Enhanced Gas Sensitivity and Selectivity on Aperture-Controllable 3D

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Enhanced Gas Sensitivity and Selectivity on Aperture-Controllable 3D Interconnected Macro−Mesoporous ZnO Nanostructures Jing Liu,† Huawen Huang,† Heng Zhao,† Xiaoting Yan,† Sijia Wu,† Yu Li,*,† Min Wu,† Lihua Chen,† Xiaoyu Yang,† and Bao-Lian Su*,†,‡,§ †

Laboratory of Living Materials at the State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, 122 Luoshi Road, 430070 Wuhan, Hubei, China ‡ Laboratory of Inorganic Materials Chemistry (CMI), University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium § Department of Chemistry and Clare Hall, University of Cambridge, Cambridge CB2 1EW, U.K. S Supporting Information *

ABSTRACT: Three-dimensional (3D) macro−mesoporous structures demonstrate effective performance for gas sensing. In this work, we have designed and successfully prepared aperture-controllable three-dimensional interconnected macro− mesoporous ZnO (3D-IMM-ZnO) nanostructures by template-based layer-by-layer filtration deposition. XRD, SEM, and TEM have been used to characterize the obtained hexagonal wurzite 3D-IMM-ZnO nanostructures. Owing to its special 3D interconnected hierarchically porous structure, the 3D-IMM-ZnO nanostructures exhibit excellent gas sensing performances toward acetone and methanol. The 3D-IMM-ZnO nanostructure with the largest macropore demonstrates the best gas sensitivity owing to its largest cavity providing enough space for gas diffusion. On the basis of the results and analyses, we propose that the synergistic effect of electron liberation and electron density of acetone and the special structure make the 3D-IMM-ZnO nanostructures demonstrate better gas sensing properties than many other porous ZnO nanostructures and preferred selectivity to acetone. KEYWORDS: 3D interconnected macro−mesoporous ZnO, gas sensor, acetone, methanol, selectivity

1. INTRODUCTION

binding energy (60 meV), high mobility of conduction electrons (200 cm2/(V s)),3 and good chemical and thermal stability4 has attracted more and more attention for gas sensors.5 Generally, the morphologies, crystallization, grain size, and defects have great influences on gas sensing performances

With the development of industry, the discharge of dust, fetor, or other toxic gases such as acetone, methanol, ammonia, and toluene has become a serious environmental problem owing to their harmfulness to human health and safety.1,2 Therefore, developing gas sensors for rapid, high sensitivity, and selective detection has attracted numerous interests in recent years. Among the various gas sensing materials, ZnO as an n-type semiconductor with wide band gap (Eg ≈ 3.2 eV), large exciton © XXXX American Chemical Society

Received: December 17, 2015 Accepted: March 21, 2016

A

DOI: 10.1021/acsami.5b12315 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

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ACS Applied Materials & Interfaces

Figure 1. Schematic illustration of 3D-IMM-ZnO nanostructures synthesis.

necessary to fabricate 3D interconnected macro−mesoporous ZnO nanostructures via a simple, effective, and high yield approach and to discuss the reaction mechanism of gas sensing for such special structures. Herein, we report a simple and effective layer-by-layer filtration deposition method to synthesize aperture-controllable three-dimensional interconnected macro−mesoporous ZnO (3D-IMM-ZnO) nanostructures and explore the effect of macroporous structure on gas sensitivity. Our results show that the synthesized 3D-IMM-ZnO nanostructures have high specific surface area and interconnected channels between macropores and mesopores. The 3D-IMM-ZnO nanostructure prepared from a 600 nm colloidal template not only shows the highest gas sensitivity to acetone and methanol compared to other 3D-IMM-ZnO nanostructures due to the largest macropores providing enough space and facilitating gas diffusion but also demonstrates better gas sensing performance than our previous partial interconnected porous ZnO structure 34 and many other porous ZnO nanostructures.2,3,12,19,21,29,35−37 In addition, our 3D-IMM-ZnO nanostructures exhibit obvious selectivity to acetone. At last, the gas mechanism and the gas selectivity on 3D-IMM-ZnO to acetone and methanol are discussed.

of gas sensors. There are many studies such as controlling the grain size, modification, design, and synthesis of the different morphologies of ZnO to study their effects on gas sensing.6−13 Recent studies have demonstrated that ZnO with different morphologies (such as nanowires, nanorods, nanobelts, hollow architectures, flower-like structures, mesoporous microsphere, hierarchically porous structures) has great influence on gas sensitivity.14−20 In particular, ZnO with three-dimensional (3D) macro−mesoporous structures is considered to be a promising candidate with exciting potential for effective gas sensing application.21 The 3D macro−mesoporous morphology possesses high porosity and large surface-to-volume ratio with a less agglomerated configuration, which can shorten gas diffusion length, facilitate gas permeation and mass transport in sensor materials, and thus improve sensor performance.21−24 On the other hand, the mesoporous structures have also been reported to show high gas response and rapid gas responding kinetics due to their high surface area and well-defined porous architecture.25−29 For example, Wang et al. synthesized nestlike 3D ZnO porous structures with worm-like mesopores (3−40 nm), exhibiting high sensitivity, fast response, and recovery speed.20 Li et al. prepared multilayered ZnO nanosheets with 3D porous architectures, demonstrating highly gas sensing performances because of the significantly enhanced gas diffusion and mass transportation.29 However, the marcoporous size of these 3D ZnO architectures via annealing the zinc hydroxide carbonates is not aperture controllable. In fact, the effect of macropore size on gas sensing is seldom focused and noticed. Therefore, fabrication of aperture-controllable threedimensional macro−mesoporous ZnO and exploring its effect on sensor response are desirable for gas sensor application. In this regard, the 3D ordered macroporous (3DOM) architectures through the polymer colloid template could be suitable for high performance gas sensing.30−33 Further, the nanoparticles can construct worm-like mesoporosity in such special structure via increasing the surface area to improve their gas sensing performances. However, this method is not suitable for 3DOM ZnO powders due to the low infiltration ratio of zinc precursor leading to structure collapse. To address the problem for 3D macro−mesoporous ZnO architecture, we have developed an intriguing colloid template method.34 The obtained macro−mesoporous ZnO nanostructures showed good sensitivity to ethanol and acetone. However, its macro−mesoporous structure is only partially interconnected, which is not beneficial for gas diffusion and transport in 3D directions; i.e., the advantages of the interconnected hierarchically porous structure cannot be fully demonstrated. Further, the gas sensing mechanism for such hierarchically porous ZnO nanostructures is not discussed. Therefore, it is

2. EXPERIMENTAL MATERIALS AND METHODS 2.1. Materials. Styrene, methyl methacrylate (MMA), 3sulfopropyl methacrylate potassium (SPMAP), ammonium persulfate ((NH4)2S2O8), ammonium bicarbonate (NH4HCO3), Zn(Ac)2·2H2O, ethylene glycol (EG), ethanol, and acetone were purchased from Aldrich. 2.2. Colloidal Spheres Preparation. The monodispersed colloids of poly(styrene-methyl methacrylate-3-sulfopropyl methacrylate potassium) (P(St-MMA-SPMAP)) beads are synthesized by a soap-free emulsion polymerization according to our previous work:33,38 22.5 mL of styrene, 1.25 mL of MMA, and 110 mL of water are heated to 70 °C under a N2 atmosphere. Then a solution formed by 0.4 g of (NH4)2S2O8, 0.8 g of NH4HCO3, and (0.06−0.3 g) of SPMAP dissolved in 10 mL of water are added to initiate the reaction, and the reaction is stopped after 8 h by cooling down the container. The colloids with diameters of 320, 400, 520, and 600 nm have been obtained. Finally, the as-prepared colloid is diluted in water to obtain the P(St-MMA-SPMAP) suspension with a solid content of 0.25 wt % for further use. 2.3. ZnO Nanoparticle Precursor Preparation. The ZnO precursor with the size of ∼30 nm is synthesized via a hydrothermal method.10,38 The specific experimental steps are as follows: 0.05 mol Zn(Ac)2·2H2O is dissolved in 80 mL ethylene glycol (EG) by magnetic stirring at room temperature to form a mixed solution. Then, the resultant solution is transferred into a stainless-steel autoclave with volume of 100 mL, sealed, and heated in 160 °C for 1 h. Finally, the white product is centrifuged and washed with deionized water and B


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Figure 2. (a-c) SEM, (d, e) TEM, and (f) HRTEM images of 3D-IMM-ZnO-600. ethanol 3 times, respectively. The prepared ZnO precursor is dispersed in water to obtain ZnO nanoparticle suspension (0.01g/mL), which is further used for 3D-IMM-ZnO nanostructures preparation. 2.4. 3D-IMM-ZnO Nanostructure Preparation. A layer-by-layer separate filtration deposition strategy is conducted in this experiment as shown in Figure 1. The well-dispersed polymeric colloids in diameters at 320, 400, 520, and 600 nm are used as templates to create 3D-IMM-ZnO powders (Figure S1). The used ZnO nanoparticles in size ∼30 nm are shown in Figures S2a and S2b. First, a layer of polymer colloids is assembled to form face center cubic-like (fcc-like) structure via infiltration. Then the ZnO nanoparticle precursor is filled into the interspaces of fcc-like structure. This process is repeated for several cycles, and the obtained ZnO/P(St-MMA-SPMAP) composites are dried in a 40 °C oven. Finally, the resulting ZnO/P(St-MMASPMAP) composite is calcined in air at 300 °C with a ramp rate of 1 °C/min for 2 h and subsequently raised to 450 °C for 8 h. The samples are then designated as 3D-IMM-ZnO-320, 3D-IMM-ZnO400, 3D-IMM-ZnO-520, and 3D-IMM-ZnO-600, respectively. To show the advantage of 3D-IMM-ZnO nanostructures on gas sensitivity, the ZnO precursor suspensions are filtrated without the polymer colloids and calcined as the same procedure as 3D-IMMZnO. The obtained product is designated as ZnO-NP. 2.5. Characterizations. X-ray diffraction patterns (XRD) are obtained with a Bruker D8 Advanced diffractometer using Cu Kα radiation (λ = 1.54056 Å). Field emission scanning electron microscopy (FESEM) is performed on a Hitachi S-4800 electron microscope. Transmission electron microscopy (TEM) and highresolution transmission electron microscopy (HRTEM) are performed on a JEOL JEM-2100F microscope with an acceleration voltage of 200 kV. The specific surface area and pore-size distribution of the samples is analyzed by Micromeritics Tristar II 3020 nitrogen adsorption− desorption apparatus according to the Brunauer−Emmett−Teller (BET) and Bareet−Joyner−Halenda (BJH) method from the N2 adsorption isotherms. The gas sensor properties and response are tested on the WS-60A gas sensing test system (Weisheng Electronics Co. Ltd., P.R. China). The structures, fabrication, and testing principle of gas sensors based on 3D-IMM-ZnO are similar to previous reports.29 The gas response S is defined as the ratio of Ra/Rg, where Ra and Rg are the electrical resistance of sensor in air and testing gas environment, respectively.

Figure 2a and 2b presents the SEM images of 3D-IMM-ZnO600, showing the macroporous structure. Figure S3 displays the SEM images of other samples. These SEM observations indicate that aperture-controllable 3D-IMM-ZnO nanostructures are successfully synthesized via template-based layer-bylayer filtration deposition. The only difference for these hierarchically porous structures is the macropore size, indicating the controllable macropores. The SEM images also show that the macro−mesoporous architectures are tightly constructed by small nanoparticles (Figure 2b), suggesting a large number of worm-like mesopores within the structure. Figure 2c demonstrates a cross-sectional SEM image of 3DIMM-ZnO-600, showing the macro−mesopores uniformly distributed in 3D-IMM-ZnO nanostructures. The low-magnification TEM images (Figure 2d and 2e) confirm the 3D interconnected macro−mesoporous architecture and the worm-like mesoporous structure tightly assembled by ∼30 nm ZnO nanoparticles. Figure 2f displays the HRTEM image of one nanoparticle. The lattice fringe spacing is measured to be 2.61 Å, corresponding to the (002) crystal plane of hexagonal wurtzite ZnO (JCPDS No. 079-2205), in agreement with the XRD results (Figure S4). The crystalline grain sizes of 3D-IMM-ZnO and ZnO-NP are ∼30 nm according to the Debye−Scherrer equation (Table 1), very consistent with TEM results. To obtain further information about the hierarchically porous 3D-IMM-ZnO structures, N2 absorption−desorption analysis is performed. Figure 3a presents the nitrogen adsorption−desorption isotherms, and Figure 3b illustrates Table 1. Structure Parameters of the 3D-IMM-ZnO Nanostructures and ZnO-NP

3. RESULTS AND DISCUSSION After calcined at 450 °C for 8 h, the 3D-IMM-ZnO nanostructures with different macroporous sizes are obtained. C

samples

crystallites size (nm)

pore diameter (nm)

BET surface area (m2 g−1)

3D-IMM-ZnO-320 3D-IMM-ZnO-400 3D-IMM-ZnO-520 3D-IMM-ZnO-600 ZnO-NP

∼30 ∼33 ∼32 ∼30 ∼30

24 22 25 25 45

19.1 20.1 18.9 21.1 8.1


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Figure 3. (a) Nitrogen adsorption/desorption isotherms and (b) pore size distribution of 3D-IMM-ZnO nanostructures and ZnO-NP.

Figure 4. Gas sensing properties of 3D-IMM-ZnO and ZnO-NP. Gas sensitivities to (a) 100 ppm acetone and to (b) 100 ppm methanol at different temperatures.

Figure 5. Gas response curves to (a) acetone at 260 °C and (b) methanol at 240 °C at different gas concentrations.

NP. The gas sensor properties of 3D-IMM-ZnO nanostructures and ZnO-NP are then measured at the WS-60A gas sensing test system for acetone and methanol at different temperatures with a concentration of 100 ppm to get the optimum working temperature, respectively. Figure 4a shows the response sensitivity of the 3D-IMM-ZnO nanostructures to acetone at different temperatures. All the 3D-IMM-ZnO nanostructures exhibit the maximum response to acetone at 260 °C. This indicates that the optimal working temperature is 260 °C. Generally, at this particular temperature, sufficient adsorbed oxygen ionic species (O2−, O−, or O2−) on the surface can effectively react with acetone molecules, resulting in the highest response.40−42 Below this temperature, the adsorbed oxygen species at the surface are inactive enough to react with acetone. Above this temperature, the adsorbed amount of oxygen species at the surface is reduced due to their fast desorption, leading to decreased gas response.12

the distribution of pore size of 3D-IMM-ZnO and ZnO-NP. The BET surface areas of 3D-IMM-ZnO structures with different macroporous sizes are ∼20 m2 g−1, which are much higher than that of ZnO-NP (8.1 m2 g−1) (Table 1). The higher surface areas indicate enhanced gas sensitivity for 3D-IMMZnO structures compared to ZnO-NP. Table 1 also lists the pore size distribution for all the samples. It is interesting to note that ZnO-NP has larger mesopore size than those of 3D-IMMZnO nanostructures. This indicates that the nanoparticles in ZnO-NP are loosely aggregated (Figure S2c and S2d), which is not good for gas sensing due to the increased contacting resistance. It has been reported that the morphology of nanostructures dramatically influences the photocatalytic, optical, and physical properties of ZnO.39 It is expected that our 3D-IMM-ZnO nanostructures with interconnected hierarchical macro−mesopores bring much more effective gas sensing compared to ZnOD


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Figure 6. (a) Response ratio of Sa/Sm for 3D-IMM-ZnO and (b) gas sensitivity comparison of the previous reported porous ZnO nanomaterials with 3D-IMM-ZnO-600 for 100 ppm acetone and methanol at the optimal working temperatures.

Figure 7. Response and recovery time curves of 3D-IMM-ZnO nanostructures and ZnO-NP to (a) 100 ppm acetone at 260 °C and (b) 100 ppm methanol at 240 °C.

which is much better than other 3D-IMM-ZnO nanostructures and ZnO-NP (Table S1). For instance, the response value of 3D-IMM-ZnO-600 to 10 ppm acetone is ∼41.1, which is ∼3 times higher than that of ZnO-NP (13.5). The response value of 3D-IMM-ZnO-600 to 500 ppm acetone (S = 385.9) is almost 4 times that of ZnO-NP (S = 94.8). A result is observed similar to methanol that the response value of 3D-IMM-ZnO600 is 20.6 to 10 ppm methanol, while the response value of ZnO-NP is only 8.5. From these data, we can see that 3DIMM-ZnO nanostructures are suitable for acetone and methanol detection in a quite broad range of gas concentrations. From Figure 4 and Figure 5, one can see that the 3D-IMMZnO nanostructures and ZnO-NP display obvious different sensitivity to acetone and methanol, indicating that the 3DIMM-ZnO nanostructures may have selectivity for these two gases. Therefore, we compare the response ratio of Sa (for acetone) to Sm (for methanol) at the optimal working temperature to 100 ppm acetone and methanol, respectively (Figure 6a). The results show that all the sensors exhibit obvious selectivity to acetone compared to methanol. In particular, the 3D-IMM-ZnO-600 sensor demonstrates the best selectivity to acetone among 3D-IMM-ZnO nanostructures at the optimal working temperatures. We also compare our gas sensitivities with our previous partial interconnected macro− mesoporous ZnO structure34 and other reported porous ZnO nanostructures.2,3,12,19,21,35−37 The results show that the performance of our 3D-IMM-ZnO-600 nanostructure is superior to them (Figure 6b), verifying such special structure is beneficial for gas sensing. For the gas sensor, the response and recovery speeds are also key factors. In general, the response time is defined as the time taken by a sensor to achieve 90% of the total signal, and the

Figure 4a also illustrates that the gas sensitivities of 3D-IMMZnO nanostructures to acetone are much higher than those of ZnO-NP at each temperature. The gas sensitivity of 3D-IMMZnO-600 exhibits the highest gas sensitivity to ∼137, which is 4 times to that of ZnO-NP (∼33). These results indicate that the 3D-IMM-ZnO nanostructures are promising candidates for gas sensing. The macropores of 3D-IMM-ZnO provide excellent channels and surface accessibility for acetone molecules diffusing into the inner part of mesopores, ensuring acetone fully contacting with ZnO nanoparticles, whereas the absence of macropores in ZnO-NP significantly slows down the gas response owing to a long and tortuous pathway for acetone permeation and diffusion.27 The loosely agglomerated nanoparticles in ZnO-NP can also lead to resistance increase. Further, the largest macropore in 3D-IMM-ZnO-600 offers the largest cavity and much more space for acetone diffusion, leading to the fastest speed to reach the conducting state for the redox reaction between oxygen species and acetone compared to other 3D-IMM-ZnO nanostructures.27 This makes 3DIMM-ZnO-600 exhibit the highest gas sensitivity to acetone. Figure 4b displays the gas sensitivities of all the samples to methanol. The maximum sensitivity values locate at 240 °C. Still, the 3D-IMM-ZnO nanostructures demonstrate higher sensitivity than ZnO-NP, and 3D-IMM-ZnO-600 has the best sensitivity. From the above results and analysis, it can be seen that the 3D hierarchically interconnected macro−mesoporous structure plays a major impact factor on gas sensing properties. In particular, the larger the macropore is, the better the gas sensitivity is. Figure 5a and 5b presents the gas responses of 3D-IMMZnO and ZnO-NP to acetone and methanol from 10 to 500 ppm at the optimal working temperature of 260 and 240 °C, respectively. Still, 3D-IMM-ZnO-600 has the best performance, E


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nanostructures demonstrate an obvious difference in response time, and the recovery time to acetone is faster than to methanol. Further, the recovery time is more closely related to the target gas. Therefore, we attempt to explain our findings here and propose the synergistic effect of electron-liberate and electron density of target gas according to the typical reactions below

recovery time is defined as the time required from the absorption−desorption equilibrium of oxygen species in the sensor surface to the baseline conductance.5,43 Figure 7a presents the response times for 100 ppm acetone at 260 °C for 3D-IMM-ZnO and ZnO-NP. All the 3D-IMM-ZnO nanostructures demonstrate very close response time of ∼13 s, which is ∼9 s faster than that of ZnO-NP owing to the higher surface area with more reactive sites for electrons, oxygen, and target gas. In addition, such hierarchical porous structure and linked channels are also conducive to acetone diffusion and facilitate acetone molecule transfer. As a result, the 3D-IMM-ZnO nanostructures show faster response speeds than ZnO-NP. It is noted that their recovery times are very close to ∼50 s. The response and recovery times of 3D-IMM-ZnO nanostructures and ZnO-NP to 100 ppm methanol at 240 °C are also employed (Figure 7b), displaying the response and recovery time of ∼50 s and ∼85 s, respectively. From Figure 7, we can see that both the response time and recovery times to acetone are faster than those to methanol. This means that ZnO can react easier with acetone than with methanol, verifying the preferred selectivity to acetone for 3D-IMM-ZnO. It is also worth noting that the recovery times of 3D-IMM-ZnO nanostructures and ZnO-NP to acetone have no difference. This situation is similar to methanol. This means that the recovery time is not closely related to the morphology, but the target gases, i.e., the reaction mechanism between ZnO and the target gas, determines the recovery time. Generally, upon heating to working temperature, the O2 molecules adsorbed on the surface of ZnO nanomaterials will extract electrons from the conduction band EC and trap the electrons at the surface to form charged species (O2−, O−, or O2−), which cause an upward band bending at the surface and thus a reduced conductivity compared to the flat band as illustrated in Figure 8, where EV is the valence band; EF is the

CH3COCH3 + 8O−(ads) → 3CO2 + 3H 2O + 8e−

(1)

CH3OH + 3O−(ads) → CO2 + 2H 2O + 3e−

(2)

Upon exposure to acetone, acetone molecules adsorb and donate the electrons to EC edge of ZnO when the oxygen ionic species are consumed through chemical reaction with acetone, leading to the reduction of Λair thickness and the bent degree of EC edge. This can decrease the sensor resistance (Figure 8). At the same concentration of acetone and methanol, acetone releases 5 more electrons into EC than methanol (Formulas 1 and 2), indicating that the O− as an acceptor can easily attract and react with acetone. Moreover, acetone has a larger dipole moment and higher electron density than methanol owing to the existence of CO (p-π conjugated effect). Therefore, acetone can donate more electrons to the EC edge of ZnO compared to methanol, which further reduces the resistance. Finally, acetone can quickly reach equilibrium at high conducting state compared to methanol, resulting in the high gas response values. Thus, the response and recovery speeds of 3D-IMM-ZnO nanostructures to acetone are much faster than those of methanol. From the above results and analysis, we can reasonably conclude that the synergistic effect of electron liberation and electron density of acetone in 3D-IMM-ZnO nanostructures leads to better gas sensing properties to acetone and methanol and preferable selectivity to acetone.

4. CONCLUSION Aperture-controllable 3D-IMM-ZnO with hierarchically macro−mesoporous structures, high surface area, and 3Dinterconnected channels are successfully prepared. Compared with ZnO-NP without such structure, 3D-IMM-ZnO nanostructures show excellent sensing performances for acetone and methanol. The enhanced sensing performances are attributed to large contacting surface area with more reactive sites for electrons, oxygen ions, and target gas and 3D interconnected channels for gas diffusion and transport. In addition, 3D-IMMZnO nanostructures demonstrate remarkable selective detection to acetone owing to the synergistic effect of electronliberation and electron density of acetone. Further, our results indicate that larger macroporous structure can bring better gas sensitivity. Our work here shows that the presence of interconnected macro−mesoporous structure plays an important role for gas sensing properties enhancement, and we also proposed a possible gas sensing mechanism for such porous ZnO nanostructures.

Figure 8. Schematic diagram of band bending after chemisorptions of charged species (O2−, O−, or O2−), where e− and + represent the conducting electrons and the donor sites, respectively.

Fermi level; Λair represents the thickness of the space-charge layer; and eVsurface denotes the potential barrier.42 Previous reports have demonstrated that the rate of adsorption− desorption of oxygen and reducing gases, or oxidation products, and the rate of surface decomposition of reducing gas can largely affect the sensor response.41 The adsorbed oxygen mainly depends on the type of materials, particularly their chemical reaction with the gas; i.e., the chemical reaction between the gas and sensing layer has an effect on sensor response.43 As discussed above, our 3D-IMM-ZnO nanostructures have preferable selectivity to acetone. The 3D-IMM-ZnO

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.5b12315. Figures S1−S4 and Table S1 (PDF) F


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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work is realized in the frame of a program for Changjiang Scholars Innovative Research Team (IRT_15R52) of Chinese Ministry of Education. B. L. Su acknowledges the Chinese Central Government for an “Expert of the State” position in the Program of the “Thousand Talents”. Y. Li acknowledges Hubei Provincial Department of Education for the “Chutian Scholar” program. This work is also financially supported by Hubei Provincial Natural Science Foundation (2014CFB160 and 2015CFB516), the Fundamental Research Funds for the Central Universities (2013-YB-024), the National Science Foundation for Young Scholars of China (No. 51502225), International Science & Technology Cooperation Program of China (2015DFE52870), and Self-determined and Innovative Research Funds of the SKLWUT (2015-ZD-7). We also thank J. L. Xie, X. Q. Liu, and T. T. Luo for TEM analysis from Research and Test Center of Materials at Wuhan University of Technology.

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