Content of heavy metals in soil statistics (unit: mg/kg).
Due to the development of industries in recent decades, the demand for mineral resources is also growing. However, the mining and post-processing of mineral resources will increase the heavy metals that permeate and accumulate in the soil. These heavy metals are in abundance, in terms of persistence and toxicity, which can inhibit soil functions and increase the soil pollution [1, 2, 3]. And when accumulated to harmful levels, the heavy metals in soil may pose an environmental risk and threaten human health through contaminating the food chain, water and air [4, 5, 6]. Thus, the pollution of soil by heavy metals is considered as one of the major environmental problems, and the monitoring of heavy metal content is very important for environment management in mining areas.
\nThe traditional method for estimating the heavy metal contents involves the field data measure and laboratory analysis of soil samples. Although direct sampling can provide an accurate measurement of both the intensity and diversity of soil contaminants at specific sites, however, these procedures are often time-consuming and costly, and it can only analyze the point samples [7, 8, 9]. So rapid, periodic monitoring of heavy metals in the areas vulnerable to pollution is important. The development of remote-sensing technology, especially hyperspectral, provides a possibility for the rapid and large-scale distribution of heavy metals, which can acquire the continuous spectrum of the target. Many studies have shown that the spectral curves of heavy metal-contaminated soil and the spectral curves of uncontaminated soil have a difference [10, 11]. Although soil heavy metals are spectrally inactive, their relationships with spectrally active soil properties, such as clay and Fe oxides, may allow for their visible and near-infrared diffuse reflectance spectroscopy (VNIRS) prediction [12]. Thus, the relationship between heavy metal content and soil spectrum was used to establish heavy metal retrieval model to predict and map the heavy metal content in the relevant areas [11, 13, 14, 15].
\nIn this chapter, the spectral sampling of soil samples was obtained by ASD Fieldspec III spectroradiometer and the contents of Mn and Co were measured by chemical analysis. Then, the feature spectra can be obtained from the results of spectroscopic analysis to establish the heavy metals’ retrieval models. Then, the parameters of these models can used to explore the feasibility of using hyperspectral data to retrieve soil heavy metals for soil remediation and ecological restoration.
\nMiyi Country (26°42ˊ–27°10ˊ N, 101°44ˊ–102°15ˊ E) is located in the north of Panzhihua City, the southwest of Sichuan Province. The terrain is high in the south and low in the north. In this chapter, the Bai-ma, De-sheng and Yuan Bao-shan mining areas in Miyi County are selected as the study areas, as shown in Figure 1. The study area is located in the east of the Yalong River and northwest of the Anning River. The environment in the mining area and surroundings have been contaminated by heavy metals which can enter into the soil through discharge and infiltration and which exist in the waste residue and waste liquor generated in the mining process, especially Mn and Co.
\nThe study area locations of sampling points.
The spectra of 55 soil samples were collected in June 2015; the locations of the sample points are shown in Figure 1. Meanwhile, 32 out of the 55 soil samples had been chemically analyzed for the Mn and Co contents by conventional digestion methods using inductively coupled plasma mass spectrometer (ICP-MS). The ICP-MS is the most popular ion source in analytical chemistry for elemental mass spectrometry. In ICP-MS, a mass spectrometer is coupled to an ICP torch by an interface including sampler and skimmer cones so that representative samples of the plasma can be transmitted through its orifices to the mass analyzer [16].
\nThe soil sample’s spectrum was obtained from a high spectral resolution ASD Fieldspec III spectroradiometer, which covers the visible and near-infrared (350–2500 nm) region and offers a spectral resolution between 3 and 10 nm, interpolated to 1 nm. Illumination was provided by an ASD high-reflectance probe when collecting soil spectra in the field, while a halogen bulb was used as the light source while collecting water spectra in the laboratory. Each sample was measured three times and the average value was calculated afterwards [11].
\nThe soil spectra may contain noise or error that was introduced by operating in situ measurement instruments improperly or using an in situ measurement instrument that is not calibrated properly [17]. And, heavy metals are spectrally featureless in the visible and near-infrared parts of the electromagnetic spectrum. Thus, a serious predicament is observed while dealing with the analysis of overlapping bands of the analytes and interferences which make the extraction of qualitative and quantitative data difficult [18]. Pre-processing of the spectrum is often required to reduce the effect of noise and enhance the spectral signature. Meanwhile, Savitzky–Golay differentiation is a commonly used spectral pre-treatment method, and in practice the first and second derivatives eliminate the interference of the baseline or background, improve sensitivity and detect and enhance minor or subtle spectral features [18, 19]. Obtained spectra were continuum removed and normalized to enhance the spectral absorption features. The continuum that is a convex hull of straight-line segments is fitted over a reflectance spectrum and subsequently removed by division or ratioing relative to the complete reflectance spectrum [8].
\nIn view of the weak relationship between soil spectroscopy and heavy metals, the logarithmic treatment of feature bands can be used to enhance their relationship [8]. In this chapter, the reflectivity of all bands is extracted to create a single-band reflectivity matrix. The band reflectivity from the second band to the last band was selected as the outer loop and the first band to the penultimate band was selected as the inner loop. Then, the band ratio matrix can be obtained when the outer loop is divided by the inner loop, and the band normalization matrix can be obtained by the difference of reflectivity divided by the summation between the outer loop band reflectance and the inner loop, and the multivariate analysis matrix of two bands and three bands can be obtained by combining two or three output feature bands randomly. Then, these matrices were used for Pearson correlation analysis with the soil heavy metal content matrices. The Pearson correlation coefficient is a measure of the linear dependence (correlation) between two variables X and Y, the greater the absolute value of the correlation coefficient, the greater the correlation between the two variables [20, 21]. Thus, it is possible to predict heavy metal contents in the soil with the high correlation between heavy metal content and soil spectrum [22].
\nIn this chapter, the methods of smoothed, the first derivative, the second derivative and the continuum removal of the spectral data were performed by the View Spec Pro and ENVI to eliminate background noise and enhance the spectral feature. The methods of ratio, normalization and multivariate analysis are used to enhance the correlation between heavy metal content and feature spectra. Finally, the IBM SPSS software was used to establish retrieval model.
\nMn and Co are the predominant heavy metals in mining waste in the study area, so they were selected as indicators of the environmental impacts from mining activity. The chemical analysis results of soil samples are shown in Table 1. From Table 1, we can see that the contents of Mn made a great difference with the contents of Co in soil. The standard deviation (SD) of Mn in the soil was relatively high (773.6), which indicates that its concentrations are of little great difference, while the contents of Co in the soil is of small difference.
\n\n | Max | \nMin | \nMean | \nSD | \n
---|---|---|---|---|
Mn | \n4114.0 | \n531.3 | \n1536.1 | \n773.6 | \n
Co | \n111.9 | \n6.00 | \n43.5 | \n25.87 | \n
Content of heavy metals in soil statistics (unit: mg/kg).
\n | Original spectra | \n1st spectra | \n2nd spectra | \nContinuum removed spectra | \n
---|---|---|---|---|
Feature spectra | \n473, 791, 1395, 1413, 1854, 1926, 2136, 2170, 2208, 2243, 2259, 2320, | \n439, 465, 549, 584, 1382, 1396, 1403, 1421, 1452, 1482, 1766, 1890, 1926, 1993, 2121, 2151, 2172, 2200, 2215, 2234, 2280, 2361, 2375, 2398 | \n550, 590, 625, 995, 1006, 1374, 1393, 1403, 1411, 1425, 1466, 1883, 1906, 2138, 2163, 2196, 2209, 2220, 2240, 2291, 2368, 2387 | \n452, 486, 627, 765, 810, 962, 1029, 1285, 1414, 1698, 1786, 1835, 1918, 1922, 2142, 2205, 2236, 2267, 2296, 2342, 2371, 2386, 2411 | \n
Feature spectra of four kinds of curves (unit: nm).
The visual inspection of the measured soil spectra with different pre-treatment methods showed a significant difference. The original spectral curve and the curves pre-processed by the first derivative, the second derivative and the continuum removal method of soil samplings in the study area are shown in Figure 2. The feature spectra can be obtained from these spectrum curves, as shown in Table 2. From Figure 2, we can see that only 12 feature spectra can be selected as the feature bands from the original soil spectral curve at 473, 791, 1395, 1413, 1926 nm and so on; however, more feature spectra can be selected as feature bands from the soil spectral curves after pre-processing at 584, 1382, 1396, 1403, 1421, 1452, 1890, 1906 nm and so on.
\nReflectance spectra of soil samples. (a) Raw reflectance spectral, (b) first-derivative reflectance spectral, (c) second-derivative reflectance spectral, (d) continuum removed reflectance spectral.
The spectral features selected from the reflectance spectra are used for spectral analysis and the band combination with the maximum Pearson correlation coefficient (R) is selected as the feature band to build the inversion models of heavy metals. And the regression equations of heavy metals’ concentrations in the soil are presented in Table 3. For the heavy metal in the soil, the determination coefficients (R2) of the regression equations are Mn: 0.645 and Co: 0.8. And the determination coefficient (R2) of the regression equations indicates that the measured heavy metals have a strong relationship with spectral features. Specifically, the ratio of bands at 2124 and 2296 nm has a strong relationship with the contents of Mn, and the bands at 1918, 1922 and 2205 nm have strong relationships with the contents of Co. And the significance indexes of these regression equations are less than 0.05.
\n\n | Feature bands (nm) | \nR2 | \nRegression equations | \nSignificance F | \n
---|---|---|---|---|
Mn | \n2142, 2296 | \n0.645 | \nY = −33749.8 + 34703.04X | \n2.04507E − 05 | \n
Co | \n1918, 1922, 2205 | \n0.800 | \nY = −235.03–7507.17X1 + 7452.81X2 + 333.65X3 | \n7.73E − 06 | \n
Spectra parameters and regression equation of Mn and Co.
Note: X corresponding to R2142/R2296; X1, X2, X3 corresponding to R2121, R2234, R2398, respectively.
The relationship of the measured and predicted concentrations of Mn and Co in soil is shown in Figure 3. From the scatter diagrams, we can see that there is a good linear relationship between the measured and predicted concentrations of Mn and Co.
\nScatter plots of the measured values and predicted values: (a) Mn and (b) Co.
The contents of 12 test soil samples of Mn and Co can be calculated by these regression equations. Then the F-test was carried out to validate the feasibility of these regression models for predicting heavy metal contents, as shown in Table 4. From the statistics, we can see that the difference of the mean values of Mn and Co is smaller and the P-value of the F-test between measured and predicted values of Co and Mn in the soil is less than 0.05. This indicates that the models can be used to predict the heavy metal contents in the study area.
\n\n | Measurement | \n\n | Prediction | \nP(F ≤ f) | \n||||
---|---|---|---|---|---|---|---|---|
\n | Max | \nMin | \nMean | \n\n | Max | \nMin | \nMean | \n|
Mn | \n4114 | \n531.3 | \n1871.6 | \n\n | 2290.1 | \n924.9 | \n1474.7 | \n0.003 | \n
Co | \n111.9 | \n13.1 | \n44.0 | \n\n | 82.2 | \n22.1 | \n48.4 | \n0.008 | \n
Summary statistics of measured and predicted heavy metal concentration and F-test (concentration unit: mg/kg).
The results of the predicted content of Mn and Co in soil are shown in Figure 4. From the content distribution of the test samplings, we can see that the sampling sites are mainly distributed along the Anning River area. From Figure 4(a), we can see that the content of Mn in the vicinity of Yakou Town is the largest, and it is decreased with the water that flows southward. This indicates that the water has a great influence on the distribution of the content of Mn. From Figure 4(b), we can see that the contents of Co are high in Yuanbaoshan county and Desheng mining area. This indicates that the water has a poor influence on the distribution of the content of Co. The reasons of the high content of Co are (1) the leakage of minerals and slag during transportation and (2) the combustion of Co which is not complete.
\nPredicted content of Mn and Co. (a) The predicted content of Mn and (b) the predicted content of Co.
The roughness of soil surface, molecular vibration and electron transition can be changed by the particles of heavy metals adsorbed by the soil organic matter, which makes it is possible to use the soil spectrum to invert the soil heavy metals. The quantitative relationship between the spectrum and the heavy metal content was established by using spectral analysis and chemical analysis of the soil samples. Then the heavy metal content of Co and Mn can be obtained to provide prediction data for regional soil quality research and treatment. From Figure 4, we can see that the content distribution of each heavy metal is irregular in the study area for the influences of the river, vegetation and mineral transportation. The flow of river could help transform gradients to the location and extent of heavy metal pollution. The absorption of vegetation could reduce the heavy metal pollution in the soil and the mineral transportation could lead to the jumping change of heavy metal pollution.
\nCompared to traditional methods, the field of hyperspectral method has many advantages such as fast, efficient, wide coverage and nondestructive to estimate the heavy metals’ contents. It can provide predictive data for mine environment monitoring to improve the efficiency of monitoring and management of mine area and protect the surrounding residents’ normal life quality. But it still needs much time to collect spectral data and build models, and the monitoring area is limited, and the field sample collection and spectral measurements may contain errors. Therefore, the following research work is to (1) consider the influence of temperature, altitude, weather and other factors on the spectrum to improve the accuracy of the model, (2) obtain the spectral data at the same time when the field data collection is obtained and (3) acquire the Hyperion or AVIRIS imagery of the study area but considering the influence of vegetation, rock and atmosphere on soil spectrum.
\nIn this chapter, a fast and convenient method to get the heavy metals’ contents in the soils of the study area is described, which can provide a prediction for the eco-remediation of heavy metals in the mining area, leading to assign the human and other resources properly. So the time of remediating the heavy metals contaminated soils can be shortened and prevent the further spread of heavy metals in the soils.
\nThe authors would like to thank Wang Jun-wei from Space Star Technology Co., LTD., and Yang Wen-bing from China Nonferrous Metals in Changsha Survey and Design Institute Co., LTD., for the help in the collection of spectra data. The authors would also like to thank Song Lian from Nanjing University and Gao Xiao-jie from the National Geographic Information Bureau of Surveying and Mapping for their help in the chapter’s literal correction.
\nNanotechnology attracted wide attention over the last decades, leading to a very fast development of materials and processing routes. Different areas such as electronics, cosmetics, medicine/biology, optical systems, energy, and many others, have profited from this rapid growth. Having in mind the environmental issues that we are facing in the modern era, the importance of searching for environmentally friendly, recyclable and low cost nanomaterials and fabrication processes is essential. [1]
This has been a concern in strategic areas as large area electronics (LAE), one of the fastest growing technologies in the world, with projected market growth from $31.7 billion in 2018 to $77.3 billion in 2029. [2] LAE includes many segments (e.g., displays, sensors, logic, memory), which are desired to be seamlessly integrated on virtually any object to create smart surfaces. Due to their good electrical properties, transparency, large area uniformity and good mechanical flexibility, oxide thin films have been crucial materials to advance these concepts. [3] Depending on the metal cations (and on the metal to oxygen ratio), metal oxide thin films can be considered as dielectrics, semiconductors or even conductors. [4, 5, 6] Owing to their remarkable electrical properties, In-based materials, such as ITO (indium tin oxide) and IGZO (indium-gallium-zinc oxide) are currently the multicomponent oxide conductor and semiconductor thin films with larger market relevance in LAE. [4, 7] However, indium is an expensive material, due to its scarcity and high market value, appearing in the current list (2020) of the critical raw materials from the European Commission. [8] The same applies for gallium, another element of IGZO. Therefore, the replacement of these materials is imperative to assure long-term sustainability. [1]
This quest for new oxide materials is naturally also transposed for nanostructures, as their fascinating properties will certainly boost even further the demand for oxide (nano)materials in a plethora of industries. Departing from critical cations, ZnO is perhaps the most widely studied oxide nanostructure. Its properties are nowadays well-known and useful for multiple applications, from photocatalysis, to solar cells or biosensors. [9] It can also be prepared by a multiplicity of methodologies, from vapor- to solution-based processes. [10] Multicomponent oxide nanostructures, particularly those based on sustainable materials, have been significantly less studied, but already show great potential to enhance properties and enlarge the range of applications of oxides. As in thin films, a great advantage of these multicomponent materials is the possibility of tuning their properties by adjusting the cationic ratio. [11, 12] Zinc tin oxide (ZTO) is one of the multicomponent oxides that has been explored and has shown very interesting properties when compared with its binary counterparts (ZnO and SnO2). In fact, ZTO was already demonstrated to exhibit similar properties to IGZO in low-temperature thin film transistors (TFTs), while avoiding the use of critical raw materials. [13]
This chapter provides a literature review on the hydrothermal synthesis of ZTO nanostructures, the main properties of this material, and its applications, highlighting its multifuncionality.
Hydrothermal methods have been widely explored and developed in the last years. [14] This method consists in a chemical reaction in an aqueous solution, under high pressure (> 1 atm) and at temperatures usually ranging between 100°C and 300°C. In case of using non-aqueous solvents, the method is called solvothermal. Typically, the solution is kept inside an autoclave and a conventional oven is used as heat source. The pressure inside the autoclave is dependent both on the temperature and the volume used. This allows for a high energy supply for the reactions even at relatively low temperatures. While the typical nucleation and growth mechanism of the oxide nanostructures in these reactions is thought to consist mainly in dissolution–reprecipitation, these mechanisms are often not well understood.
The synthesis of multicomponent oxide materials such as ZTO is usually easier and more efficient by vapor phase methods, such as chemical vapor deposition and thermal evaporation, than by solution processes, due to the higher temperatures of the former. However, vapor phase methods present drawbacks that are important to consider, such as high temperatures (>700°C) and high costs. On the other hand, while inexpensive and simple, the hydrothermal technique still allows for a well-controlled synthesis of the desired nanostructures’ shape and structure with high reproducibility, thus presenting as an excellent alternative to the conventional physical methods. [15, 16] Additionally, while conventional ovens are typically used as the heat source, microwave-assisted synthesis started recently to be widely explored, enabling reduced synthesis duration due to its more efficient and more homogeneous heat transfer process. [17]
ZTO appears commonly in two main forms, a stable one, Zn2SnO4, and ZnSnO3, a metastable phase. The stable Zn2SnO4 phase is an orthostannate with and inverse spinel structure and is a n-type semiconductor with a band gap of 3.6 eV. [18] ZnSnO3, the metastable phase, can have a rhombohedral structure or a perovskite structure either orthorhombic (−orth) or ordered face centered structure (−fcc). [14, 19] This phase is a well-known piezo/ferroelectric material and presents a band gap of 3.9 eV. [18] Several ZTO nanostructures such as nanoparticles, octahedrons, nanocubes, nanowires, and nanoflowers, have been produced by hydrothermal synthesis, appearing in both ZnSnO3 and Zn2SnO4 phases. Figure 1 shows examples of Zn2SnO4 and ZnSnO3 nanostructures with different morphologies and dimensions (0D, 1D, 2D and 3D) produced by hydrothermal synthesis.
Multiple ZTO nanostructures obtained by hydrothermal synthesis, analyzed by scanning electron microscopy (SEM): (a) Zn2SnO4 nanoparticles, from reference [28]; (b) fcc-ZnSnO3 nanoparticles produced by our group; (d) rhombohedral-ZnSnO3 nanowires growth on FTO seed-layer, reprinted with permission from [42], copyright (2020) American Chemical Society; (e) orth-ZnSnO3 nanowires synthesized without employing seed-layers (in form of powder), from reference [43]; (f) orth-ZnSnO3 nanoplates, reprinted with permission from [45], copyright (2020) American Chemical Society; (h) orth-ZnSnO3 hollow spheres, reprinted with permission from [47], copyright (2020) American Chemical Society; (i) orth-ZnSnO3 nanocubes produced by our group; and by transmission electron microscopy (TEM): (c) Zn2SnO4 nanorods, reprinted with permission from [37], copyright (2020) American Chemical Society; and (g) Zn2SnO4 nanoplates, reprinted with permission from [34], copyright (2020) American Chemical Society.
Lehnen et al., for example, reported very small Zn2SnO4 quantum dots (with diameters below 30 nm), produced with a microwave-assisted hydrothermal synthesis, followed by high-temperature annealing. [20] Numerous other reports on Zn2SnO4 nanoparticles have been shown (Figure 1a), either using standard hydrothermal synthesis or solvothermal synthesis. [16, 21, 22, 23, 24, 25, 26, 27, 28] Regarding ZnSnO3 nanoparticles (Figure 1b), several hydro and solvothermal routes have been reported for its synthesis. [29, 30, 31, 32] For instance, Beshkar et al. reported the use of the Pechini method at 80°C to synthesize fcc-ZnSnO3 nanoparticles, followed by a calcination at 700°C for 2 h. [33]
Concerning 1D structures, while several reports on Zn2SnO4 nanowires exist, these consist essentially in vapor phase methods, more specifically in thermal evaporation at high temperature (>750°C), [19] showing the difficulty in obtaining the stable phase of ZTO in the nanowire form. [34] This is emphasized by the fact that there are only a few reports for Zn2SnO4 nanowires from hydrothermal synthesis, mostly assisted by seed-layers. For example, Zn2SnO4 nanowires were grown on a stainless steel seed-layer and from Mn3O4 nanowires. [35, 36] Zn2SnO4 nanorods by hydrothermal synthesis were also reported by Chen et al. (Figure 1c), but only organized in 3D flowerlike superstructures. [37] Regarding ZnSnO3, only a few reports for nanowires exist, also consisting typically in physical processes (carbon-thermal reaction, thermal evaporation or CVD processes). [38, 39] For hydrothermal processing of ZnSnO3 nanowires seed-layers are typically used. Lo et al. employed an FTO thin film as seed-layer for this end (Figure 1d). [40, 41, 42] A different approach was reported by Men et al. who transformed ZnO nanowires into ZnSnO3 nanowires by a hydrothermal synthesis. [41] Recently, our group demonstrated for the first time ZnSnO3 nanowires obtained by an one-step hydrothermal synthesis without employing any seed-layer (Figure 1e). [32, 43]
2D structures of ZTO have also been reported. Joseph et al. synthesized fcc-ZnSnO3 flakes by a hydrothermal method at only 100°C. [44] Guo et al. produced orth-ZnSnO3 nanoplates (Figure 1f) by a hydrothermal process at 260°C for 24 h. [45] Chen et al. obtained orth-ZnSnO3 nanosheets through a hydrothermal synthesis at 180°C for 12 h, where a precipitate of ZnSn(OH)6 was achieved followed by a calcination at 600°C for 3 h. [46] Zn2SnO4 nanoplates have also been reported, for example, by Cherian et al. (Figure 1g). [34]
There are also several reports regarding 3D ZTO nanostructures. Gao et al. reported the synthesis of ZnSnO3 hollow spheres (Figure 1h) by hydrothermal synthesis at 120°C for 3 h. [47] A commonly reported shape for ZTO nanostructures is the nanocube shape (Figure 1i). For instance, Chen et al. reported a synthesis which could result in ZnSnO3 nanocubes or ZnSnO3 nanosheets, depending on the processing temperature. [46] The octahedron shape is also common, and octahedrons of Zn2SnO4 have been reported by several groups, being these identified as the most stable phase and shape for ZTO nanocrystals. Zn2SnO4 octahedrons constituted by nanoplates can also be formed. [48]
While Figure 1 shows the wide range of possibilities offered by hydrothermal synthesis within the ZTO system, it is challenging to obtain structures with a targeted phase (ZnSnO3 or Zn2SnO4 in this case) and shape (e.g. nanosheet or nanowire). [40, 49] For this end, a comprehensive tailoring of the synthesis parameters is required.
Usually the hydrothermal synthesis of ZTO nanostructures is performed inside a teflon-lined stainless-steel autoclave using a conventional oven as heating source. Nevertheless, as previously shown, there are already a few examples of microwave-assisted hydrothermal synthesis of ZTO nanostructures. As an example of a typical method, our synthesis starts with the dissolution of the zinc and tin precursors separately in 7.5 mL of deionized water, followed by their mixture. Then a surfactant (ethylenediamine, EDA) is added, and the solution is magnetically stirred for 30 minutes. The last step is the addition of the mineralizer agent (NaOH). It is observed that milling the precursors before their dissolution in water leads to a more homogeneous result. After the solution preparation, it is transferred into the autoclave and kept in the oven for 24 h at 200°C. After the synthesis, the resultant precipitate (comprising the nanostructures) should be washed several times with deionized water and isopropyl alcohol, alternately, and centrifuged at each time. The nanostructures are usually dried at ≈60°C, in vacuum, for a at least 2 hours. [32, 43]
Understanding the influence of each synthesis parameter is a key step in achieving the desired structures. Specifically, considering seed-layer free processes allows evaluation of the intrinsic influence of each synthesis parameter on the nanostructures’ growth. Moreover, the solvent also plays a major role in the process strongly determining the dissolution and diffusion of the species during the synthesis. When the precursors’ solubility is not high enough, precluding an efficient reaction, mineralizer agents can be used (NaOH, KOH, etc.) to increase the solubility of the species. [50, 51]
To understand the growth within Zn:Sn:O system it is essential to revise the main equations related with the chemical reactions behind each ZTO phases. The chemical reaction processes for the formation of ZnSnO3 nanostructures have been represented in the literature by the following equations: [52]
Regarding Zn2SnO4, its formation has been described by different reactions depending on the precursors and solvents involved in the synthesis. For example, Li et al. represented the chemical reaction of Zn2SnO4 nanowires through the equations below, which have been the most common in literature. [35]
On the other hand, Fu et al. employed a different synthesis method to avoid the use of NaOH, using four different amines (surfactants) instead, [53] represented as:
Several reports show that ZnO and SnO2 crystals can co-exist with ZTO nanostructures when the synthesis parameters differ, even if slightly, from the ideal conditions for ZTO formation. Usually the formation of ZnO (Eq. 11) and SnO2 (Eqs. 12–14) is associated with the two alkaline concentration extremes, higher and lower, respectively. [54]
In fact, in our previous work on seed-layer free synthesis of ZTO nanowires, using zinc and tin chloride precursors at a fixed concentration ratio it was shown that while for lower NaOH concentrations SnO2 nanoparticles were obtained, for higher NaOH concentrations ZnO nanowires (mixed with fcc-ZnSnO3 nanoparticles) were achieved, whereas intermediate NaOH concentrations yielded ZnSnO3 nanowires. [32] As shown in Figure 2, a similar trend is seen even when increasing only the NaOH concentration (keeping the precursors’ concentration fixed). This suggests that there is an optimal concentration of the mineralizer. These results agree with those reported by Zeng et al., [54] however, while the authors suggest specific values of pH for obtaining the different structures (SnO2, Zn2SnO4 and ZnO), in our case the pH is much higher due to the presence of ethylenediamine (EDA) which yields a pH of at least ≈ 12, showcasing the trend specifically with the variation of the NaOH concentration and not necessarily the overall pH. [32]
SEM images of resultant nanostructures from synthesis with different precursors’ molar concentrations, i.e., ZnCl2:SnCl4.5H2O:NaOH of (a) 2:1:12 M, (b) 4:2:24 M (from reference [43]) and (c) 8:4:48 M, respectively, while maintaining the same proportion between them. Increasing the precursors’ molar concentrations the materials obtained follow the common trend when increasing only the NaOH concentration: SnO2 nanoparticles, orth-ZnSnO3 nanowires and ZnO nanowires (mixed with fcc-ZnSnO3 nanoparticles).
As mentioned, the precursors’ solubility is a key factor to achieve a well-controlled synthesis. Our previous work showed that for different zinc precursors (zinc chloride or zinc acetate), maintaining the same tin precursor (tin chloride), the reaction differs, being slower and less homogeneous when using zinc acetate, due its lower solubility in the EDA surfactant. The use of surfactants, such as EDA, cetrimonium bromide (CTAB) and sodium dodecyl sulfate (SDS), is very common specially when aiming to induce the growth of 1D nanostructures. Surfactants act as directing growth agents as their molecules aggregate to the surface of the metallic atoms inducing the growth of specific structures/shapes. The solubility of each precursor in the solvents is a key factor for achieving a better synthesis efficiency and homogeneity. This also influences the Zn to Sn precursor ratio required to optimize the achievement of the desired nanostructures.
The duration and temperature of the synthesis are also crucial to determine the achieved nanomaterials. Several reports showed that below 180°C no ZTO phases are obtained, with the intermediate phase
Schematic of the growth mechanism of ZTO nanostructures (ZnSnO3 nanowires and Zn2SnO4 nanoparticles) on a hydrothermal synthesis as a function of the energy available and the duration of the synthesis. From reference [43].
While tailoring the chemico-physical parameters is always necessary, the use of a seed-layer material, usually a thin film, can be very effective in strongly inducing the growth of a desired structure by means of an epitaxial growth mechanism. This approach is commonly used when 1D structures are aimed, as briefly mentioned in the previous section. The selection of the seed material depends on the desired material and structure (phase and shape) and while several reports for different structures exist, the relation between different seed materials and grown structures was not detailed yet in literature. This depends on a complex interrelation between preferential epitaxial growth and thermodynamical stability of the multiple phases and shapes within the Zn-Sn-O system. While the seed-layer route presents advantages for specific applications such as gate-all-around transistors or photocatalysis, [55, 56] its absence also brings numerous benefits. For instance, one of the main issues related with the use of seed-layers is the common residuals incorporated in the nanostructures, which are usually undesired for the applications. Also, without seed-layers the synthesis is less complex and this approach brings higher degree of freedom concerning the integration of nanostructures into devices. [14, 32, 54]
Obtaining a single phase and shape of a multicomponent oxide as ZTO is highly desirable due to the different characteristic properties of each phase and shape, still by a hydrothermal synthesis is a challenging process as shown in the last section. In addition, the proper identification of the different possible phases obtained is a difficult task.
As previously presented, ZTO can grow in two different structural phases: Zn2SnO4 and ZnSnO3 (fcc, orth and rhombohedral). Their identification by XRD analysis is challenging since both phases and intermediary compounds show very similar diffraction patterns. While the fcc-ZnSnO3 (ICDD 00–011-0274) has a similar pattern to that of the intermediate phase ZnSn(OH)6 (ICDD 01–073-2384), the orth-ZnSnO3 (ICDD 00–028-1486) pattern can be confused with that of a mixture of Zn2SnO4 and SnO2. In fact, the 00–028-1486 card was deleted from the ICDD database for this reason. Figure 4 shows the XRD peaks of these phases. For clarification, the orth-ZnSnO3 identification was performed by peak indexation, using both treor and dicvol methods, for which the determined crystalline structure was proven to be orthorhombic. [32]
(a) Representation of XRD peaks of ICDD cards of: ZnSn(OH)6, fcc-ZnSnO3, orth-ZnSnO3, Zn2SnO4 and SnO2. Note that the card 00–028-1486 (orth-ZnSnO3) was deleted from ICDD. (b) In-situ XRD patterns of ZnsnO3 nanowires during annealing until 850°C. (c) SEM images of the ZnsnO3 nanowires before and after the in-situ annealing experiments in XRD.
As previously stated, temperature conditions can induce different phase transformations. For instance, Bora et al. studied the phase transformation of fcc-ZnSnO3 nanocubes into the inverse spinel Zn2SnO4 through Raman analysis during in-situ annealing treatment. [57] In this study the phase transformation occurred at 500°C.
Phase transformation in the ZnSnO3 nanowires, synthesized by our group, was investigated by recording XRD patterns in the course of in-situ annealing treatment up to 850°C. Figure 4b shows the XRD patterns at different temperatures, where no phase transformation is observable bellow 750°C. At 850°C the characteristic peaks of Zn2SnO4 and SnO2 start to be more pronounced, suggesting the phase transformation described in Figure 3. Nevertheless, a nanowire-like morphology is still obtained after this in-situ annealing experiment (Figure 4c), which was somehow unexpected from the experimental results used to propose the growth mechanism shown in Figure 3.
Thermogravimetry (TG) and differential scanning calorimetric (DSC) measurements up to 1350°C were also performed on ZnSnO3 nanowires to shed light into this. A clear transformation occurred at ≈ 570°C with a mass loss of ≈ 4% (Figure 5), which can be attributed to the expected decomposition of ZnSnO3 into Zn2SnO4 and SnO2. Through XRD patterns (Figure 5b) the orth-ZnSnO3 phase is identified before the annealing, while after the annealing a predominance of SnO2 is noticeable (mixed with Zn2SnO4). SEM images presented in Figure 5c show the nanowires before and after the annealing. After annealing, larger and rounder structures are observed for which energy dispersive X-ray spectroscopy (EDS) analysis showed a predominance of Sn (Sn/Zn ratio of 14.5), in agreement with the XRD analysis.
(a) TG and DSC curves of as-prepared ZnSnO3 nanowires at a heating rate of 10°C/min at N2 atmosphere. (b) XRD patterns and (c) SEM images of ZnSnO3 nanowires before and after annealing (TG-DSC measurements).
The difference of the decomposition temperature observed between the DSC and the XRD annealing treatments can probably be attributed to the annealing process in both techniques. While the XRD annealing is performed through the heating of a platinum foil (where the nanostructures are placed), in DSC the nanostructures are placed in a melting pot, leading to a more efficient heating and faster decomposition of the ZnSnO3 nanowires.
These results show that when annealing processes are demanded to improve the ZnSnO3 crystallinity, it is important to consider phase transformations carefully. Furthermore, it is noticeable that the temperatures to achieve these phase transformations as a post-synthesis treatment are significantly larger than those required during hydrothermal synthesis, owing to the higher energy provided during synthesis due to the combined effect of temperature and pressure.
The wide array of ZTO nanostructures present different physico-chemical properties which are imposed not only by the structures’ shape but also by their phase (Zn2SnO4 or ZnSnO3).
Concerning the optical properties, ZTO is a wide band gap semiconductor, with reported band gap values of 3.46–3.6 eV for Zn2SnO4 and 3.6–3.9 eV for ZnSnO3. [18, 32, 35, 58] Nevertheless, these values are not only dependent on the phase, but also on the shape and size of the nanostructures, with higher band gaps for smaller particles due to the quantum confinement effect. [16]
While optical properties of nanostructures can be determined simply, their electrical properties are much more challenging to access, especially when considering the properties of a single nanostructure. For this reason, there are only a few reports on electrical characterization of single ZTO nanostructures. While most reports are focused on nanowires with lengths >10 μm (mostly produced by physical processes), smaller ZnSnO3 nanowires (lengths <1 μm), produced in our group by hydrothermal synthesis, were probed individually by using nanomanipulators inside SEM, as shown in Figure 6a. For these, an average resistivity (in vacuum) of 7.80 ± 8.63 kΩ·cm was achieved. [32, 59] When compared to the ≈73 Ω·cm reported by Xue et al. for ZnSnO3 nanowires produced by thermal evaporation (990°C), this resistivity is significantly higher, [60] which can be attributed to the higher defect density expected for lower temperature (200°C) and solution-based processes. Concerning Zn2SnO4 nanowires, Karthik et al. reported a resistivity of 6 Ω·cm in vacuum for nanowires synthesized by vapor phase methods at 900°C. [61] Moreover, for Zn2SnO4 nanostructures, which are an n-type semiconductor, mobilities higher than 112 cm2V−1 s−1 have already been reported, highlighting the relevance of using this material for electronic applications. [62]
(a) SEM image showing the tungsten tips of the nanomanipulators, which are contacting in-situ deposited Pt electrodes for the electrical characterization of a single ZnSnO3 nanowire. Atomic force microscopy characterization of individual ZnO and ZnSnO3 nanowires: (b) topographies in noncontact mode, and (c) contact mode tip oscillation as a function of tip-bias ac-voltage. Reprinted with permission from [63]. Copyright 2020 American Chemical Society.
The ZnSnO3 phase is well-known for its piezoelectric properties. A piezoelectric polarization along the c-axis of ≈59 μC/cm2 was reported by Inaguma et al. for ZnSnO3, being much higher than the ≈5 μC/cm2 reported for ZnO. [64, 65] Moreover, the piezoelectric constants of individual ZnSnO3 and ZnO nanowires produced by hydrothermal synthesis were recently determined by piezoresponse force microscopy (PFM) measurements as 23 pm/V and 9 pm/V, respectively (Figure 6b and c). [63] The enhanced piezoelectric properties reported for ZnSnO3 are related with the higher displacement of the Zn atom in the ZnO6 octahedral cell when compared to the one of the Sn atom in the SnO6 octahedral cell, leading to a higher polarization along the c-axis. [66] Even when compared with other 1D nanostructures produced by hydrothermal synthesis, only the piezoelectric constant of the well-known BaTiO3 (31.1 pm/V) and LiNbO3 (25 pm/V) exceeds the value reported for ZnSnO3. Having sustainability in mind, ZnSnO3 is then a very good alternative to both BaTiO3 and LiNbO3 as these contain critical raw materials. [63]
Table 1 summarizes the optical, electrical, and piezoelectric properties of some of the most typical oxide semiconductor nanostructures. These properties show the potential of ZTO compared with other binary and ternary compounds to achieve the desired multifunctionality to meet the concepts of IoT and smart surfaces while avoiding the use of critical raw materials.
Properties | Band gap (eV) | Resistivity (Ω·cm) | Piezoelectric constant or polarization | References |
---|---|---|---|---|
Material | ||||
SnO2 | 3.60 | 2.17 | n/a | [67] |
TiO2 | 3.00–3.40 | 1.09 | n/a | [68, 69] |
ZnO | 3.37 | 1.4–2 × 10−4 * | 5 nC/cm2, 9–26.7 pm/V | [63, 70, 71, 72] |
IGZO | 3.67 | >106–10−3 * | n/a | [73, 74, 75] |
BaTiO3 | 3.47 | 6.25 × 109–2.33 × 108 | 31.1 pm/V | [76, 77, 78, 79] |
ZnSnO3 | 3.90 | 73–1.4 × 103 | 59 nC/cm2, 23 ± 4 pm/V | [18, 32, 60, 63] |
Zn2SnO4 | 3.30–3.70 | 1.6 | n/a | [18, 58] |
Optical, electrical and piezoelectric properties of some of the most typical oxide semiconductor nanostructures.
The properties marked with * are referent to the bulk materials. Abbreviations: n/a – not applicable.
The multicomponent nature, together with the wide range of different ZTO nanostructures provide this material system with truly impressive multifunctionality, which will be briefly covered next, mostly focusing on photocatalysis, energy harvesting and electronic applications.
Industrial actions and human activities play a negative environmental impact, raising water pollution. [80] Oxide nanostructured materials present great advantages for breakdown of water pollutants, as their band gaps are close to the visible light range and they have high surface-to-volume ratios. [81] Moreover, multicomponent oxides such as ZTO have a higher stability in aqueous environments when compared with binary compounds, which is significantly advantageous for photocatalytic applications. [82]
The mechanism of photocatalytic activity of ZTO under UV light can be represented by the equations below [14, 54, 83]:
Considering photocatalytic activity under visible light, Jain et al. [84] proposes the following equations:
Zn2SnO4 nanocrystals were used for the degradation of 50% of reactive red 141 dye in 270 min under sunlight. [85] Different ZnSnO3 structures such as nanowires and nanoplates were already used as photocatalysts for organic pollutants (for example, methylene blue and rhodamine B). [33, 40, 86] Due to its high optical band gap (3.3–3.9 eV) UV light is usually required to photoactivate this material. Nevertheless, fcc-ZnSnO3 nanoparticles were already reported with a very satisfactory photocatalytic behavior on methylene blue degradation under visible light (0.0156 min−1). [81]
Alternatives to the conventional photocatalytic approach have also been explored, making use of the piezoelectric properties of materials such as ZnSnO3 (nanowires and nanoplates) for piezocatalysis (in the dark) [87] or for piezophotocatalysis (under illumination). [42, 87, 88] Indeed, piezoelectricity and ferroelectricity (associated with perovskite structures) have shown to play an important role in photocatalysis, since the photogeneration of electron–hole pairs is enhanced by the dipole moment formed by the polarization electric field across polar materials. [89, 90] A schematic representation of the piezocatalytic mechanism is presented in Figure 7b and shows the influence of the characteristic polarization of the piezoelectric materials, which contributes to the generation of hydroxyl radicals and consequently degradation of rhodamine B. The dye degradation was achieved in 2.5 h, with a degradation rate of 4.5 × 10−2 min−1. [87].
(a) Piezocatalysis using ZnSnO3 nanoparticles under ultrasound exposure. (b) Schematic of the piezocatalytic mechanism. Reprinted with permission from [87]. Copyright (2020) American Chemical Society.
Other interesting applications of photocatalytic properties have been reported, such as the photocatalytic inactivation of Escherichia coli using ZTO nanocubes under visible light. Only a 10% surviving rate was found for the bacteria, whereas the absorption of the visible light was attributed to the inherent surface defects enhancing the absorption edge in the visible region. [82] With this in mind, lower cost methods for nanostructure production (as hydrothermal methods), which typical result in more defective structures, might be advantageous for these applications as defect levels near the band edges may increase the absorption for lower energy levels.
Nanogenerators are devices that can convert external stimulus into electrical energy, being highly interesting for smart and self-sustainable surfaces, as they can be used for sustainable energy sources, biomedical systems and smart sensors. [91] Due to its excellent ferroelectric and piezoelectric properties, different ZnSnO3 nanostructures (i.e., nanowires, nanoplates, nanocubes) have been widely explored for energy harvesting devices and sensitive human motion sensors, through their piezoelectric (induction of electrical charge by the applied mechanical strain) and piezoresistive (electrical resistivity change by the applied mechanical strain) effects, respectively. [45, 66, 92, 93, 94] The fcc-ZnSnO3 nanocubes have been the most popular ZTO structures for these applications. For instance, Wang et al. reported the nanogenerators of fcc-ZnSnO3 nanocubes mixed with polydimethylsiloxane (PDMS), reaching a maximum output of 400 V, 28 μA at a current density of 7 μA·cm−2. [95] While, Paria et al. mixed fcc-ZnSnO3 nanocubes with polyvinyl chloride (PVC), achieving a maximum output of ≈40 V and ≈1.4 μA, corresponding to a power density of 3.7 μW·cm−3 (Figure 8a). [94]
Hybrid nanogenerators of: (a) a composite film based on fcc-ZnSnO3 nanocubes and PVC. Reprinted with permission from [94], copyright 2020 American Chemical Society; and (b) a composite film based on orth-ZnSnO3 nanowires and PDMS. (c) Schematic of the charge generation mechanism in the micro-structured devices of (b). Images (b) and (c) were reprinted with permission from [63], copyright 2020 American Chemical Society.
ZnSnO3 nanoplates were also applied for nanogenerators. Guo et al. reported produced nanogenerators fabricated with orth-ZnSnO3 nanoplates embedded in flat films of PDMS, reaching voltage and current outputs of 20 V and 0.6 μA, respectively, under bending stress. [45] More recently in our group, orth-ZnSnO3 nanowires were mixed with PDMS to fabricate nanogenerators of micro-structured composites (Figure 8b). [63] In the same work, a charge generation and displacement mechanism was proposed, as depicted in Figure 8c. Briefly, the micro-structures induced in PDMS are suggested to improve the force delivery to the nanowires, enhancing its piezoelectric signal, while bringing also a triboelectric contribution to the nanogenerator output. This results in an output voltage, current and instantaneous power of approximately 9 V, 1 μA and 3 μW·cm−2, respectively, when applying a force of only 10 N. For higher forces the devices were capable to reach outputs around 120 V and 13 μA, which was shown to be enough energy to light up LEDs and several small electronic devices. [63]
Electronic applications are always a relevant drive for materials. Multicomponent semiconductor nanostructures as ZTO are particularly interesting for these applications, with wide band gap semiconductors allowing for high-power and high-frequency operations. [50] Field-effect transistors (FETs) are the key elements enabling today’s electronics, being 1D nanostructures particularly interesting in this regard, given the easiness of confining migratory direction of charge carriers through its length, i.e., between source and drain electrodes. Indeed, 1D nanostructures have already proven great usefulness for the upcoming generations of semiconductors in FETs. [96] While several reports already demonstrated ZTO as a candidate for replacement of IGZO in thin film technologies, [13] similarly, ZTO is also one of the most promising multicomponent metal oxides for transistors with nanostructures. [62] Demonstrations of discrete Zn2SnO4 nanotransistors have already been made using nanotransfer molding of ZTO inks followed by annealing at 500°C, or by simple pick-and-place approach of drop-casted ZTO nanowires prepared by CVD above 700°C and by thermal evaporation at 1000°C. [39, 97, 98] While the achievement of on/off ratio ≈106 and field-effect mobility ≈20 cm2/Vs is a good demonstration of the ZTO’s potential, transistors using ZTO nanostructures synthesized by solution processes have not been reported yet. Furthermore, these nanostructures have also been used for the resistive switch layer in the emerging type of memory devices known as memristors. Reports show ZTO as the active material in memristors in the form of both Zn2SnO4 nanowires and ZnSnO3 nanocubes, being the latter especially relevant for this application due to its ferroelectric properties. Properties such as high off/on ratios (>105), long retention times (>5 months) and fast response speeds (<20 ns) are obtained for these devices. [99, 100]
Transforming ZTO or other nanostructures into well-established LAE semiconductor materials, while highly desirable from the performance and functionality point of view, will still require significant advances in reliable techniques for alignment and density control in transparent (and flexible) substrates. [101]
Besides the applications briefly presented above, ZTO nanostructures have also been widely used in sensing applications, with gas sensors being the most popular. [102] Their small crystallite size, high surface-to-volume ratios and surface reactivity result in enhanced sensitivities/selectivity, with multicomponent materials typically presenting smaller response times and superior stabilities compared to binary compounds. [103] Moreover, the implementation of these nanostructures in sensors allows miniaturization of the devices, as well as cost reduction. ZnSnO3 has been reported as an excellent humidity sensor, in different nanostructure forms such as nanoparticles or even in composites of ZnSnO3 nanocubes and Ag nanowires. [29, 104] Additionally, ZnSnO3 nanoparticles were used as electrochemical biosensors for label free sub-femtomolar detection of cardiac biomarker troponin T and a composite of Zn2SnO4 nanoparticles and graphene was used for morphine and codeine detection. [105, 106] Recently, Durai et al.. reported ultra-selective sensors, based on ZnSnO3 nanocubes modified glassy carbon electrode (GCE), for simultaneous detection of uric acid and dopamine through differential pulse voltammetry technique. [107] Zn2SnO4 and ZnSnO3 nanostructures of different shapes such as nanoparticles, nanowires and nanocubes, have also been widely explored as photoconductors. [23, 108, 109, 110, 111] While the optical band gap of these materials is typically in the UV energy levels (hence their transparency in visible range), quantum confinement effects or even defect levels near the band edges can be explored to increase the absorption for lower energy levels. Other applications that have been explored using ZTO nanostructures are related with energy storage and conversion. Zn2SnO4 has been widely used as photoanode for dye solar cells in different nanostructure morphologies such as nanoparticles and nanowires. [21, 35] Cherian et al. reported the performance of nanowires and compared with nanoplates of Zn2SnO4 for Li-batteries. [34] Supercapacitors (SC) have also started to be explored using ZTO nanostructures, with Bao et al. having reported the use of Zn2SnO4/MnO2 core shell in carbon fibers showing a capacitance of 621.6 F·g −1. [112]
Expanding LAE to IoT and smart surface concepts requires an increasing number of objects to have embedded electronics, sensors and connectivity, driving a demand for compact, smart, multifunctional and self-sustainable technology with low associated costs. While nanomaterials are thought to be able to meet these requirements, playing an important role in the future technological world, low cost and sustainable technologies are demanded. For this, both low cost fabrication methods and sustainable materials must be considered. This chapter shows the versatility of the hydrothermal method to control the growth and morphology of zinc tin oxide (ZTO) nanostructures, and the variety of shapes that can be produced for each of the different ZTO phases. Compared to other preparation methods, especially vapor phase methods, hydrothermal synthesis reveals a large set of advantages from both research and industrial viewpoints. First, while the multitude of parameters to control requires an in-depth understanding of their role in the final products, it also brings enormous flexibility to tune the synthesis process for the desired results. Also, it can be performed at low temperature (< 200°C), which is compatible with a wide range of substrates for direct growth, while assuring lower costs. This links perfectly with the demonstrated upscaling capability of hydrothermal synthesis which is a crucial aspect for industrial implementation.
Furthermore, a summary of exciting results that have been reported regarding application in devices of these ZTO nanostructures over the past few years is presented. The multifunctionality of this material system is highlighted by its successful implementation in energy harvesters, photocatalysis, electronic devices, sensors, and others.
The authors would like to thank Ana Pimentel for the TG-DSC measurement.
This work is funded by FEDER funds through the COMPETE 2020 Programme and National Funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the project UIDB/50025/2020, and the doctoral grant research number SFRH/BD/131836/2017. This work also received funding from the European Community’s H2020 program under grant agreement No. 716510 (ERC-2016-StG TREND), No. 787410 (ERC-2018-AdG DIGISMART) and No. 685758 (1D-Neon). This work is part of the PhD Thesis in Nanotechnologies and Nanosciences defended by Ana Rovisco at FCT-NOVA entitled “Solution-based Zinc Tin oxide nanostructures: from synthesis to applications” in December 2019.
The authors declare no conflict of interest.
Authors are listed below with their open access chapters linked via author name:
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