiate the subtle electricalresponse differences induced by the concentration difference
and achieves a high concentration recognition accuracy ratioof 85.9%. This work demonstrates the importance of peak temperature for the simultaneous discrimination of structurally
similar VOCs molecules by thermally modulating MOS sensor
arrays, and it offers new avenues for exploring future advanced
VOCs recognition electronics.

2. Results and Discussion
2+

3+

In this work, mild hydrolysis of Ni (as well as Sc andLi+) in base solution under appropriate temperature was
employed to obtain hierarchical layered structure β-Ni(OH)2
Adv. Mater. Technol. 2021, 2100762

microflowers.[23,24,26] Followed by a simple sintering process, aseries of p-type NiO microflowers, including pure NiO, Sc:NiO,
Li:NiO, and Sc-Li:NiO, could be obtained to construct sensor
arrays (Figure 1a–d). All the NiO materials show a pure facecentered cubic NiO phase (JCPDS No. 78–0423) (Figure 1e).
An enlarged scan indicates a shift of (220) peak to small diffraction angle (right of Figure 1e), suggesting an expansion of
the NiO lattice due to Sc and/or Li doping, which agrees wellwith the fact that both Sc3+ (74.5 pm) and Li+ (76 pm) dopant
have a larger ionic radius than that of Ni2+ (69 pm) under thesame coordination number of 6.[24] In addition, there is a slightbroadening of X-ray diffraction (XRD) (200) peak of Sc contained NiO materials in Figure 1e, inferring smaller averagegrain sizes (estimated by Scherrer formula) of Sc:NiO (15.4nm) and Sc-Li:NiO (14.5 nm) than NiO (19.7 nm) and Li:NiO(18.9 nm). As the solubility product constant of Sc(OH)3 (10−31)
is much smaller than that of Ni(OH)2 (10−16),[27] the adding of
Sc3+ in Ni2+-based precursor possibly accelerates the hydrolysis rate, and thus results in a slight size shrinking of microflowers illustrated in scanning electron microscope (SEM)
images (Figure 1a–d). Additional material characterization data,

Figure 1. SEM images of a) NiO, b) Sc:NiO (Ni0.98Sc0.02O, 1.6 at% Sc),c) Li:NiO (Ni0.94Li0.06O, 5.6 at% Li), and d) Sc-Li:NiO (Ni0.93Sc0.02Li0.05O,
1.8 at% Sc + 5.3 at% Li) microflowers. e) XRD patterns of the pure NiO,
Sc:NiO, Li:NiO, and Sc-Li:NiO, the right shows an enlarged XRD patternof (220) peak.

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Figure 2. Variations of four series of NiO-based sensor resistances upon successive exposure to 200 ppm a) methanol, b) ethanol, c) IPA at operation
temperature of 150 and 250 °C. Radar plot for the response times of four series of NiO-based sensors to 200 ppm d) methanol, e) ethanol, and f) IPA,
and recovery time to 200 ppm g) methanol, h) ethanol, and i) IPA at operation temperatures ranging from 150 to 250 °C.

including transmission electron microscopy, X-ray photoelectron spectroscopy, electron paramagnetic resonance spectroscopy, and energy dispersive X-ray spectroscopy, can be found in
our previous works.[23,24]
The isothermal sensing characteristics of four kinds of NiObased sensors toward alcohol molecules have been investigatedfirst (Figure 2 and Figure S1, Supporting Information). Aliovalent Sc, Li, and Sc-Li doping could alter the electrical properties(base resistance, Rair) of the sensing channel (Figure 2a–c). As
the widely used shallow acceptor for p-type NiO,[28,29] Li doping
allows to increase the conductivity (reduce the base resistance)of p-type NiO. On the contrary, high valence Sc3+ cations may
introduce additional donor states, resulting in an increase ofbase resistance (Figure 2a–c). In comparison with Li dopant, Sc
and Sc-Li dopants are supposed to create more surface active
sites (i.e., ROS) in NiO and contribute to higher electricalresponses to alcohols.[23,24] The differences in the total available
surface active sites are beneficial for concentration discrimination, which is discussed later. The response time (Figure 2d–f)Adv. Mater. Technol. 2021, 2100762

and recovery time constants (Figure 2g–i) suggest a differencein the surface redox reaction and recovery rate of methanol,
ethanol, and IPA molecules on NiO-based materials. Sc-Li:NiO
sensor possesses a higher response (Figure S2, Supporting
Information) and a relatively shorter response/recovery timethan that of undoped NiO (Figure 2d–i) at the same operation
temperature. Meanwhile, regarding the same Sc-Li:NiO sensor,
the optimal operation temperature shifts from ≈225 °C (methanol) to 200 °C (IPA) and 175 °C (ethanol), implying a difference in the reducibility of alcohol groups with different numbers of carbon chains, which leads to differences in the rate of
the redox reaction on the surfaces of sensitive materials.[30]Although the isothermal measurements of the NiO sensor
arrays (Figure 2a–c and Figure S2, Supporting Information)
show different temperature-dependent sensing characteristics,
limited features (steady response, response time, and recoverytime) obtained by one measurement cycle (taking ≈200 s in
Figure 2a–c), hinder the discrimination of the tested alcohol
molecules. The temperature modulation of a single sensor

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Figure 3. a) One cycle of temperature modulation profile with varied initial temperatures (50–300 °C) and a fixed ending temperature of 50 °C, theprogrammed cooling temperature waveforms used in this work are shown by solid lines. b) Resistance variations of NiO-based sensor arrays uponsuccessive exposure to various IPA gases (40–1000 ppm, 40 ppm step) under the periodic programmed cooling temperature modulation profiles from
300 to 50 °C (dash magenta line). Gas response signals during cooling stage (light gray zones) were utilized for the subsequent feature extraction.

demonstrated its efficiency and capability for molecule feature
extraction,[22,31] as illustrated by the recognition performancecomparison between temperature modulation and isothermal
testing (Figure S3, Supporting Information). Because temperature plays a crucial role in the adsorption and subsequent redox
reactions of adsorbed molecules with MOS surfaces,[20] appropriately selecting the temperature range for temperature modulation is critical for fully exploring the discrimination capabilityof an MOS sensor. In this work, programmed cooling temperature waveforms with varied initial temperatures (50–300 °C) anda fixed ending temperature (50 °C) have been proposed (solidlines in Figure 3a), exhibiting an improved recognition performance (Figure S4, Supporting Information) than heating or periodic heating–cooling down waveforms widely used in previous
works.[32] The peak temperature was systematically varied from
300 to 50 °C to modulate the recognition performance of theNiO sensor arrays. The transient resistances of the sensor arrays
under successive programmed temperature cooling (to 50 °C)
and heating (to 300 °C) cycles (dash magenta line in Figure 3b)
upon exposure to 40–1000 ppm IPA vapors (light gray zones)
are depicted in Figure 3b. A slight increase in the sensor resistance at 50 °C with increasing IPA concentration agrees well with
the isothermal responses of p-type NiO sensors. All the sensors
could be fully refreshed after the heating and high-temperature
maintenance stages (dash plateau magenta line in Figure 3b),as indicated by the stable base resistance (before alcohol vapor
injection). Moreover, differing from previous temperature modulation reports widely using n-type SnO2-based sensors, present
p-type NiO sensors show a large temperature coefficient of resistance/resistivity, which inevitably introduces dominant variationof sensor resistance (two to five orders), purely arising from theintrinsic electrical property of the sensing channel, irrespective of
the adsorbed molecules. Using sensitivity data (Rgas/Rair) insteadof resistance data has proved to be a key step in extracting the
intrinsic features of adsorbed molecules from thermal modulation data.[22]Figure 4 and Figure S5 in the Supporting Information present
the normalized response of the sensor arrays toward three typesof alcohol vapor with different concentrations, under various
peak temperatures. At a constant low temperature of 50 °C, theresponses of all sensors were rather noisy, arising from fewer
thermally generated carriers (or high resistance) in NiO-based
sensing channels. Tiny electrical responses at low temperatureAdv. Mater. Technol. 2021, 2100762(50 °C) (see Figure S2, Supporting Information) indicate negligible VOCs/oxide interfacial redox reactions, which hindersubsequent recognition. Increasing the peak temperature facilitates the generation of surface ROS,[33] which subsequently participate in the oxidation reaction with adsorbed reducing vapors(i.e., VOCs)[20,33] and contributes to the increase in the electricalresponse upon exposure to alcohol vapors. Therefore, the normalized responses of the four sensors increased first (Figure 4
and Figure S5, Supporting Information), and then decreased
because of less generated ROS at low temperatures. This trend
can be seen clearly in Figure S6 and Table S1 in the Supporting
Information. Sc-Li:NiO sensor arrives at the peak response earlier (30 s for 40 ppm methanol at a peak temperature of 200 °C)
than the other three sensors because of the high and prompt
response at 200 °C (Figure S6, Supporting Information) andnegligible response at 50 °C (Figure S7, Supporting Information). The NiO and Li:NiO sensors possess a longer time constant (≈68 and 247 s, respectively) under the same conditions(Figure S6, Supporting Information), owing to the relatively
high response at 50 °C (Figure S7, Supporting Information)and the long response, recovery times at both high temperature(Figure S6, Supporting Information) and 50 °C (Figure S7, Supporting Information). With increasing concentration of alcohol
vapor, a gradual (left) shift in the normalized response curves
was observed (rainbow colored curves in Figure 4 and Figure
S5, Supporting Information) for all sensors, which contain therich features related to the concentration of tested VOCs vapors.
Moreover, the rainbow curves have a large span with an increasein the peak temperature (Figure 4 and Figure S5, Supporting
Information), inferring improved concentration features, which
are discussed later. The distinct transient response characteristics, endowed by the altered surface physical and chemical
properties of NiO materials by aliovalent doping, undoubtedly
multiply the features of test VOCs molecules and thus benefit
molecule discrimination by machine learning algorithms.
A supervised LDA algorithm was first applied to reducethe multidimensional feature vector into a 2D visual space, as
shown in Figure 5. Except at the constant temperature of 50 °C
and high peak temperature of 300 °C, methanol (blue square),ethanol (green circle), and IPA (red triangle) species could beclearly separated from other types of gas molecule by analyzing
the response signals obtained from temperature modulation.
Although the performance of a single sensor at different peak

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Figure 4. Normalized response curves upon exposure to 40–1000 ppm alcohol vapors during the programmed cooling stage.

temperatures varied, the recognition capability of the sensor
arrays outperforms all four sensors at any peak temperature(Figure S8, Supporting Information). More interestingly, evena low peak temperature of 100 °C (≈100 °C lower than the
optimal operation temperature in Figure S2, Supporting Information) is capable of classifying alcohol types. The powerful

discrimination capability of low peak temperature-modulated(NiO) sensor arrays promises a future low-power-consumption
electronic nose, providing new opportunities for diverse environmental and health monitoring.
Next, the alcohol concentrations were analyzed quantitatively. The input concentration (40–1000 ppm) was split into

Figure 5. The 2D feature space of LDA mapping of species classification based on features extracted from entire sensor arrays at the peak temperaturesof a) 50, b) 100, c) 150, d) 200, e) 250, and f) 300 °C. The half-filled markers indicate test sets carried out in independent experimental sessions. (LD1 andLD2 are the discriminant scores of the first two linear discriminant functions with the largest contribution to classification) (25 samples of each analyte).

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Figure 6. The 2D feature space of LDA mapping of concentration classification based on entire sensor arrays at peak temperatures of a) 50, b) 100, c)
150, d) 200, e) 250, and f) 300 °C. (LD1 and LD2 are the discriminant scores of the first two linear discriminant functions with the largest contribution
to classification) (25 samples of each analyte).

five intervals, and the gas molecules in a specific concentrationinterval were regarded as a single category. The LDA concentration classifications under various peak temperatures are shownin Figure 6. The peak temperature plays an important role inspecies discrimination (shown as the nonoverlapping of threeshaped frames in the feature space). When the peak temperature is 200 °C, the points of different colors in each frame are
separated more clearly, indicating that the species and concentration discrimination at the peak temperature is most effective.
Under low peak temperature modulation, molecular features
are not easily extracted owing to the low gas response.
To evaluate the effect of peak temperature on the correct
recognition rate, an MLP neural network was used. Consistent
with the LDA classification (Figure 5), species classification canbe accomplished easily for all the peak temperatures ranging
from 100 to 300 °C, and the correct ratio is higher than 75%,
even when using a small number (4% of the total) of samples
as the training set (the remaining 96% of the samples are usedas the testing set), as shown in Figure 7. When the peak temperature increased, the correct recognition rate first increased
and then decreased, reaching the highest value at about 200 °C.
Confusion matrix analysis (Figure S9, Supporting Information)
indicates that the highest mean correct recognition rate of 87.7%

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Figure 7. Correct recognition rate versus peak temperature for speciesclassification of 80% of samples for training set (red line), species classification of 4% of samples for training set (blue line), and concentration
classification of 80% of samples for training set (yellow line) (25 samplesof each analyte).

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occurred at the peak temperature of 200 °C, with the lowest error
rates indicated by a higher transparency of the pink color, which
agrees well with the PCA classification (Figure S10, Supporting
Information). On the contrary, the correct ratio for concentration
recognition is much smaller than that for category recognition,even when 80% of the total samples were used as the training set(Figure 7), indicating a potential overlap of concentration-related
features. Moreover, the correct ratio monotonically increased
with the peak temperature, and the recognition capability of thesensor arrays outperforms all four sensors at any peak temperature (Figure S11, Supporting Information).
Clarifying the variation in electrical responses during temperature modulation is essential for the rational design ofhigh-performance electronic noses. The increased surface
unsaturated oxygen vacancy defects induced by Sc3+ dopant
were generally regarded as active sites for catalytic oxidizationof adsorbed VOCs molecules, which greatly enhance the electrical response toward VOCs molecules.[23] Regarding Sc andLi co-dopant, density functional theory suggests that ethanol
molecules could preferentially binding with alkali Li+ sites incomparison with Ni2+ sites,[24] results in a drastic improvement of electrical responses toward ethanol. We suppose thataltering the surface reactivity of NiO-based sensors via doping,either enhancing or suppressing, results in the distinct interfacial redox kinetics, which contributes to the electrical featuresof tested VOCs with different concentrations. In addition to
the intrinsic temperature-dependent resistance of the semiconductor, the resistance variation of the NiO-based sensor
could be modulated by both ionosorption (reaction (2))[33] andsurface redox reactions between ROS (O2−) adsorbed VOCs
molecules (note that physisorption typically does not accountfor significant resistance variation of an MOS sensor[20]). Underan ambient air atmosphere, the ionosorption of adsorbed O2(greatly activated by increasing the operation temperature[33])extracts minority electron carriers from p-type NiO and causes
a decrease in resistance via the following reactions
O2 gas → O2 ads(1)O2 ads + e − → O2 −(2)

Ionosorption generates surface ROS O2− ions, which enableto oxidize the adsorbed alcohol (such as methanol) molecules
via two possible reaction pathways[34]2CH3OHads + O2 − → 2HCHO + 2H2O + e −(3)

2CH3OHads + 3O2 − → 2CO2 + 4H2O + e −(4)

The injection of free electrons into the NiO-based sensing
layers via reactions (3) and/or (4) results in an increase in thesensor resistance. The electrical response of the sensor depends
on the concentration of adsorbed alcohol molecules and ROS,
both of which depend on the temperature. It is well known thatraising the temperature facilitates the generation of ROS.[33]Raising the operating temperature (from room temperature)
usually enhances the electrical responses, while a temperaturethat is too high enhances the desorption of preadsorbed analyte alcohol molecules.[35] Therefore, a volcano response versus
temperature curve with an optimal operating temperature iscommonly exhibited, as shown in Figure S2 in the Supporting
Information. The varied response kinetics, including the isothermal temperature-dependent responses (Figure 2a–c) and
response and recovery time constants (Figure 2d–i) indicate
the different surface redox kinetics between the tested alcohol
molecules and (doped) NiO. Such a difference is essential forcontributing to the electrical response differences or intrinsic
features of different VOCs molecules with varied concentrations under appropriate temperature modulations.
To gain insight into the misclassified groups, a confusion
matrix analysis of concentration recognition under different
peak temperatures was performed (Figure 8 and Figure S12,
Supporting Information). Under a low operation temperature (50–100 °C), most of the misclassified samples (pink) areneighboring concentration samples of the same gas. The correct recognition rate (noted by the main diagonal of the matrix)
increases with elevating the modulated peak temperature(Figure 8b). At a high peak temperature, more ROS could be
generated in the air atmosphere. These ROS would be gradually or rapidly consumed (via reactions (3) and (4)) during the
programmed cooling stage when alcohol vapors are injected.

Figure 8. Confusion matrix of MLP concentrations classification based on entire sensor arrays at peak temperatures of a) 50 and b) 300 °C (25 samplesof each analyte).

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The onset and decay of the normalized peak response dependon the supply rate (or concentration) of alcohol vapors. Higherconcentration alcohols feed alcohol and consume ROS fasterthan the low-concentration alcohols, resulting in a fasterincrease (and decay) of the normalized response, which is thereason for forming the rainbow curves in Figure 4 and Figure
S5 in the Supporting Information. At a high peak temperature, a relatively long programmed cooling time (Figure 2d)offers additional ROS and enlarges the shift of the normalized
responses of different concentration analytes (rainbow colors
in Figure 4 and Figure S5, Supporting Information), and thefeatures of alcohol group gas concentrations can be effectively
distinguished based on species classification. Furthermore,
additional two low-concentration categories (conc1: 1–10 ppm,
conc2: 10–20 ppm) were examined using sensor arrays. The
MLP results (Figure S13, Supporting Information) indicate
poor recognition performance in such a low-concentration
range (with small electrical responses). Quantitative analysis oftrace (i.e., < 10 ppm) VOCs molecules by thermally modulating
the electronic nose with a higher response and/or low detectionof limit will be another interesting topic beyond the scope of
the present work.[36–38]

3. Conclusion
All in all, using a programmed cooling temperature waveform,
the effect of peak temperature on the discrimination capabilityof a thermally modulated NiO-based electronic nose has been
investigated, probed by three kinds of alcohol VOCs with different concentrations. It was found that category classification
can be readily achieved at any peak temperature modulation(even at a low peak temperature of 100 °C). The concentration
features are closely related to the peak temperature. A highpeak temperature of ≈300 °C facilitated the concentration classification with an accuracy ratio of 85.9% in a large concentration range (40–1000 ppm). The possible underlying mechanism
during temperature modulation was discussed to provide inspiration for the rational design of advanced VOCs molecule recognition electronics in future for diverse emerging applications.

4. Experimental Section
Synthesis and Characterization of NiO-Based Sensitive Materials: The
NiO-based materials were synthesized via a wet chemical method, anddetailed procedures can be found in previous works.[23,24] In short, 1.5 mLof Ni(CH3COO)2⋅4H2O aqueous solution (1.2491 g dissolved in 10 mLdeionized water), appropriate dopant (no addition for pure NiO, 11.1 mg
of Sc(NO3) 3⋅H2O for Sc:NiO, 4.59 mg of Li(CH3COO)2⋅2H2O for Li:NiO,both of the above chemicals for Sc-Li:NiO), 0.3 mL ethylenediamine, and9 mL NaOH aqueous solution (5.6 g dissolved in 20 mL deionized water)
were mixed and stirred for 10 min. The mixed precursor was heated in an
oil bath at 100 °C for 30 min with strong magnetic agitation, then washedthree times with deionized water and ethanol in a centrifuge, and baked inan oven at 70 °C for 24 h. Finally, the NiO-based samples were obtainedby annealing in air at 500 °C for 2 h. The morphologies of the NiO-based
sensors were observed using SEM (VGA3SBH). XRD measurement wereconducted using a D8 ADVANCE X-ray diffractometer, operated at 40 kV,
40 mA, and an angle of 2θ ranging from 10° to 70° to collect the crystalline
characteristics of the samples.

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Device Fabrication and Sensing Test: First, four kinds of NiO-based
powder were dispersed in an appropriate amount of ethanol andcontinuously stirred for more than 1 h. The paste that formed wasthen coated onto the sensing ceramic substrates (with heater andtest electrodes) as the sensing layer. All sensors were aged in air at≈220 °C for 1 week before testing. Three types of alcohol group gas ofmethanol, ethanol, and IPA (with quite similar properties, see Table S2,
Supporting Information) were selected as the target analytes. Different
concentrations of alcohol group gases were obtained by mixing standard
gas and dry air in any ratio, controlled by mass flow controllers (CS200, Sevenstar Electronics Co. Ltd.). The total gas flow rate wasset at 500 sccm. The gas sensing tests were carried out in a fourchannel gas-sensitive measurement system (SD101, Huachuang Ruike
Technology Wuhan Co. Ltd.), which allowed to measure the resistance
ranging from 100 Ω to 1 GΩ with a verified accuracy of ±5.0% and an
accuracy of ±18.3% for 10 GΩ, as seen in Figure S14 in the Supporting
Information. The temperature of the sensor was modulated from thepeak temperature (50, 100, 150, 200, 250, or 300 °C) to a fixed low
temperature of 50 °C by tuning the heating voltage. The alcohol groupvapors were injected into the test chamber in the temperature drop
range. After the temperature modulation, raising the heater temperatureand maintaining a constant peak temperature (≈120 s) facilitated thedesorption of the tested analytes and recovery of the sensor baseline
resistances. Before the sensors resistance under the target alcoholgroup gases (Rgas) was measured, the air resistance (Rair) was measured
in the first temperature modulation cycle. The response of the alcoholgroup gases was defined as Rgas/Rair. Methanol, ethanol, and IPA, eachat 25 different concentration levels (40–1000 ppm, 40 ppm ascendingstep), 75 analytes in total, were tested once in sequence from the second
temperature modulation cycle.
Statistical Analysis: The features used to distinguish alcohol groupgases were extracted through preprocessing steps. For each temperature
modulation test, the cycle duration time was fixed, and the response
value was obtained from Rgas divided by Rair, both with cycle time
dimension
S (t ) = Rgas (t) / Rair (t )(5)

Next, the gas responses of different concentrations obtained by eachtemperature modulation were normalized
S nor (t ) = (S (t ) − S min ) / (S max − S min )(6)

where Smax and Smin are the maximum and minimum values of the
response in the cooling range, respectively, Snor(t) represents thenormalized response, and t is the time. Data processing was completedin Python 3.6. The features of the cycle time dimension were reduced
to a 2D visual space using PCA. In addition, LDA classified thesamples based on the input features. LDA used leave-one-out crossvalidation, and each example was classified by functions derived fromall examples except that example. A multilayer perceptron was a typeof feedforward neural network proposed to solve nonlinear problems
that could not be solved by a single-layer perceptron. MLP was a simple
neural network, which allowed to quantitatively evaluate the features ofelectrical responses obtained under different peak temperatures. The
features of the four sensors were input to the MLP with a hidden layer,
and the number of units in the output layer was 3 or 15 according to
species or concentration classification. Softmax and tanh were used as
the activation functions of the output and hidden layers, respectively.
The cross-entropy with faster convergence was selected as the costfunction to evaluate the neural network model by minimizing the costfunction. There were three training modes of the MLP, two of whichwere species classification, while one was concentration classification.
For the first mode, 80% of the samples (75 samples in total) wereused as the training set, and the rest were used as the test set. A
fivefold cross-validation could make full use of the sample data. For thesecond mode, 4% of the samples were used as the training set, and2100762 (8 of 9)© 2021 Wiley-VCH GmbH

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the rest were used as the test set. In other words, only one sample ofeach species (25 samples in total) was used as the training set, andsamples of the same concentration of the three alcohol group gases
were selected. Then, a 25-fold cross-validation was performed to avoid
accidental errors. For the third mode, gas samples of 0–1000 ppm
were divided into five concentration ranges according to a segment of
200 ppm. 80% of the samples (four samples) of a concentration range
were used as the training set, and the rest were used as the test set.
Fivefold cross-validation was used. The algorithms (PCA, LDA, andMLP) were implemented using IBM SPSS Statistics 26.

Supporting Information
Supporting Information is available from the Wiley Online Library orfrom the author.

Acknowledgements
This work was financially supported by the CAS Pioneer Hundred
Talents Program of Chinese Academy of Sciences, Natural Science
Foundation of Top Talent of SZTU (2020101), CAS-NSTDA Joint Research
Projects (GJHZ202101), CAS-JSPS Joint Research Projects (GJHZ1891),
Shenzhen Science and Technology Program (KQTD20170331115422184),
and the Key Lab of Photovoltaic and Energy Conservation Materials(PECL2018QN001 and PECL2019QN005).

Conflict of Interest
The authors declare no conflict of interest.

Data Availability Statement
The data that support the findings of this study are available from thecorresponding author upon reasonable request.

Keywords
NiO-based sensor arrays, quantitative discrimination, simultaneousclassification of type and concentration, temperature modulation
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