MOx heterostructured nanomaterials for highly selective gas sensor array integration

Accepted Manuscript Title: One-step electrospun SnO2 /MOx heterostructured nanomaterials for highly selective gas sensor array integration Authors: Longfei Song, Liping Yang, Zhou Wang, Di Liu, Linqu Luo, Xinxu Zhu, Yan Xi, Zaixing Yang, Ning Han, Fengyun Wang, Yunfa Chen PII: DOI: Reference:

S0925-4005(18)32216-0 https://doi.org/10.1016/j.snb.2018.12.097 SNB 25859

To appear in:

Sensors and Actuators B

Received date: Revised date: Accepted date:

23 June 2018 14 December 2018 19 December 2018

Please cite this article as: Song L, Yang L, Wang Z, Liu D, Luo L, Zhu X, Xi Y, Yang Z, Han N, Wang F, Chen Y, One-step electrospun SnO2 /MOx heterostructured nanomaterials for highly selective gas sensor array integration, Sensors and amp; Actuators: B. Chemical (2018), https://doi.org/10.1016/j.snb.2018.12.097 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

One-step electrospun SnO2/MOx heterostructured nanomaterials for highly selective gas sensor array

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integration

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Longfei Songa,b, Liping Yangb,c, Zhou Wangb, Di Liua, Linqu Luoa, Xinxu, Zhua, Yan Xia,

College of Physics and Cultivation Base for State Key Laboratory, Qingdao University,

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a

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Zaixing Yangd, Ning Hanb,c*, Fengyun Wanga* and Yunfa Chenb,c

State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering,

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b

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Qingdao 266071, China

c

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Chinese Academy of Sciences, Beijing 100190, China Center for Excellence in Regional Atmospheric Environment, Institute of Urban

School of Microelectronics and Center of Nanoelectronics, Shandong University, Jinan 250100, China

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d

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Environment, Chinese Academy of Sciences, Xiamen 361021, China

Corresponding Authors: *E-mail: [email protected]; *E-mail: [email protected]

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Graphical abstract

Highlights

Heterostructured SnO2/MOx (M=Zn, Ga, W) nanotubes and nanofibers were

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Selectivity was effectively changed and controlled by designing various

SnO2/MOx gas sensors were firstly used to compose sensor arrays to precisely

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

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SnO2/MOx heterostructures.

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

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fabricated by one-step electrospinning and subsequent calcination.

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detect VOCs mixtures by means of matrix manipulation.

Abstract：It is a challenge to effectively detect the components of a gas mixture using metal oxide sensors which are easily fabricated and inexpensive. In this work,

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heterostructured SnO2/MOx (i.e. M=Zn, Ga and W) nanotubes (NTs) and nanofibers (NFs) are synthesized via a one-step electrospinning technology and subsequent calcination for use in gas sensors. In specific, compared with pure SnO2 NTs, SnO2/ZnO NTs show an enhanced response to 100 ppm ethanol and acetone, while

SnO2/Ga2O3 NTs show an obviously higher response to 100 ppm ethanol and SnO2/WO3 NFs show an optimal response to 100 ppm xylene. All these sensors are selective in the presence of typically interfering gases such as formaldehyde, benzene and toluene. As a proof-of-concept, sensor arrays constructed using these three

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sensors precisely detected the gas mixtures of ethanol, acetone and xylene by means of matrix manipulation, delivering a superior accuracy of <9% deviation for the gas

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concentration of 10 ppm and 20 ppm and <38% for the concentration of 5 ppm. These results show the possibility of improved selectivity in detecting gas mixtures using

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heterostructured gas-sensing materials for sensor arrays.

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Keywords: SnO2/MOx heterostructures, nanofibers and nanotubes, selectivity, gas

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mixture, sensor arrays

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1. Introduction

Due to their superior sensitivity resulting from their highly specific surface area

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and excellent chemical stability, one-dimensional SnO2 nanostructures have been widely adopted as the gas-sensing material used to detect inflammable, toxic gases

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[1-10]. In particular, volatile organic compounds (VOCs) have been extensively investigated with many types of gas sensors so they can be detected in-situ because of their poisonousness to both the environment and human beings [11-16]. Recently,

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highly sensitive VOC sensors have become available, such as the 1-D SnO2-based sensor with high response of 60.6 to 50 ppm acetone [17]. However, an intrinsic drawback of this kind of sensor is its low selectivity, which means it is highly sensitive to both the target gas and certain interfering gases with similar properties. For example, the aforementioned, highly-sensitive acetone sensor also shows a high

response of 23 to 50 ppm ethanol and a response of 13.88 to 50 ppm toluene [17]. Therefore, an improvement of the selectivity of metal oxide gas sensors is needed for the detection of both a single gas and gas mixtures. In the literature, selectivity is generally enhanced using two strategies. The first strategy is morphology modulation, surface functionalization, etc. in an aim to tune

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the sensitivity and selectivity of a single sensor [5, 18-21]. The formed heterostructures, not only accelerate the electron-hole pairs separation resulting from

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the interfacial depletion layer and enhance their sensitivity, but also vary the surface’s chemical properties (i.e. surface acid-base property and chemical active sites) and

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band structure (i.e. the bend of energy level and the decrease of carrier height) to

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induce a change of selectivity [8, 22]. However, this technology is always not very

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effective in enhancing the selectivity because gases such as ethanol and acetone show

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similar responses. The second strategy is to fabricate the sensor array to separate the multi-gases signals by complex signal processing techniques such as Principal

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Component Analysis [23, 24] and Artificial Neural Networks [25-27], etc. These sensor arrays involve complicated calculations from highly precise measurements like

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successful qualitative determination of different alcohols, but are not in good at quantitative detection [28, 29].

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In this work, we present a facile method to fabricate porous SnO2 NTs and

SnO2/MOx (SMO, M=Zn, Ga and W, denoted as SZO, SGO and SWO)

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heterostructured NTs and NFs via a one-step, electrospinning technology and subsequent calcination. The formation of heterostructured NTs not only enhance the surface area, but also tune the acidity/alkalinity of the sample by the addition of different MOx. Accordingly, the responses of SZO NTs to 100 ppm ethanol and acetone are greatly enhanced when compared with those of SnO2 NTs. Likewise,

SGO and SWO nanostructures show enhanced, selective responses to ethanol and xylene respectively in the presence of interfering gases such as formaldehyde, benzene and toluene. These three SMO sensors were utilized to construct a sensor array to detect and precisely calculate the concentration of a mixture of ethanol, acetone and xylene by means of simple matrix manipulation. Results obtained showed

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a negligible deviation of <9% for the gas concentration of 10 ppm and 20 ppm, as

well as an acceptable deviation of <38% for a gas concentration of 5 ppm. All these

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results show the effectiveness of the in-situ fabrication of these heterostructures by an

electrospinning method, which show great potential for gas sensor array integration in

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mixed-gas detections.

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2.1 Preparation of the Electrospun Precursor

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2. Materials and Methods

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The SnO2 electrospun precursor solution was prepared by dissolving 0.3 g stannous chloride (SnCl2·2H2O, Aladdin, AR, 99.9%) and 1.5 g Polyvinylpyrrolidone (PVP,

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Aladdin, K88-96) into 10 g N,N-dimethylformamide (DMF, Aladdin, AR, 99.5%). The SnO2/MOx (M= Zn, Ga, W) electrospun precursors were prepared by dissolving

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0.15 g SnCl2, 0.15 g salts containing M elements and 1.5 g PVP into 10 g DMF. The

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M elements were zinc chloride (ZnCl2), gallium nitrate (Ga(NO3)3), and ammonium metatungstate (H28N6O41W12). Then, the above precursor solutions were stirred for 12 h at 25 oC.

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2.2 Electrospun Process The uniform precursor solution was put into a syringe with a stainless steel needle

of 0.5 mm inner diameter. The horizontal distance between the stainless steel needle and grounded substrate was held at 12 cm. The positive accelerating voltage of 15 kV was applied between the needle and the collector, and the injection rate was set as 0.2

mL h−1. Then, the NFs were obtained on the collector substrate with a proper spinning time of 5 h. All the above mentioned experimental processes were conducted at room temperature with a relative humidity of 30–50%. In order to remove PVP and obtain polycrystalline SnO2 and SMO NTs or NFs, the prepared initial NFs were annealed at 600 oC for 5 h with a heating rate of 2 oC min-1. Finally, the targeted materials were

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prepared. 2.3 Materials Characterization

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The morphology of the prepared materials was investigated by scanning electron

microscope (SEM, JSM-7001F+INCA X-MAX), and transmission electron

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microscope (TEM, JEM-2100F). The crystal structures were analyzed by X-ray

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diffraction (XRD, X'Pert PRO MPD) and the elemental analyses were performed by

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X-ray photoelectron spectroscopy (XPS, ESCALAB 250, Al K radiation).

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Additionally, nitrogen absorption-desorption isotherm was performed on a specific area and a pore-size analyzer (SSA-7300, BUILDER) was used to analyze the surface

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area and pore size distribution by the Brunauer-Emmett-Teller (BET) method and Barett-Joyner-Halenda (BJH) model, respectively.

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2.4 Devices Fabrication and Measurement

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As-prepared materials (~5 mg) were then mixed with ethanol (~100 μL), and dispersed uniformly by ultrasonic. The dispersed solution was coated onto a ceramic plate with Pt wires (i.e. heater and measurer), and aged under a voltage of 5 V for

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three days in air. Next, the sensor was placed into an air chamber (18 L) and heated to different working temperatures. The ethanol, acetone and formaldehyde gases are prepared by dropping pure liquid ethanol, pure acetone, or 40% formaldehyde aqueous solution on an evaporation stage. Finally, the prepared devices were measured under a gas sensor analyzer (Winsen Electronics, WS-30A, Henan, China).

The distinction between the sensor resistance in air and that in an air-VOCs mixture was measured.

3. Results and discussions The as-spun PVP/Sn NF precursors have a uniform diameter of 200~300 nm and smooth surface as displayed in the SEM images in Figure S1, which are then annealed

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at high temperature (~600 oC) to obtain the SnO2 NTs. As demonstrated in schematic diagram in Figure 1, the formation of NTs and NFs depends on the inward and

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outward diffusion rates causing Kirkendall effects during the subsequent calcination

process. If the outward diffusion rate of Sn/M composites (defined as JA) is larger

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than the inward diffusion rate of organics (defined as JB), the final product will be

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perfect NT due to Ostwald ripening [30]. For example, the JA of Sn/Zn and Sn/Ga is

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larger than the JB of the organics in initial fiber due to the higher solubility in DMF

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compared with that of PVP [31], thus the electrospun product is NT after calcination. Conversely, the JA of Sn/W is smaller than JB of organics, and Sn/W ions will

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compensate the voids caused by PVP degradation which diffuses outward during the calcination process, thus forming dense NFs [32].

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Figures 2a~c are the SEM images of SnO2, SZO and SGO NTs after calcinations

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which show a typically hollow morphology, a distinct difference from the SWO NFs shown in Figure 2d. To further verify the microstructures of the products, TEM images were obtained as shown in Figure 3. The as-spun SnO2 in Figure 3a exhibits a

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typical hollow morphology with an inner diameter of 136.5 nm, a wall of ~35.8 nm and relatively large mesopores. The zoomed-in HRTEM in Figure 3b confirms the existence of SnO2. Similarly, as displayed in Figure 3c, the prepared SZO also exhibits the NT morphology with an inner diameter of 191.7 nm, a wall of 121.1 nm, and many mesopores (<10 nm approximately) on the surface. The existence of ZnO

and SnO2 grains is evident in Figure 3d. However, no Ga2O3 grains can be observed in the TEM and HRTEM images in Figure 3e-f, though SGO also demonstrates the NT structure with a thin wall of 8.0 nm and a small inner-diameter of 55.7 nm. In contrast, the SWO demonstrates a dense NF structure composed of SnO2 and WO3 nano-grains as shown in Figure 3g-h.

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In order to further assess the crystal phases, XRD spectra conducted on a X-ray diffractometer with Cu Kα radiation (λ=0.15418 nm) were obtained as shown in

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Figure 4a. It is noted that the XRD spectrum of pure SnO2 NTs exhibits six peaks at

26.6o, 33.9o, 37.9o, 42.6o, 54.7o, and 57.8o, corresponding well to the tetragonal SnO2

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phase (110), (101), (200), (211), (220) and (002) lattice planes (JCPDS#1-1445). And

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there are no other peaks observed within the resolution of XRD, inferring the pure

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phase of the SnO2 NT. At the same time, in addition to the SnO2 phase, the XRD

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spectra of SZO NTs and SWO NFs reveal hexagonal ZnO and monoclinic WO3 phases, corresponding well to JCPDS No.36-1451 and JCPDS No.43-1035,

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respectively. All these indicate that SZO and SWO are composed of SnO2 and ZnO/WO3 grains, corresponding well with the HRTEM observations. Notably, unlike

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SZO NTs and SWO NFs, there are only SnO2 peaks in the spectrum of SGO NTs and no Ga2O3 peaks observed, which might be resulted from the amorphous structure of

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Ga2O3. To verify the existence of Ga2O3, we synthesized pure Ga2O3 without SnO2 as shown in Figure S2. The pure Ga2O3 has an amorphous characterization with no XRD

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peaks, which would be the reason why it was not observed by the HRTEM or XRD [33]. It is also noted that the full width at half maximum (FWHM, β) of the SMO XRD peaks are all larger than those of pure SnO2 NTs, suggesting the prohibited crystallization and decreased crystallite size. The various crystallite sizes in

electrospun SMO NTs and NFs are also studied by Scherrer equation: D = K/cos, where D is the grain size, K is Scherrer constant, β is the FWHM obtained from XRD spectra and θ is the diffraction angle. As revealed in Figure 4b, the calculated SnO 2 crystallite sizes of pure SnO2, SZO and SGO NTs, and SWO NFs are 26.9, 10.2, 7.1 and 7.4 nm, respectively. The grain sizes are also measured from HRTEM images of

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SnO2, SZO, SGO and SWO and found to be 30.5±4, 13.6±2.5, 5.5±1.5 and 6.5±1.8

nm respectively as shown in the statistics of the fifty nano-grains in Figure 4b. The

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calculated ZnO and WO3 crystallite sizes are 9 nm and 5 nm respectively, which are

also similar to the observed results from the HRTEM images. A Ga2O3 crystallite size

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was not obtained by the Scherrer equation, since the Ga2O3 peaks in XRD spectrum

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were non-existence. When doped by other metal elements, the grain size and

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crystallite size became remarkably smaller than those of pure SnO2 NTs as

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demonstrated by both HRTEM and XRD measurements. This phenomenon is consistent with reports demonstrating that In2O3, ZnO, TiO2 and WO3 can effectively

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limit the growth of SnO2 host nanoparticle during the high temperature crystallization [22]. These MOx exist as a heterostructure coating on the SnO2 nanocrystallites and

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act as a barrier to prevent the development of SnO2 grain boundaries and limit the

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growth of SnO2 grains [34-36]. These results may enhance gas sensitivity, because the small grain size can enlarge the depletion region caused by the touch of grains, thus accelerating the separation of electron-hole pairs.

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XPS was obtained to further verify the elements and analyze the heterojunction

structures in the prepared materials. As displayed in the typical XPS of SnO 2 NTs and SMOs in Figure 5a, Sn/O/C elements are observed in all four kinds of materials. Specifically, Zn, Ga and W elements are demonstrated by individual full spectrum, as well as the high resolution spectra in Figure S4 a~c, verifying again the composite

materials. With the purpose of evaluating the formation of depletion layer and the change of band structure, Sn3d XPS spectra were performed and contrasted in Figure 5b. The Sn3d spectra of SZO, SGO and SWO shift to lower binding energy by 0.22, 0.24 and 0.5 eV compared with that of SnO2 NTs, which would be a result of the electron transfer between the heterojunctions. This formation of depletion layer plays

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an important role in accelerating the separation of electron-hole pairs, which would further improve gas sensitivity. As displayed in Figure S3, when the SMO

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hetrostructures were exposed to the reducing gases, the potential barrier and the

depletion layer narrowed due to the carriers generated by the gases and electrons

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transferred from MOx to SnO2. This facilitated the formation of a charge transfer state

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and the separation of the carrier pairs generated by the targeted gas to keep the

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balance of the built-in field in the depletion layer [37].

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To assess the specific surface areas and pore size distributions, nitrogen absorption-desorption was employed, of which the results are shown in Figure S5.

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The specific areas were measured by BET model to be 39.8, 55.6, 583.8 and 37.3 m2 g-1 for SnO2, SZO, SGO NTs and SWO NFs, respectively, indicating a relatively large

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surface area as displayed in Table 1. SGO NTs exhibited a superior specific area to SnO2 and SZO NTs, which can be attributed to their small diameter pores, light

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density and the amorphous structure of Ga2O3. In order to further investigate the aforesaid mesoporous characteristics, the pore size distributions were tested using the

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BJH model. The average pore size obtained was 111.2, 89.7, 46 and 40.5 Å respectively, as shown in Table 1. In addition, the total pore volume was measured to be 0.22, 0.25, 1.34 and 0.19 cm3 g-1 individually, as revealed in Table 1. In a sense, these results also prove that the large surface area of SGO NTs is partly a result of the mesoporous with small size and thin diameter. Overall, the enlarged specific area

enhanced the effective contact-area between the targeted gas and the surface of NTs, and further improved the gas sensitivity. Apart from their physical properties, the surface chemical characteristics were also taken into consideration, and NH3 and CO2 temperature-programed desorption (NH3and CO2-TPD) were employed to illustrate the change in the surface acid-base

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properties. The amount of acidic sites was measured by the integral method to be 61.3, 124, 260 and 169 mmol mg-1 for SnO2, SZO, SGO NTs and SWO NFs respectively,

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as shown in Figure 6. Similarly, the amount of basicity sites was observed to be 68,

111, 290 and 67 mmol mg-1 in turn. The large amount of basicity sites of SGO and

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SZO was not only caused by the enlargement of the specific area, but also the

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influence between the heterojunctions. On one hand, the basic sites of SnO2 were

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increased due to the electrons transfer from MOx to SnO2, as observed from the shift

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of Sn3d to the lower binding energy as revealed in Figure 5b. On the other hand, the basicity or acidity sites of MOx also greatly affected the total amount of basicity or

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acidity sites. It is worth noting that the difference in the acidic and basic sites (i.e. the amount of acidic sites subtract the amount of basic sites) is -6.7, 13, -30, and 102

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mmol mg-1, showing the decreased relative acidity of SGO and increased relative alkalinity of SWO. The influence of the difference of surface acidic or basic amounts

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to gas selectivity is discussed in detail in the subsequent section. In an effort to demonstrate the sensitivity/selectivity of prepared sensors to various

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VOCs, and to obtain the optimal operation temperatures and doping concentrations, the responses of SMOs to VOCs were measured at different temperatures in ambient as depicted in Figure S6 and Figure 7a. For comparison, the optimal sensors were determined to have a doping concentration of 1:1 (weight ratio of M:Sn), operated at an optimized working temperature of 300 oC. Shown in Figure 7a, SnO2 NTs and

SZO NTs sensors show the highest response (defined as Rair/Rgas) to 100 ppm ethanol (10 and 31.6) and 100 ppm acetone (9.39 and 22.7), indicating a remarkably enhanced response. Notably, when compared with pure SnO2 NTs sensor, the introduction of elemental Ga improves the response to ethanol but not to acetone, supporting the result that gas selectivity can be attributed to the superior sensing effect of Ga2O3. In

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addition, SWO NFs sensors showed a superior response of 9.8 to100 ppm xylene at

300 oC. In order to evaluate the dynamic response to various concentrations, dynamic

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tests of 10 ppm to 100 ppm ethanol, acetone and xylene were performed as shown in

Figure 7b~d, and the corresponding actual sensors resistances are displayed in Figure

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S7. The SGO and SZO NTs sensors showed high response to ethanol, the SZO NTs

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exhibited superior response to acetone, and the SWO NFs sensors demonstrated a

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high response to xylene. To further explore the relationship between response and gas

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concentration, linear fitting is depicted in Figure S8a and b. The fitted slopes of response to ethanol showed gradients of 0.312, 0.365 and 0.04, and the fitting slopes

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of response to acetone showed gradients of 0.210, 0.06 and 0 for SZO, SGO and SWO respectively. The fitting slope of response to xylene for SWO NFs, shown in

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Figure S9, showed a gradient of 0.118, and the response of SZO and SGO NTs can be ignored. These slope values play an important role in the following mixed gas

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concentration calculations by the sensor array. Considering the significance of response and recovery speed for the practical

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applications of sensors, these were investigated as displayed in Figure 8. In the response curves of the SZO NTs sensors to 100 ppm ethanol in Figure 8, the response time and recovery time are 10 s and 56 s respectively, illustrating a very fast gas response and recovery. SZO NTs sensors also exhibit a fast response to 100 ppm acetone with response and recovery time of 5 s and 10 s respectively. The response

and recovery speeds for the SGO NTs sensor to ethanol are the same as those of the SZO NTs sensor, which was all lower than 60 s. Additionally, the response and recovery of the SWO NFs sensor to 100 ppm xylene are also found to be 12 s and 12 s, indicating a relatively fast speed. Selectivity is always the key factor in the choice of sensors for mixed gas detection.

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For sensor arrays used in the detection of mixed gas, sensors with different selectivity may also be required, which is also a critical challenge to solve in this work. In order

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to evaluate the selectivity of fabricated sensors, three other typical VOCs (i.e. toluene, benzene and formaldehyde) were utilized to examine the sensors’ selectivity. Notably,

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all of these devices exhibit a small response to water vapor, and the interference of

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water vapor in 40% formaldehyde aqueous solution can be excluded as demonstrated

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in Figure S10. As shown in Figure 9a, these gases have limited interference with the

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materials. This is similar to the literatures shown in Table S2 that, SnO2/In2O3 NFs exhibit a superior selectivity to formaldehyde and SnO2/TiO2 NFs show a preferable

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selectivity to acetone [38, 39]. Stability is another problem that must be considered in practical applications. The response of the SZO NTs sensor to ethanol is changed

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from an initial value of 31.6 to 30.4 (<5%) after thirty days, as indicated in Figure 9b. Likewise, the SGO sensors and SWO sensors all show fantastic stability. All of these

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indicate that selectivity can be controlled by doping various metal elements. And these results provide a foundation for the construction of sensor arrays that can be

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used to precisely detect gas concentrations in a mixed gas. Next, the mechanism of the selectivity change was demonstrated in details. To the

best of our knowledge, the surface acidic and basic sites play an important role in alcohol degradation [40]. An acidic oxide surface favors dehydration and a basic surface favors dehydrogenation [22]. The conversion of ethanol to CO2 and H2O is a

dehydrogenation process, so high basic amounts of SGO is beneficial to the degradation of ethanol, while low basic amount of SWO slows the degradation of ethanol. With the SZO NTs sensor, the difference value is so close to pure SnO2 NTs that it maintains a high response to ethanol. Additionally, ZnO has been documented to have good sensitivity to ethanol in previous studies [22]. Therefore, the

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introduction of ZnO grains has a synergistic effect with the SnO2 grains in the degradation of ethanol, and SnO2/ZnO heterojunctions are always employed as

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alcohol sensors. Likewise, ZnO is also sensitive to acetone, so the synergistic effect of SnO2 and ZnO grains gave an enhanced response to acetone [22]. While Ga2O3 and

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WO3 are not sensitive to acetone, discrete SnO2 grains exhibit a poor response to

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acetone. It is worth noting that SWO NFs sensors are selective for xylene, which can

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be attributed to the sensitivity of WO3 to xylene [41]. During the process of xylene

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degradation, SnO2 accepts electrons from WO3 and increases the density of charge carriers, which enhance the gas response. Finally, the design of the SMO

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heterojunctions increased the gas selectivity as expected. Fixed or uncontrollable gas selectivity is always the key challenge for conventional

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sensors, hindering their application in mixed gases detection. To solve this problem, the sensor array was constructed using SMO sensors. For mixed gas detection, the

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sensor array must have the same number of sensors as the number of gas types in mixed gas, and each sensor in the array must possess a different selectivity to the

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VOCs present in the system. In order to verify the feasibility of the sensor array, three concentrations of mixed gases including ethanol, acetone and xylene (i.e. I: 5ppm-5ppm-5ppm; II: 10 ppm-10ppm-10ppm; III: 20ppm-20ppm-20ppm), were selected as detection objects shown by the sketch map in Figure 10a. In this gas mixture with ethanol, acetone and xylene concentrations of x1, x2 and x3, the (Rair/Rgas

-1) of SZO, SGO and SWO are R1, R2 and R3, obtained from Figure S11 and displayed in Table S1. In addition, the corresponding slopes were defined as kmn (i.e. m=1, 2, 3; n=1, 2, 3).Therefore, (1) (2) (3)

And the gas concentrations can be calculated as:

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x1 R1 0.312 0.21 0 x [0.365 0.06 0 ] × [ 2 ] = [R2 ] x3 R3 0.04 0 0.118

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k11 x1 +k12 x2 +k13 x3=R1 k21 x1 +k22 x2 +k23 x3=R2 k31 x1 +k32 x2 +k33 x3=R3

Finally, the results are obtained by means of the matrix calculation displayed in

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Figure 10. As displayed in the detection results in Figure 10b, the calculated gas

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concentrations have a low deviation of <9% compared with actual gas concentration

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of mixture II and III, and an acceptably low deviation of <38% for mixture I. All these

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results indicate great potential in future detections of the VOCs mixtures by the SMO

4. Conclusion

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gas sensors array.

One dimensional SnO2, heterostructured SnO2/ZnO NTs, SnO2/Ga2O3 NTs, and

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SnO2/WO3 NFs were fabricated by a low-cost and effective one-step electrospinning

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method. The heterostructures had tuned surface area and surface acidity/alkalinity as characterized by the NH3 and CO2 temperature-programmed desorption, which showed relatively high selectivity to the targeted gases such as ethanol, acetone and

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xylene as compared with the low response to formaldehyde, benzene and toluene. Specifically, the SZO NTs sensor showed improved response to both ethanol and acetone compared with pure SnO2 NTs, SGO NTs showed a high response to ethanol only, and SWO NFs showed a high response selectively to xylene. These three selective sensors were integrated into a sensor array, which qualitatively detected the

concentrations of ethanol, acetone and xylene (mixture II: 10, 10, 10 ppm and mixture III: 20, 20, 20 ppm, respectively) in a mixture with a low deviation of <9%. More importantly, even in the low concentration of the ethanol, acetone and xylene mixture (mixture I: 5, 5, 5 ppm), the calculated results demonstrated an acceptably low deviation of <38%. These results show that the heterostructure design improves the

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selectivity of metal oxide gas sensors and can be fabricated into sensor arrays for the

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detection of gas mixtures.

Formatting of funding sources

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This research did not receive any specific grant from funding agencies in the public,

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commercial, or not-for-profit sectors.

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Appendix A. Supplementary data

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Supporting Information. The additional SEM images, XRD, XPS, schematic diagram and testing data of VOCs mixtures are available in the supporting information for this

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paper.

Acknowledgements

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The work was financially supported by the Natural Science Foundation of

Shandong Province, China (ZR2018JL021, ZR2014EMQ011), the National Natural

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Science Foundation of China (51402160), and the National Key R& D Program of China (2016YFC0207100). The work was also supported by the Taishan Scholar Program of Shandong Province, China, National Demonstration Center for Experimental Applied Physics Education (Qingdao University), and the State Key Laboratory of Multiphase Complex Systems (MPCS-2015-A-04).

Authors Biographies

Longfei Song Longfei Song received the B.S. degree in microelectronic from the Qingdao University

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in 2016. He is currently a postgraduate in Qingdao University.

Liping Yang

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Liping Yang received the pH.D. degree in Institute of chemistry, CAS in 2016. She is currently a Post-Doctoral Fellow with the Institute of Process Engineering, CAS.

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Zhou Wang

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Zhou Wang received the B.S. degree in chemical engineering and technology from the

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Wuhan Institute of Technology. He received the M.S. degree with the Beijing

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University of Chemical Technology in 2018. He is currently working in Xiamen Leading

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Optics Co., LTD.

Di Liu

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Di Liu received the B.S. degree in Anhui University of Technology in 2016. He is

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currently a postgraduate in Qingdao University.

Linqu Luo

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Linqu Luo received the B.S. degree in microelectronic from the Qingdao University in 2016. He is currently a postgraduate in Qingdao University.

Xinxu Zhu

Xinxu Zhu received the B.S. degree in Polymer Materials Science and Engineering from the Changzhou University in 2017. He is currently a postgraduate in Qingdao University.

Yan Xi

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Yan Xi received the pH.D. degree in Shangdong University in 2013. She is currently a

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Post-Doctoral Fellow with the Qingdao University.

Zaixing Yang

Zaixing Yang received the pH.D. degree Nanjing University in 2012. He was a

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Post-Doctoral Fellow with the Department of Physics and Materials Science, City University of Hong Kong, from 2012 to 2016. He has been a Professor in Shangdong

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University. He is works on investigations of preparation, structure, and property of

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semiconductors. He has authored over 40 articles.

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Ning Han

Ning Han received the pH.D. degree in chemical engineering from the Institute of Process Engineering, CAS, in 2010. He was a Post-Doctoral Fellow with the

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Department of Physics and Materials Science, City University of Hong Kong, from 2010 to 2014. He has been a Professor with IPE CAS via One Hundred Talents Plan since

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2014. He is works on investigations of preparation, structure, and property of semiconductors, and has developed highly sensitive and selective gas sensing

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materials. He has authored over 80 articles.

Fengyun Wang Fengyun Wang received her degree in Physics and Materials Science from City University of Hong Kong in 2012. She was a postdoctoral fellow in Department of Physics and Materials Science in City University of Hong Kong in 2012-2013, and is now a professor in College of Physics and Cultivation Base for State Key Laboratory,

Qingdao University since 2013. Her research interest includes preparation of III-V compound semiconductors and metal oxide semiconductors, and applications in gas sensors, electronics and optoelectronics. He has published more than 40 peer reviewed journal articles (with more than 300 citations), and held 1 Chinese patent.

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Yunfa Chen Yunfa Chen received the pH.D. degree in material science from the Université Louis Pasteur Strasbourg, France, in 1993. He is currently a Professor with the Graduate

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University of Chinese Academy of Sciences, and a Research Professor and the Vice Director of the Institute of Process Engineering, Chinese Academy of Sciences. His current research interests are the preparation and assembly of nanoparticles,

functional materials, organic–inorganic composite materials and layered materials, and

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industrial application of nanomaterials.

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VOCs gases using sensor array with neural networks. IEEE Sens. J. 16(2016) 6081-6086. [28] M. G. Campbell, S. F. Liu, T. M. Swager, M. Dinca. Chemiresistive Sensor Arrays from Conductive 2D Metal-Organic Frameworks. J. Am. Chem. Soc. 137(2015) 13780-13783.

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gas-sensing properties of SnO2–In2O3 nanocomposite oxides. Sensor. Actuat. B Chem.

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ZnO-SnO2 heterojunction nanofibers. Adv. Mater. 25(2013) 4625-4630. [38] H. Du, J. Wang, M. Su, P. Yao, Y. Zheng, N. Yu. Formaldehyde gas sensor based on SnO2 /In2O3 hetero-nanofibers by a modified double jets electrospinning process. Sensor.

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Figure 1. Schematic diagram of the electrospun process and the NT and NF formation

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processes.

Figure 2. SEM images of different NFs and NTs after thermal process. (a) SnO2 NTs;

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(b) SZO NTs (c) SGO NTs and (d) SWO NFs.

Figure 3. (a), (c), (e) and (g) TEM images of SnO2, SZO, SGO NT and SWO NF. (b),

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(d), (f) and (h) HRTEM images of SnO2, SZO, SGO NT and SWO NF.

Figure 4. (a) XRD spectra of SnO2, SZO, SGO NT, and SWO NF. (b) Different nanocrystallite sizes of SnO2, SZO, and SGO NT and SWO NF calculated by Scherrer

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Formula and measured from HRTEM, respectively.

Figure 5. (a) XPS spectra of SnO2, SZO, SGO NTs and SWO NFs. (b) The XPS

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spectra of Sn3d peaks.

Figure 6. Amount of acidic and basic sites of SnO2, SZO and SGO NTs, and SWO

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NFs.

Figure 7. (a) Response of SnO2, SZO and SGO NTs, and SWO NFs to ethanol and acetone at various working temperatures, and dynamic response of SnO2, SZO and

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SGO NTs, and SWO NFs to (b) ethanol, (c), acetone and (d) xylene at 300 oC.

Figure 8. (a) and (b) Response and recovery time of SZO NTs to 100 ppm ethanol and acetone, respectively at 300 oC. (c) Response and recovery time of SGO NTs to 100 ppm ethanol at 300 oC. (d) Response and recovery time of SWO NFs to 100 ppm

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xylene at 300 oC.

Figure 9. (a) Selectivity test of SnO2, SZO and SGO NTs, and SWO NFs sensors towards 100 ppm ethanol, acetone, toluene, xylene, formaldehyde and benzene at 300 o

C. (b) Stability test of SZO NTs sensors towards 100 ppm ethanol and acetone, SGO

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NTs sensors towards 100 ppm ethanol, and SWO NFs sensors towards 100 ppm

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xylene at 300 oC.

Figure 10. (a) Sketch map of the detection of mixed gases by SMO sensors. (b) Mixed VOCs detection of ethanol, acetone and xylene by SZO, SGO NTs and SWO NFs sensor array. (Mixed gas I: ethanol: acetone : xylene = 5 ppm : 5 ppm : 5 ppm;

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Mixed gas II: ethanol: acetone : xylene = 10 ppm : 10 ppm : 10 ppm; Mixed gas III:

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ethanol : acetone : xylene = 20 ppm : 20 ppm : 20 ppm.)

Table 1. BET specific area and BJH average pore size of SnO2, SZO and SGO NTs, and SWO NFs.

BET specific area

BJH average pore size

(m2/g)

Total pore volume

(Å)

(cc/g)

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39.8

111.2

0.22

SZO NTs

55.6

89.7

0.25

SGO NTs

583.8

46

SWO NFs

37.3

40.5

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SnO2 NTs

1.34

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0.19