Hydrothermal synthesis of one-dimensional α-MoO3 nanomaterials and its unique sensing mechanism for ethanol

Hongpeng Xu; Chenhui Liu; C. Srinivasakannan; Minghong Chen; Qian Wang; Lingbo Li; Ying Dai

doi:10.1016/j.arabjc.2022.104083

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Original article

09 2022

:15;

104083

doi:

10.1016/j.arabjc.2022.104083

Hydrothermal synthesis of one-dimensional α-MoO₃ nanomaterials and its unique sensing mechanism for ethanol

Hongpeng Xu^{^a,^b}, Chenhui Liu^{^a,^b,⁎}, C. Srinivasakannan^{^c}, Minghong Chen^{^a,⁎}, Qian Wang^{^a,^b}, Lingbo Li^{^a,^b}, Ying Dai^{^a}

a School of Chemistry and Environment, Yunnan Minzu University, Kunming 650500, PR China

b Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming 650093, PR China

c Chemical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates

⁎Corresponding authors at: School of Chemistry and Environment, Yunnan Minzu University, Kunming 650500, PR China (C. Liu). liu-chenhui@hotmail.com (Chenhui Liu), cmhkm99@163.com (Minghong Chen)

Received: 2022-5-10, Accepted: 2022-6-26,

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

In gas sensor applications, the availability of highly sensitive and rapid response/recovery detector for ethanol gas is sparse. One-dimensional orthogonal crystalline molybdenum trioxide nanomaterials were synthesized by an economical and environmentally friendly hydrothermal method. X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and energy spectroscopy (EDS) were used to investigate the structure and morphology of the nanometer materials. The relevant characterization shows that nanobelts are highly crystalline layered structures with a width of about 200 nm and a length of a few micrometers. The synthesized ethanol gas sensors based on α-MoO₃ semiconductor material show the highest response at 350 °C. Gas sensitivity tests indicated that α-MoO₃ nanobelts respond well to 50 ∼ 600 ppm ethanol at optimal operating temperatures. The selectivity test among various reducing gases shows that the sensor responds better to ethanol compared to other gases such as xylene, NO₂, CO, and H₂ gases. This excellent sensing performance is attributed to the unique sensing mechanism formed in the layered MoO₃ nanobelts through the catalytic reaction between ethanol and MoO₃ lattice oxygen and adsorbed oxygen. The sensing mechanism of the co-catalytic effect of lattice oxygen and adsorbed oxygen on ethanol is also discussed in depth.

Keywords

α-MoO3 nanobelt

Hydrothermal synthesis method

Gas sensing properties

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1 Introduction

Volatile organic compounds (VOCs) are formed by the evaporation of organic compounds, which are composed of hydrocarbons and their derivatives. According to their chemical structures, they can be divided into aromatic hydrocarbons (such as benzene, toluene, and xylene), and aliphatic hydrocarbons (such as ethylene and butane), alcohols, aldehydes, carboxylic acids, and other categories (Bouricha et al., 2021). The risks to human health from organic emissions are manifold and vary in toxicity. For example, benzene vapors can damage the central nervous system, causing neurological disorders; poly aromatic hydrocarbons (PAH) are strongly carcinogenic; benzoic acid can deform or coagulate cellular proteins, resulting in systemic poisoning. According to the national comprehensive emission standards for air pollutants (GB-16297–1996), the maximum allowable emission concentration and the maximum allowable emission rate of VOCs are 150 mg/m³ and 6.3 kg/h. Therefore, Rapid real-time detection of VOC levels is critical for air quality testing and human health monitoring. However, the existing detection methods rely on expensive laboratory equipment such as gas chromatography (GC) and mass spectrometry (MS), and luminescence methods (Yin et al., 2022). Hence, convenient and quick detection of harmful gases for VOCs is still a key challenge in protecting lives.

Recently, semiconductor metal oxide sensors (chemi-resistors) were reported to be an attractive option for effective real-time detection due to their unique advantages of high sensitivity, fast response time, short recovery time, low cost and low power consumption (Barsan et al., 2007). Most oxide-based gas sensors work based on a chemosensitive (conductivity) mechanism, where the resistance of the sensing layer varies with the adsorption of the target gas molecules. This sensing mechanism is based on the interfacial charge transfer between the metal oxide and the adsorbed gas molecules. Molybdenum trioxide (MoO₃) is an n-type semiconductor with a band gap energy of 2.39–2.9 eV. Its crystalline structure is known to have angle/edge/face-sharing MoO₆ octahedron units which are capable of forming a unique layer/tunnel structure. (MoO₆) octahedra are con angular connected in the direction (1 0 0) to form a long chain. These chains are connected with each other in the direction of (0 0 1) to form a monolayer structure. Finally, these layers are combined with Johannes Diderik van der Waals forces to form a multilayer MoO₃ (Barsan et al., 2007, Dewangan et al., 2011). Its unique layered structure and abundant active sites are widely used in lithium-ion batteries (Wu et al., 2017), organic light-emitting diodes (Yeh et al., 2018) and water supercapacitors (Zhao et al., 2020). The MoO₃-based material sensor also exhibits a good response in detecting various VOC’s such as toluene, xylene (Wang et al., 2020), formaldehyde (Martins et al., 2007), and trimethylamine (Zhou et al., 2021), but the MoO₃ sensor show the better response for ethanol compared to other VOCs, with excellent selectivity. Yueli Liu et al. have used a template-free hydrothermal method to prepare h - MoO₃ nanorods with a detection limit of 5 ppm for ethanol at an operating temperature of 332 °C and a sensitivity of 1. The sensitivities of 8.24, 1.57, and 3.17 for 500 ppm C₂H₅OH, NH₃, and CH₃OH were reported at the optimum operating temperature, demonstrating its good selectivity for ethanol (Liu et al., 2015). Shuang Yang et al. have reported that the sensor response of Zn-doped MoO₃ nanobelts to 1000 ppm ethanol was about 15 times higher than that of pure MoO₃ nanobelts and had a shorter response recovery time (Yang et al., 2017).

It is known that the sensitivity characteristics of these sensors strongly depend on the microscopic morphology of the metal oxide semiconductor. Compared with other morphologies of MOS nanostructures, one-dimensional structures are receiving increasing attention as they can provide direct pathways for axial electron transport, high crystallinity, large specific surface area, etc., and are considered a high-performance sensing material (Sui et al., 2015). Notably, the sensing mechanism of molybdenum trioxide differs from the conventional surface chemisorption mechanism of other metal oxides by using a lattice oxygen reaction (Kumar et al., 2020). The lattice oxygen reaction mechanism shows that the decrease in resistance is due to the reaction of the lattice oxygen with the reducing agent to generate free electrons from the oxygen vacancies formed by the partial reduction of Mo⁶⁺ to Mo⁵⁺ (Illyaskutty et al., 2013). Therefore, MoO₃ is considered a promising material for practical sensing of VOCs. Combing the above factors, an attempt was made to prepare pure molybdenum trioxide nanobelts structures by hydrothermal synthesis and to test its efficacy to serve as an ethanol sensor. The results show that the sensor has good sensitivity and selectivity for ethanol gas. The experimental results show that MoO₃ nanobelts have high sensitivity and good selectivity to ethanol, and the unique oxygen vacancy mechanism of MoO₃ lattice oxygen formation is discussed in depth.

2 Experimental methods

2.1

2.1 Synthesis of α-MoO₃ nanobelts

A certain amount of ammonium heptamolybdate ((NH4)₆Mo₇O₂₄·4H₂O) was calcined in a heat treatment furnace at 500 °C for 2 h. The light green micron-sized molybdenum trioxide is formed by calcination. The prepared 0.05 mol MoO₃ powder was slowly added to 4 ml of 30% H₂O₂ and 10 ml of deionized water and stirred continuously until the powder was completely dissolved. The MoO₃ dissolved slowly and the color of the solution gradually changes to yellow and transparent. The mixed solution was transferred to a Teflon-lined stainless-steel autoclave (20 ml), and the hydrothermal reaction was performed at 160 °C for 24 h. After cooling to room temperature naturally, the precipitate was separated by centrifugal filtration and washed several times with deionized water and anhydrous ethanol to remove ionic residues from the final product. After the product was completely dried, was transferred to a muffle furnace and calcined at 400 °C for 2 h to obtain the target product.

2.2

2.2 Characterizations

The composition and phase of the as-prepared product were characterized using X-ray diffraction (XRD, Advance D8, Bruker Co., Ltd., Germany)) using CuKa1 radiation (λ = 0.15406 nm) at 30 kV and 40 mA at a scanning rate of 2° at 2θ min ⁻¹ ranging from 10° to 80°. The morphology and nanostructure of the products were characterized using a field emission gun scanning electron microscope (FESEM, NOVA-NANOSEM-450, America) at an accelerated voltage of 5 kV and a transmission electron microscope (FEI Talos F200S) with an accelerated voltage of 100 kV. The energy-dispersive X-ray spectroscopy (EDS) analysis was analyzed by the FESEM attachment. More details about the structures were investigated by the selected area electron diffraction (SEAD) pattern. The X-ray photoelectron spectra (XPS) were obtained on an X-ray photoelectron spectrometer to determine the electronic structure of the surface of MoO₃ nanobelts.

2.3

2.3 Fabrication of α-MoO₃ nanobelts sensors

The performance of the MoO₃ sensor was evaluated in the air using a commercial static gas sensing system (WS-30B). The α-MoO₃ nanobelts were mixed with the appropriate amount of terpineol to form a paste. A brush is utilized to uniformly coat the outer surface of the ceramic tube with four platinum electrodes, forming a thick film. The coated ceramic tubes were heat treatment at a temperature of 200 °C for 5–6 h. The coated alumina ceramic tube was soldered to the test base via four platinum leads attached to gold electrodes as shown in Fig. 1. The Ni-Cr resistor was used as a heater through the tube, so the desired constant operating temperature can be controlled by adjusting the heating voltage across the Ni-Cr resistor. The load resistor was connected in series with the sensor to form a measurement circuit as shown in Fig. 2. The heating voltage was set to 3 V and the test voltage was set to 5 V for three days of aging to enhance the stability and repeatability of the sensor before testing.

Schematic of preparation of the gas sensor.

2.4

2.4 Measurement of gas sensing performance

The performance of the MoO₃ sensor was evaluated in the air using a commercial static gas sensing system (WS-30B). The sensor material was placed in a glass chamber at the beginning and the detection target gases (ethanol, xylene and nitric oxide) were injected through a microinjector onto an evaporator plate in the sensing chamber (18 L), which mixed with air after complete evaporation. The response for different vapor concentrations was then calculated based on the density of the liquid reagent and the volume of the test vessel. The concentration of the target gas obtained from the liquid is calculated by the following equation (1).

(1)

C t g = \frac{22.4 \times ρ \times d \times V 1}{M \times V 2}

where C (ppm) is the target gas concentration, ρ(g/mL), d, M(g/mol) respectively represent the density, purity and molar mass of the target liquid reagent(M_ethanol = 46 g/ mol, ρ_ethanol = 0.789 g/cm³, M _xylene = 106.16 g/ mol, ρ_xylene = 0.86 g/cm³, M _NO = 30 g/mol, ρ_NO = 1.27 g/cm³, d_ethanol = d _xylene = d_NO = 0.99). V1 (µL) is the volume of the liquid, and V2 (L) is the volume of the glass chamber. The entire experiment was conducted at an ambient relative humidity of 42–52%, and the resistance and response of the sensors were automatically acquired by the data analysis system. The response of sensor test gas was measured by monitoring the voltage of the reference resistor (Vout put). Since the MoO₃ nanobelts exhibit an n-type semiconductor response to the reducing ethanol gas, the response coefficient of the semiconductor sensor to the target gas was defined as the sensitivity (S).

(2)

Sr = R_{air} / R_{gas}

where R_a and R_g are the steady electrical resistances of the gas sensor in air ambiance and in target gas ambiance. Then, the response time is defined as the duration required for the sensor to reach 90% of its resistance stability in the measured gas. Similarly, the recovery time refers to the duration required for the sensor to recover to 90% of the initial steady-state of its resistance after the gas is removed at a certain operating temperature.

3 Results and discussion

3.1

3.1 Structural analysis

Fig. 3(a) shows the X-ray diffraction analysis of the α-MoO₃ nanobelts. Diffraction pattern relates to high crystallinity, orthorhombic and pure phase of α-MoO₃, indexed to the orthorhombic MoO₃(α-MoO₃) of the JCPDS card (No. 05–0508 a = 3.962 Å, b = 13.858 Å and c = 3.697 Å). The sharp diffraction peaks suggest a well-crystallized material. The diffraction peak at 12.8°, 25.7°, and 39.0°, respectively, corresponding to (0 2 0), (0 4 0), and (0 6 0) crystal planes of the HKL system, which indicates the anisotropic growth of as-synthesized α-MoO₃ belts and the distinct laminar structure (Siciliano et al., 2009).

(a) XRD patterns of the MoO3 nanobelts (b) survey spectrum (c) Mo 3d core level region (d) O 1s core level region.

The weak intense diffraction peaks are observed at 2θ values of 23.32°, 27.33° 33.12°, 33.73°, 45.74°, 46.28°, and 49.24°corresponding to (1 1 0), (0 2 1), (1 0 1), (1 1 1), (2 0 0), (2 1 0) and (0 0 2) planes, respectively. These weak intense diffraction peaks indicate that the crystal growth is slow in these directions.

XPS analysis was performed to further confirm the composition and molecular structure of the compounds and analyze the elemental state of the product. XPS full spectrum indicates the presence of molybdenum oxygen and a small amount of carbon. A doublet pattern of Mo 3d can be observed, as shown in Fig. 3(c). Both the Mo 3d_5/2 and 3d_3/2 peaks are fitted to two components. The characteristic peaks of the major components at 236.18 and 233.05 eV correspond to the Mo 3d_3/2 and Mo 3d_5/2 tracks of hexavalent molybdenum (Fig. 3(c)), since these are close to the standard values of the Mo⁶⁺ ion (Mo 3d_5/2:232.5 ± 0.2 and Mo3d_3/2: 235.7 ± 0.2)(Troitskaia et al., 2010). The other peaks with lower binding energies are contributed to Mo⁵⁺ions in the lattice. The O 1s spectrum in Fig. 3(d) can be inversely folded into 2 Gaussian-Lorentz sub-peaks according to its position. The peak located at 530.7 eV is attributed to the O₂⁻ ions in the MoO₃ lattice, and that at 531 eV corresponds to the O⁻ and O₂⁻ ions in the oxygen-deficient regions resulting from oxygen vacancies. (Chen et al., 2011, Wang et al., 2016). Oxygen vacancies are oxygen atoms (oxygen ions) detached from the lattice in a metal oxide or other oxygen-containing compound, resulting in the absence of oxygen. In short, defects formed by the removal of an oxygen atom from the lattice oxygen of an oxygen vacancy metal oxide. During the contact with ethanol gas, Mo⁶⁺ is reduced to Mo⁵⁺ and a large amount of oxygen vacancies are generated.

3.2

3.2 Morphologies and microstructure of α-MoO3 nanobelts

The microstructure and morphology of the samples were further characterized by FESEM and TEM. Fig. 4(a-d) shows the FESEM images of MoO₃ nanobelts at different magnifications. The image shows that the rod-shaped product has a smooth surface and is densely and uniformly distributed. Nanobelts are neatly aligned, non-adhesive, with well-defined shapes and uniform widths. The clear lamellar structure may be seen with lengths of approximately 5–10 nm and diameters of around 100–220 nm as shown in Fig. 4(e). Fig. 4(f) shows a representative EDS spectrum of the sample at a certain position. The spectrum shows peaks of Mo, Au, and O elements, indicating the high purity of the sample without other elements. The gold peak is from the metallic gold deposited on the sample before the EDS measurement to increase the conductivity of the SEM sample.

(a) SEM image at 10,000 times (b) 20,000 times (c) 40,000 times (d) 80,000 times (e) nanobelts measure dimensions (f) EDS spectrum.

High-resolution TEM images demonstrate that the α-MoO₃ samples are nanobelt structures. These structures are identified as nanobelts because their projected width varies with the rotation of the axis, which means the cross-section of the reactor is not spherical. Since almost all nanobelts are parallel to the grid (Sunu et al., 2004), The thickness of the nanobelts is extremely difficult to correctly quantify. Fig. 5(a) and (b) show the images of single nanobelts and multiple heel nanobelts intertwined and overlapped, respectively. The distance of the lattice stripes marked in the HRTEM images are 0.395 nm and 0.36 nm (Fig. 5(c)), corresponding to α-MoO₃ (1 0 0) and (0 0 1) lattice planes, respectively (Mai et al., 2007, Huang et al., 2013). The corresponding selected area diffraction (SAED) pattern (Fig. 5(d)) shows a clear and well-aligned pattern of many spots indicating the prepared MoO₃ is a single crystal.

(a) TEM image of an individual α-MoO3 nanobelt (b) TEM images of multiple MoO3 nanobelts (c) HRTEM image of lattice fringes (d) the selective area electron diffraction pattern.

The mechanism of α-MoO₃ nanobelt formation can be explained by the anisotropic growth of the material itself. The deformed MoO₆ octahedra are interconnected by a common edge in the (0 0 1) direction (c-axis) and a common vertex in the (1 0 0) direction (a-axis), and the interaction of adjacent layers on the b-axis depends on the strong covalent bond on the c-axis (Gao et al., 2012). So from an energy point of view, MoO₃ crystals will release more energy as they grow along the (0 0 1) direction resulting in MoO₃ nanobelts tending to grow along the c-axis (Zhang et al., 2016), while corresponding with the result of the characterization.

3.3

3.3 Gas-response properties

The chemical interaction between the sensing material and the test gas is widely assumed to influence the sensor's gas response. The metal oxide semiconductor-based sensing materials can demonstrate the maximum sensor response at a given concentration of reduced gas at the optimum operating temperature. The most critical aspect of determining a material's sensing capability is its operating temperature. It is not only connected to the target gas's diffusivity, but also to the target gas's or water molecules' ability to adsorb. Furthermore, surface reactions must be activated at a certain temperature, and high temperatures must be avoided since high temperatures might cause target gas molecules to desorb quickly (Xu et al., 2016). Fig. 6(a) and (b) measure the response and resistance transients of α-MoO₃ nanomaterials to 50 ppm ethanol at operating temperatures from 250 °C to 400 °C. The figure depicts the MoO₃ sensor's reaction to 50 ppm ethanol gas as a function of operational temperature. The response tends to increase with an increase in the operational temperature up to 350 °C, while found to decrease with a further increase in temperature. The corresponding response values were 2.1, 3.5, 4.9, and 2.6 at 250 °C, 300 °C, 350 °C, and 400 °C at a concentration of 50 ppm, respectively. The constant growth in responsiveness with increasing operating temperature from 250 °C to 350 °C could be due to the thermal energy of the gas molecules to overcome the activation energy barriers of the surface processes. It is also probable that with an increase in temperature the conversion of adsorbed oxygen species will attract additional electrons from the semiconductor (Bie et al., 2007). However, at a working temperature higher than 350 °C, the process is limited by the adsorption capacity of gas molecules which causes the sensing material to be underutilized, resulting in a diminished response (Zhou et al., 2015). Fig. 6(c)and (d) show the response curves and resistance transients of the α-MoO₃ sensor for 50 ppm ethanol gas at 275 °C, 325 °C, and 375 °C to prevent inaccuracies in deriving the optimum operating temperature. The corresponding response values were 2.92, 4.02, and 3.35 at 275 °C, 325 °C, and 375 °C at a concentration of 50 ppm, respectively. This leads to the conclusion that the optimum operating temperature of the sensor is 350 °C.

(a) Response curve of α-MoO3 sensor for 50 ppm ethanol gas at 250 °C to 400 °C (b) the resistance transients of the α-MoO3 sensor for 50 ppm ethanol gas (c) response curve of α-MoO3 sensor to 50 ppm ethanol gas at 275 °C,325 °C and 375 °C (d) the resistance transients of α-MoO3 sensor to 50 ppm ethanol gas.

Based on the data presented in Fig. 6, the optimal temperature for the molybdenum trioxide sensor is 350 °C. The response recovery generated by continuously injecting ethanol gas into the measurement chamber at the optimum operating temperature is shown in Fig. 7(a). The dynamic resistance variation at different concentrations is shown in Fig. 7(b). It is clear from the images that the response of the nanomaterial-based sensor with increase with increasing ethanol gas concentration. The response increased from 5.01 to 19.8 with increase in ethanol concentration from 50 ppm to 600 ppm (5.01–19.8 for 50–600 ppm). Fig. 7(c)shows the log (S − 1) versus log (C) plot of the sensors for ethanol gas at the operating temperature of 350 °C and the corresponding linearized fit. Good linearity of response with concentration is observed. It can be indicated that the experimental data were based on pristine α-MoO₃ nanobelts and were fitted as: Y = 0.614X-0.426, with the regression coefficient R² = 0.981. Beyond that, the selectivity of our sensor to various interfering gases, such as xylene, NO₂, CO, and H₂ gases, are analyzed, as plotted in Fig. 7(d). It indicates that the fabricated sensors exhibit the highest response to ethanol gas, suggesting their reliable selectivity to ethanol gas compared with other gases. In addition to high response, response and recovery times are important for evaluating the gas-sensitive performance of sensors in real-world monitoring. In Fig. 7(e) and (f), the response/recovery times of the α-MoO₃ sensor to 50 ppm and 100 ppm ethanol gas are 37/40 s and 35/38 s, respectively.

(a) Plot of sensor response as a function of time (b) the resistance transients of the α-MoO3 nanobelts sensor (c) linear relationship of log (S − 1) - log (C) plot to ethanol at 350℃ (d) response to 1000 ppm xylene, ethanol, nitric oxide gas at different temperatures (e) response and recovery times of the α-MoO3 nanobelts sensors to 50 ppm ethanol gas (f) response and recovery times of the α-MoO3 nanobelts sensor to 100 ppm ethanol gas.

Fig. 8(a) shows the sensors undergo 4 successive reversible cycles to 100 ppm ethanol gas, the result reveals that the sensor based on the MoO₃ nanobelts exhibits excellent stability and repeatability on the gas-sensing tests to 100 ppm of ethanol the relative humidity influence on the gas sensor is also evaluated. Fig. 8(b) shows the response of the α-MoO₃ nanobelts sensor to 100 ppm ethanol gas as a function of RH in the range of 25–85%. As can be seen, the responses of the α-MoO₃ nanobelts sensor to variations in RH (25, 45, 65, and 85%) are 7.02, 6.41, 5.76, and 4.78, respectively. This is mainly due to the increase in relative humidity and more water molecules covering the material surface, making O₂ adsorption more difficult. The ionized oxygen will be significantly reduced, which leads to a dramatic decrease in gas-sensitive performance. At higher relative humidity, ionized oxygen species can react directly with water molecules to form hydroxyl (OH⁻) proton (H⁺) layers also provide electrons to the material severely affecting the gas-sensitive performance. To compare the response properties of α-MoO₃ nanobelts with other typical other metal oxide nanomaterials to ethanol gas we present some relevant reported work in Table 1.

(a) The stability of the α-MoO3 sensor to 100 ppm ethanol gas for four cycles (b) Effect of relative humidity on the gas-sensing responses of the α-MoO3 sensor.

Table 1 Lists the sensing performance of other metal oxide nanomaterials for ethanol as a comparison.

Materials	Structure	Response (R_a/R_g)	Test Concentration	Operating Temperature (°C)	Response/Recovery time (s/s)	References
WO₃	nanotubes	16.9	300 ppm	340	1/13	(Song et al., 2015)
CuO	nanoparticles	17.3	100 ppm	320	14/30	(Al-Hadeethi et al., 2018)
ZnO	nanorods	25	100 ppm	320	47/50	(Wang et al., 2012)
In₂O₃	nanospheres	21	100 ppm	275	16/24	(Song et al., 2014)
SnO₂	hollow nanofibers	19	200 ppm	300	12/8	(Cheng et al., 2014)

3.4

3.4 Gas-sensing mechanism

It is generally believed that the gas-sensing mechanism of semiconductor metal oxide sensors stems from the modulation of the depletion layer and the adsorption of reactive oxygen molecules on the surface of the sensing material, which reacts with the test gas to cause a corresponding change in resistance (Bagheri et al., 2014, Gu et al., 2014). The possible mechanism of α-MoO₃ for ethanol differs from that of conventional n-type semiconductors, in which both adsorbed oxygen and lattice oxygen are involved in the catalytic oxidation of ethanol to produce H₂O and CO₂ during the reaction. Lattice oxygen catalyzes the redox gas by partially reducing Mo^6＋ ions to Mo^5＋ ions, resulting in a decrease in resistance (Illyaskutty et al., 2013). Adsorption of oxygen is the release of captured electrons back into the conduction band of MoO₃, which reduces the width of the α-MoO₃ surface depletion layer and thus the resistance of the MoO₃ sensor.

The surface of the sensor is exposed to oxygen (O₂) from the air or the surrounding environment. Lattice defects or surface unsaturated groups capture and ionize oxygen molecules, resulting in the formation of new reactive oxygen species (O₂⁻, O⁻, O²⁻) on the semiconductor surface (Ju et al., 2014). When the working temperature is below 100 °C, oxygen is absorbed mostly as O²⁻; when the temperature is between 100 °C and 300 °C oxygen is absorbed primarily as O⁻; and when the temperature is above 300 °C, it generates O₂⁻ in parallel (Firooz et al., 2010, Bai et al., 2011). The MoO₃ is an n-type semiconductor whose carriers are electrons. Therefore, the concentration of electrons affects the resistance of the sensing material. The reaction progress can be expressed as the followings:

(3)

O_{2} (g a s) \to O_{2} (a d s)

(4)

O_{2} (a d s) + e^{-} {\to O}_{2}^{-} (a d s)

(5)

O_{2}^{-} (a d s) + e^{-} \to 2 O^{-} (a d s)

(6)

O^{-} (a d s) + e^{-} \to O^{2 -} (a d s)

when the sensor based on n-type MoO₃ is exposed to air, oxygen molecules adsorbed on the MoO₃ surface will ionize into oxygen ions (O₂⁻, O⁻, O²⁻) by depriving them of electrons in their conduction bands. This oxygen adsorption process leads to the formation of an electron depletion layer and an increase in resistance as shown in Fig. 9(a) (Ouyang et al., 2012). It may react with the oxygen species adsorbed on the surface at a high exposure temperature of reducing gas (e.g. ethanol), and release the trapped electrons back into the conduction band of MoO₃, resulting in a reduction in the width of the surface depletion layer (Fig. 9(b)), which in turn reduces the resistance of the MoO₃ sensor as illustrated in equation (Kim et al., 2012, Yan et al., 2015, Güntner et al., 2016 ). When the sensor is withdrawn from the reducing gas, oxygen molecules in the air re-adsorb on the MoO₃ surface, restoring the sensor resistance to its original value.

(7)

C_{2} H_{5} O H (g a s) + 6 O^{-} \to 3 H_{2} O + C O_{2} + 6 e^{-}

(8)

C_{2} H_{5} O H + 3 O_{2}^{-} \to 2 C O_{2} + 3 H_{2} O + 3 e^{-}

(9)

C_{2} H_{5} O H + 6 O^{2 -} \to 2 C O_{2} + 3 H_{2} O + 12 e^{-}

The proposed gas sensing mechanism (a) before and (b) after ethanol exposure.

The shear structures formed by removing lattice oxygen at the surface are related to the facile rearrangement of the polyhedra coordination. For (MoO₆) octahedra, the energy of the edge-linked system (such as α-MoO₃) is considerably lower than that of the corner-linked system, making its lattice oxygen removing process highly favourable (Ressler 2002). The ethanol sensing mechanism on MoO₃ may be thought of as a catalytic ethanol vapor oxidation process on the oxide surface. First, the O-H bond of the adsorbed ethanol is dissociated to form ethanol groups and hydrogen (Mai et al., 2007), Subsequently, heterolytic generation of hydrogen atoms react with nearby lattice oxygen anions to form adsorbed hydroxyl groups, while ethanol groups form ionic bonds with unsaturated metal sites (Datta et al., 2017).

(10)

C_{2} H_{5} O H (a d s) \to C_{2} H_{5} O (a d s) + H (a d s)

(11)

C_{2} H_{5} O + H + - O - M O - O - M o \to - O H + C_{2} H_{5} O - M o - O - M o

Subsequently, the intermediate product (C₂H₅O) undergoes a dehydrogenation reaction and forms H₂O, and gives the proton to the cation, which may be due to the reducible oxidizable cation nature of molybdenum (Ou et al., 2011) as shown in equation (12).

(12)

C_{2} H_{5} O - M O - O - M o - O H \to C H_{3} C H O + M o - O - M o + H_{2} O

Adsorbed hydroxyl groups and protons are liberated as water on metals (Mo), leaving surface oxygen vacancies. Acetaldehyde is then oxidized to produce water and carbon dioxide^. A schematic of the ethanol sensing mechanism on the MoO₃ surface is shown in Fig. 10. Since the lattice oxygen (Oi²⁻) and oxygen vacancies (vo²⁺) coexist in defect equilibrium on the faulty MoO₃ crystal surface (Zhang and Park 2018), the total response of sensitive interaction may be expressed by the equation (13).

(13)

C_{2} H_{5} O H + O i^{2 -} \to C H_{3} C H O + H_{2} O + V O^{2 +} + 2 e^{-}

Schematic of ethanol detecting mechanism on the MoO3 surface. On the MoO3 surface layer, catalytic oxidation of ethanol to acetaldehyde results in electron transport to the metal site. Water is formed due to the release of protons and adsorbed OH on Mo desorb. Acetaldehyde can then be oxidized to produce H2O and CO2.

According to the above overall reactions the lattice oxygen concentration decreases and the oxygen vacancy concentration increases. In both reactions the production of free electrons occurs in the reducing gas, resulting in an increase in conductivity or a decrease in resistance of MoO₃ (Yang et al., 2015). Furthermore, since vacancy is the donor and lattice oxygen is the acceptor, an increase in the donor and a decrease in the acceptor lead to an increase in the gas response.

It is worth mentioning that the XPS results show a shift towards lower binding energy peaks due to Mo⁵⁺ ions in the lattice which serve as the favorite absorption site for oxygen species. A large amount of Mo⁵⁺ would favor oxygen adsorption and increase the response (Illyaskutty et al., 2013). Therefore, it can be concluded that ethanol undergoes a reduction reaction through catalytic oxidation of MoO₃ leading to electron transfer to the metal sites, generating a large number of oxygen vacancies and Mo⁵⁺ ions. Therefore, the ethanol sensing mechanism on α-MoO₃ can be considered a catalytic oxidation process of ethanol. So, based on the mechanism of ethanol's catalytic effect on MoO₃, it is assumed that both adsorbed oxygen and lattice oxygen are engaged in the catalytic oxidation of ethanol to form H₂O and CO₂ throughout the process, resulting in a resistance change.

4 Conclusions

In summary, MoO₃ one-dimensional nanomaterials with smooth surface and width of about 200 nm and length of several microns were synthesized by hydrothermal method using permolybdate precursors. The MoO₃ nanobelts and gas-sensitive properties for various gases (xylene, nitrous oxide and ethanol) were investigated. The results indicate that the as-prepared sensor exhibited efficient sensing performance to ethanol gas. The sensor based on α-MoO₃ exhibits high response at 350 °C. The response of MoO₃ to 50, 200, and 600 ppm of ethanol is 5.01, 12.1, and19.2, respectively. Good linearity in the logarithm of the response to ethanol concentration in the concentration range of 50–600 ppm and good stability and repeatability of the sensor in four consecutive reversible cycles of gas sensitivity tests. The response of α-MoO₃ sensor to 100 ppm ethanol gas was investigated as a function of RH, and the gas sensitivity of the sensor decreased with increasing relative humidity. In addition, the unique sensing mechanism of MoO₃ for ethanol is presented in detail. The simultaneous participation of absorbed oxygen and lattice oxygen in the catalytic oxidation of ethanol to create H₂O and CO₂ facilitate molybdenum trioxide respond effectively to ethanol gas in the sensing process.

Acknowledgments

The authors acknowledge the financial supports from the National Natural Science Foundation of China (No. 51964046) and Yunnan Province Ten thousand Youth Program Top Talents (No. YNWR-QNBJ-2019-066).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

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Zhang Y., Park S.-J., . Bimetallic AuPd alloy nanoparticles deposited on MoO3 nanowires for enhanced visible-light driven trichloroethylene degradation. J. Catal.. 2018;361:238-247.
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Zhang J., Song P., Li Z., . Enhanced trimethylamine sensing performance of single-crystal MoO3 nanobelts decorated with Au nanoparticles. J. Alloy. Compd.. 2016;685:1024-1033.
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Zhao N., Fan H., Zhang M., . Simple electrodeposition of MoO3 film on carbon cloth for high-performance aqueous symmetric supercapacitors. Chem. Eng. J.. 2020;390
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Introduction

Experimental methods

Synthesis of α-MoO3 nanobelts
Characterizations
Fabrication of α-MoO3 nanobelts sensors
Measurement of gas sensing performance

Results and discussion

Structural analysis
Morphologies and microstructure of α-MoO3 nanobelts
Gas-response properties
Gas-sensing mechanism

Conclusions

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[1] Al-Hadeethi Y., Umar A., Kumar R., . Fabrication and Characterizations of Ethanol Sensor Based on CuO Nanoparticles. J. Nanosci. Nanotechnol.. 2018;18:2892-2897.
[CrossRef] [Google Scholar]

[2] Bagheri M., Hamedani N.F., Mahjoub A.R., . Highly sensitive and selective ethanol sensor based on Sm2O3-loaded flower-like ZnO nanostructure. Sens. Actuators, B. 2014;191:283-290.
[CrossRef] [Google Scholar]

[3] Bai S., Hu J., Li D., . Quantum-sized ZnO nanoparticles: Synthesis, characterization and sensing properties for NO2. J. Mater. Chem.. 2011;21
[CrossRef] [Google Scholar]

[4] Barsan N., Koziej D., Weimar U., . Metal oxide-based gas sensor research: How to? Sens. Actuators, B. 2007;121:18-35.
[CrossRef] [Google Scholar]

[5] Bie L.-J., Yan X.-N., Yin J., . Nanopillar ZnO gas sensor for hydrogen and ethanol. Sens. Actuators, B. 2007;126:604-608.
[CrossRef] [Google Scholar]

[6] Bouricha B., Souissi R., Bouguila N., . A real-time sharp selectivity with In2S3 gas sensor using a nonlinear dynamic response for VOCs. Measurement. 2021;185
[CrossRef] [Google Scholar]

[7] Chen H.-Y., Su H.-C., Chen C.-H., . Indium-doped molybdenum oxide as a new p-type transparent conductive oxide. J. Mater. Chem.. 2011;21
[CrossRef] [Google Scholar]

[8] Cheng L., Ma S.Y., Wang T.T., . Synthesis and characterization of SnO2 hollow nanofibers by electrospinning for ethanol sensing properties. Mater. Lett.. 2014;131:23-26.
[CrossRef] [Google Scholar]

[9] Datta R.S., Haque F., Mohiuddin M., . Highly active two dimensional α-MoO3−x for the electrocatalytic hydrogen evolution reaction. J. Mater. Chem. A. 2017;5:24223-24231.
[CrossRef] [Google Scholar]

[10] Dewangan K., Sinha N.N., Sharma P.K., . Synthesis and characterization of single-crystalline α-MoO3 nanofibers for enhanced Li-ion intercalation applications. CrystEngComm. 2011;13:927-933.
[CrossRef] [Google Scholar]

[11] Firooz A.A., Hyodo T., Mahjoub A.R., . Synthesis and gas-sensing properties of nano- and meso-porous MoO3-doped SnO2. Sens. Actuators, B. 2010;147:554-560.
[CrossRef] [Google Scholar]

[12] Gao B., Fan H., Zhang X., . Hydrothermal synthesis of single crystal MoO3 nanobelts and their electrochemical properties as cathode electrode materials for rechargeable lithium batteries. J. Phys. Chem. Solids. 2012;73:423-429.
[CrossRef] [Google Scholar]

[13] Gu F., You D., Wang Z., . Improvement of gas-sensing property by defect engineering in microwave-assisted synthesized 3D ZnO nanostructures. Sens. Actuators, B. 2014;204:342-350.
[CrossRef] [Google Scholar]

[14] Güntner A.T., Righettoni M., Pratsinis S.E., . Selective sensing of NH 3 by Si-doped α-MoO 3 for breath analysis. Sens. Actuators, B. 2016;223:266-273.
[CrossRef] [Google Scholar]

[15] Huang L., Xu H., Zhang R., . Synthesis and characterization of g-C3N4/MoO3 photocatalyst with improved visible-light photoactivity. Appl. Surf. Sci.. 2013;283:25-32.
[CrossRef] [Google Scholar]

[16] Illyaskutty N., Kohler H., Trautmann T., . Enhanced ethanol sensing response from nanostructured MoO3:ZnO thin films and their mechanism of sensing. J. Mater. Chem. C. 2013;1
[CrossRef] [Google Scholar]

[17] Ju D., Xu H., Qiu Z., . Highly sensitive and selective triethylamine-sensing properties of nanosheets directly grown on ceramic tube by forming NiO/ZnO PN heterojunction. Sens. Actuators, B. 2014;200:288-296.
[CrossRef] [Google Scholar]

[18] Kim K.-M., Choi K.-I., Jeong H.-M., . Highly sensitive and selective trimethylamine sensors using Ru-doped SnO2 hollow spheres. Sens. Actuators, B. 2012;166–167:733-738.
[CrossRef] [Google Scholar]

[19] Kumar S., Singh A., Singh R., . Facile h-MoO3 synthesis for NH3 gas sensing application at moderate operating temperature. Sens. Actuators, B. 2020;325
[CrossRef] [Google Scholar]

[20] Liu Y., Yang S., Lu Y., . Hydrothermal synthesis of h-MoO3 microrods and their gas sensing properties to ethanol. Appl. Surf. Sci.. 2015;359:114-119.
[CrossRef] [Google Scholar]

[21] Mai L.Q., Hu B., Chen W., . Lithiated MoO3 Nanobelts with Greatly Improved Performance for Lithium Batteries. Adv. Mater.. 2007;19:3712-3716.
[CrossRef] [Google Scholar]

[22] Martins R.L., Baldanza M.A.S., Souza M.M.V.M., . The effect of support on methane activation over Pt catalysts in the presence of MoO3. Appl. Catal. A. 2007;318:207-212.
[CrossRef] [Google Scholar]

[23] Ou J.Z., Campbell J.L., Yao D., . In Situ Raman Spectroscopy of H2 Gas Interaction with Layered MoO3. The Journal of Physical Chemistry C.. 2011;115:10757-10763.
[CrossRef] [Google Scholar]

[24] Ouyang Q.-Y., Li L., Wang Q.-S., . Facile synthesis and enhanced H2S sensing performances of Fe-doped α-MoO3 micro-structures. Sens. Actuators, B. 2012;169:17-25.
[CrossRef] [Google Scholar]

[25] Ressler T., . Bulk Structural Investigation of the Reduction of MoO3 with Propene and the Oxidation of MoO2 with Oxygen. J. Catal.. 2002;210:67-83.
[CrossRef] [Google Scholar]

[26] Siciliano T., Tepore A., Filippo E., . Characteristics of molybdenum trioxide nanobelts prepared by thermal evaporation technique. Mater. Chem. Phys.. 2009;114:687-691.
[CrossRef] [Google Scholar]

[27] Song P., Han D., Zhang H., . Hydrothermal synthesis of porous In2O3 nanospheres with superior ethanol sensing properties. Sens. Actuators, B. 2014;196:434-439.
[CrossRef] [Google Scholar]

[28] Song C., Li C., Yin Y., . Preparation and gas sensing properties of partially broken WO3 nanotubes. Vacuum. 2015;114:13-16.
[CrossRef] [Google Scholar]

[29] Sui L.-L., Xu Y.-M., Zhang X.-F., . Construction of three-dimensional flower-like α-MoO3 with hierarchical structure for highly selective triethylamine sensor. Sens. Actuators, B. 2015;208:406-414.
[CrossRef] [Google Scholar]

[30] Sunu S.S., Prabhu E., Jayaraman V., . Electrical conductivity and gas sensing properties of MoO3. Sens. Actuators, B. 2004;101:161-174.
[CrossRef] [Google Scholar]

[31] Troitskaia I.B., Gavrilova T.A., Gromilov S.A., . Growth and structural properties of α-MoO3 (010) microplates with atomically flat surface. Mater. Sci. Eng., B. 2010;174:159-163.
[CrossRef] [Google Scholar]

[32] Wang L., Kang Y., Liu X., . ZnO nanorod gas sensor for ethanol detection. Sens. Actuators, B. 2012;162:237-243.
[CrossRef] [Google Scholar]

[33] Wang S., Xie J., Hu J., . Fe-doped α-MoO3 nanoarrays: Facile solid-state synthesis and excellent xylene-sensing performance. Appl. Surf. Sci.. 2020;512
[CrossRef] [Google Scholar]

[34] Wang X., Zhang Y.P., Chen Z.Q., . Effect of MoO3constituents on the growth of MoS2nanosheets by chemical vapor deposition. Mater. Res. Express. 2016;3
[CrossRef] [Google Scholar]

[35] Wu D., Shen R., Yang R., . Mixed Molybdenum Oxides with Superior Performances as an Advanced Anode Material for Lithium-Ion Batteries. Sci. Rep.. 2017;7:44697.
[CrossRef] [Google Scholar]

[36] Xu Q., Ju D., Zhang Z., . Near room-temperature triethylamine sensor constructed with CuO/ZnO P-N heterostructural nanorods directly on flat electrode. Sens. Actuators, B. 2016;225:16-23.
[CrossRef] [Google Scholar]

[37] Yan H., Song P., Zhang S., . Facile fabrication and enhanced gas sensing properties of hierarchical MoO3 nanostructures. RSC Adv.. 2015;5:72728-72735.
[CrossRef] [Google Scholar]

[38] Yang S., Wang Z., Hu Y., . Highly Responsive Room-Temperature Hydrogen Sensing of alpha-MoO(3) Nanoribbon Membranes. ACS Appl. Mater. Interfaces. 2015;7:9247-9253.
[CrossRef] [Google Scholar]

[39] Yang S., Liu Y., Chen T., . Zn doped MoO3 nanobelts and the enhanced gas sensing properties to ethanol. Appl. Surf. Sci.. 2017;393:377-384.
[CrossRef] [Google Scholar]

[40] Yeh T.-H., Lee C.-C., Shih C.-J., . Vacuum-deposited MoO3/Ag/WO3 multilayered electrode for highly efficient transparent and inverted organic light-emitting diodes. Org. Electron.. 2018;59:266-271.
[CrossRef] [Google Scholar]

[41] Yin T., Meng X., Wang S., . Study on the adsorption of low-concentration VOCs on zeolite composites based on chemisorption of metal-oxides under dry and wet conditions. Sep. Purif. Technol.. 2022;280
[CrossRef] [Google Scholar]

[42] Zhang Y., Park S.-J., . Bimetallic AuPd alloy nanoparticles deposited on MoO3 nanowires for enhanced visible-light driven trichloroethylene degradation. J. Catal.. 2018;361:238-247.
[CrossRef] [Google Scholar]

[43] Zhang J., Song P., Li Z., . Enhanced trimethylamine sensing performance of single-crystal MoO3 nanobelts decorated with Au nanoparticles. J. Alloy. Compd.. 2016;685:1024-1033.
[CrossRef] [Google Scholar]

[44] Zhao N., Fan H., Zhang M., . Simple electrodeposition of MoO3 film on carbon cloth for high-performance aqueous symmetric supercapacitors. Chem. Eng. J.. 2020;390
[CrossRef] [Google Scholar]

[45] Zhou X., Liu J., Wang C., . Highly sensitive acetone gas sensor based on porous ZnFe2O4 nanospheres. Sens. Actuators, B. 2015;206:577-583.
[CrossRef] [Google Scholar]

[46] Zhou L., Mi Q., Jin Y., . Construction of MoO3/MoSe2 nanocomposite-based gas sensor for low detection limit trimethylamine sensing at room temperature. J. Mater. Sci.: Mater. Electron.. 2021;32:17301-17310.
[CrossRef] [Google Scholar]