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Ceramics 2018, 1(1), 98-119; doi:10.3390/ceramics1010009

Review
Semiconducting Metal Oxides Nanocomposites for Enhanced Detection of Explosive Vapors
Department of Applied Science and Technology, INSTM Research Unit PoliTO, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
*
Author to whom correspondence should be addressed.
Received: 15 May 2018 / Accepted: 21 June 2018 / Published: 25 June 2018

Abstract

:
In recent years, the detection of ultratraces of nitroaromatic compounds (NACs), such as 2,4,6-trinitrotoluene (TNT), has gained considerable attention due to associated problems related to environment, security against terrorists and health. The principle of NACs detection is simple since any explosive emits a rather small, but detectable number of molecules. Thus, numerous detection techniques have been developed throughout the years, but their common limitations are rather large sizes and weights, high power consumption, unreliable detection with false alarms, insufficient sensitivity and/or chemical selectivity, and hyper-sensitivity to mechanical influences associated with very high price. Thus, there is a strong need of cheap, rapid, sensitive, and simple analytical methods for the detection and monitoring of these explosives in air. Semiconducting metal oxides (SMOs) allow the preparation of gas sensors able to partially or totally overcome these drawbacks, and this paper aims to shortly review the most recent SMOs nanocomposites able to sense explosives.
Keywords:
semiconducting metal oxide; gas sensor; doping; nanocomposite; explosives detection

1. Introduction

In recent years, the detection of ultratraces of nitroaromatic compounds (NACs), such as 2,4,6-trinitrotoluene (TNT), has gained considerable attention due to environmental, anti-terrorist security, and health-related problems associated with NACs [1]. In fact, NACs are toxic compounds able to cause severe health concerns in both animals and humans, leading to anemia, abnormal liver function, cataract development, and skin irritation [2,3].
The principle of NACs detection is simple since any explosive emits a rather small, but detectable number of molecules [4]. Based on the concentration in air at the equilibrium at a certain temperature, explosives can be divided into three groups: high, medium, and low vapor pressure, as illustrated in Figure 1 [5].
Triacetone triperoxide (TATP), ethylene glycol dinitrate (EGDN), 2,3-dimethyl-2,3-dinitrobutane (DMNB, a detection taggant for explosives), nitroglycerin (NG), and 2,4-dinitrotoluene (DNT) present a high vapor pressure. These explosives have equilibrium vapor concentrations in air in the ratio of about one part per million (1 ppm) [5,6,7]. Medium vapor pressure explosives have equilibrium vapor concentrations in air near one part per billion (1 ppb), while low vapor pressure explosives have equilibrium vapor concentrations of one part per trillion (1 ppt). The medium vapor pressure group includes TNT (2,4,6-trinitrotoluene) and ammonium nitrate (NH4NO3) [5,6,7]. The low vapor pressure group includes HMX (octogen), RDX (Research Department Explosive, hexogen or cyclonite), and PETN (pentaerythritoltetranitrate) [5,6,7]. RDX is 1000 times less volatile than 2,4,6-trinitrotoluene (TNT), corresponding to a parts-per-trillion vapor pressure under ambient conditions. Thus, compared to other explosives, trace detection of RDX is highly challenging [8]. The vapor pressure value concerns pure materials; for mixtures containing these explosives, the vapor pressure may be even lower [5].
Apart from nitro-based explosives, usually indicated as military explosives, homemade explosives, also referred to as improvised explosives (IEs), are produced widely by many terrorist groups throughout the world. Ammonium nitrate-fuel oil (ANFO) and urea nitrate (UNi) are two main kinds of IEs [9]. UNi can be transformed into nitro-urea (NH2CONH–NO2), a more powerful explosive, through a dehydration process [9]. The detection of IEs (ANFO, UNi, and nitro-urea) can be achieved by the indirect detection of their raw materials, for example, AN and urea [9].
Numerous detection techniques have been developed throughout the years, but their common limitations are rather large sizes and weights, high power consumption, unreliable detection with false alarms, insufficient sensitivity and/or chemical selectivity, and hyper-sensitivity to mechanical influences associated with very high price [1]. Thus, there is a strong need of cheap, rapid, sensitive, and simple analytical methods for the detection and monitoring of these explosives in air.
Table 1 illustrates that explosives’ melting points are generally below 200 °C, while boiling points are in the range 150–300 °C [10]. Table 1 also indicates that boiling of explosives often leads to molecular decomposition. Once vaporized, the solids decompose into individual molecules, which then further decompose into smaller fragments. Experimentally, it was found that all explosives produce a cloud of reaction products made of nitrogen dioxide (NO2), nitric oxide (NO), ammonia (NH3), hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2), nitrogen (N2), oxygen (O2) and methane (CH4) [10]. Among those, NO2 is the compound that is most easily detected at low concentrations and with relative selectivity using solid state gas sensors [10] because of its high electron affinity. This feature allows it to be set aside from most other gases in the ambient air (Table 2) and enables it to extract in a very efficient way conduction electrons from semiconductor materials [10].
The electron affinity (Ea) of an element is the energy liberated when an atom in the gas phase captures an electron. If electron affinity is negative, this means that an external energy is necessary to the atom to capture an electron [11]. The most abundant molecule in air having a positive electron affinity is oxygen (Ea of about 0.5 eV); thus, if free electrons are available, ions O 2 form spontaneously (Equation (1)) in clean ambient air:
O 2 + e   O 2 ,
In the case air is contaminated with NO2 molecules (Ea of about 2.3 eV) or O3 (Ea of about 2.1 eV), electrons initially captured by O2 molecules are spontaneously transferred (Equations (2) and (3)):
NO 2 + O 2   NO 2 +   O 2 ,
O 3 + O 2   O 3 +   O 2 ,
As most explosives either contain NO 2 or NO 3 side groups (Figure 2), these materials also present high electron affinities (Table 1). Once vaporized, explosives molecules can therefore easily take up negative charge from O 2 ions, thus forming negatively-charged analyte ions, which can then be easily analyzed with regard to their ion mass (this process is extensively used in the IMS (Ion Mobility Spectroscopy) detection of explosives, drugs, and chemical weapons in airports [10].
Semiconductor metal oxides (SMOs) are able to sense gas based on the variation of their electrical properties. SMOs can be divided into two groups according to the operating temperature which dictates the mechanism by which these materials work: the first group is made by materials which follow surface conductance effects (below 600 °C), while the second one is constituted by materials which follow bulk conductance effects (above 700 °C) [8]. At lower temperature, the bulk defect effect is slow and conductance change is due to the formation and removal of surface-adsorbed oxygen species. The bulk conductance materials respond to the changes in oxygen partial pressure in the upper temperature range (>700 °C) and show the equilibrium between atmosphere and bulk stoichiometry [12].
The gas-sensing mechanism of SMO sensors involves two major functions: receptor and transducer. The receptor function involves recognition of a target molecule in gas—solid interface, which leads to an electronic change in the surface of the SMOs [13,14]. Chemical properties of the surface oxygen of the SMO itself are responsible for the receptor function in a sensor [14], which can be modified when the oxide surface is loaded with noble metal or acidic or basic oxide particles. Then, change in receptor function induces a large change in sensitivity. Transducer function is responsible for converting the signal generated by chemical interaction on the oxide surface (change in work function) into an electrical signal. This function is governed by each boundary or neck between grains.
Conductometric (resistive) gas sensors have a very simple construction: they are made of two electrodes, a gas-sensitive layer, a dielectric substrate, and a heater to ensure the working temperature in the active region [15,16]. As mentioned above, the operating principle of such sensors is based on the conductivity change of semiconductor materials under the influence of the gas-environment [15].
Figure 3 depicts receptor and transducer functions as well as physicochemical and material properties involved for a n-type semiconductor gas sensor. On the surface of the grains, the adsorbed oxygen molecules extract electrons from the conduction band and trap the electrons at the surface as ions, which leads to a band bending. Thus, an electron-depleted layer is formed (also called space-charge layer). The space-charge region, being depleted of electrons, is more resistive than the bulk of the SMO. When the particle size (D) of the sensing film is close to or less than twice the thickness of the space-charge layer (L), the sensitivity of the sensor is increased [16,17,18,19,20]. The control of L by impurity doping can give rise to a great change of the sensor response (SR) even when D is the same: Al doping of SnO2 increases SR, while Sb doping of SnO2 decreases SR, respect to pristine SnO2 [20].
The utility factor shown in Figure 3 concerns the attenuation of the response due to the diffusion and reaction of reactive target gases through the pores of the sensing film [19]. Experiments and simulations have shown that in order to achieve high sensitivity and fast response, SMOs should present: high gas permeability (large pore size), large surface area, small grain size, low degree of agglomeration, and optimal thickness [15].
It is now accepted that below 150 °C, oxygen is adsorbed in ionic form (ionosorbed) as O 2 and in the temperature range from 150 °C to 400 °C (usually, the operating temperature of SMOs gas sensors), it dissociates as O . Above 400 °C, formation of O 2 occurs [14,16,17,20,21]. The required electrons are extracted from SMO’sconduction band and trapped at the surface, leading to an electron-depleted layer, as already explained. The presence of negative surface charge leads to band-bending which generates a potential barrier at the surface of the grains (Figure 3). The height and depth of these barriers depend on the amount of charge on the surface and is correlated to the quantity and type of adsorbed oxygen ions. Neutral oxygen species such as physisorbed oxygen are assumed not to play any role in gas-sensing as well as lattice oxygen ions [16,17,21,22].
In the case of a n-type semiconductor, the resistance of the gas sensor decreases when in contact with reducing gases or vapors because surface oxygen species are consumed to oxidize the target gas and release electrons in the conduction band. On the contrary, for a p-type SMO, the conductivity decreases in presence of a reducing gas. The oxygen adsorption, the formation of electrical core–shell structures (due to holes accumulation layer at the outer surface of grains and the insulating core formed after oxygen ions adsorption), the conduction mechanisms, the catalytic activities, and the interactions with humidity of p-type oxide semiconductors are far different from those of n-type oxide semiconductors. In p-type SMOs, conduction occurs preferentially along the hole-accumulation layer near the surface where oxygen anions are adsorbed [23]. Then, the concentration of holes in this hole-accumulation layer decreases when p-type oxide semiconductor gas sensors are exposed to reducing gases [23]. Generally, the gas response of p-type-oxide-semiconductor-based gas sensors (i.e., the relative change in sensor conductance/resistance when a sensor is exposed to a gas) is equal to the square root of that of n-type oxide semiconductor-based gas sensors whose morphologies are identical to the p-type-oxide-semiconductor-based gas sensors [24]. However, the responses of p-type oxide semiconductor gas sensors can be increased by using aliovalent materials (i.e., electronic sensitization), by catalytically promoting gas detection (i.e., chemical sensitization), and by decreasing the size of the nanostructure so that p-type oxide semiconductor gas sensors can be used for practical applications. Finally, the selectivity of p-type oxide semiconductor gas sensors can be further tuned by doping or loading of catalysts that can promote the reaction between the sensor and a specific gas [23].
In 1952, Brattain and Bardeen demonstrated the change of the semiconducting properties of germanium with a variation of the partial pressure of ozone or peroxide vapors in the surrounding atmosphere [25]. Later, Seiyama demonstrated the gas sensing effect on metal-oxides [26], and Taguchi brought metal-oxide semiconductor gas sensors to market in 1971, after founding in Osaka, in 1969, the company Figaro Engineering Inc., which is still today the largest manufacturer of semiconductor gas sensors world-wide.
The gas sensors market is expected to be valued at $1297.6 million USD by 2023, growing at a compound annual growth rate (CAGR) of 6.83% between 2017 and 2023 [27]. Actually, semiconductor oxide materials contribute more than 20% to market [28].
Commercial sensors should meet the following requirements:
  • High response to the target agent,
  • High selectivity to target gas in the presence of a mixture of gases,
  • Fast and reversible interaction with analyte,
  • Low sensitivity of the signal to a change in air humidity,
  • Absence of long-term drift,
  • Short time to operational status,
  • Effective low-cost technology,
  • High reproducibility,
  • Uniform and strong binding to the surface of the substrate.
The first SMO gas sensors were based on ZnO and SnO2. Today, many different oxides have been investigated for manufacturing conductometric gas sensors (Table 3) [15,26].
It was experimentally verified, and it became a rule, that the higher the operating temperature is, the larger should be the band gap (Eg): the optimal band gap must be larger than 2.5 eV for solid-state gas sensors working above 300 °C. This requirement is satisfied by most SMOs. On the contrary, for sensors working at ambient temperature, Eg can be considerably smaller, and this may be even an advantage [30]. However, it has to be underlined that the ability to operate at higher temperatures is an important advantage of solid-state gas sensors, because this considerably reduces the influence of water vapor on gas-sensing characteristics: the lower the operating temperature is, the greater is the sensitivity of the sensors to air humidity [31].
As the sensing material in conductometric gas sensors should be relatively conductive, the concentration of point defects in the SMOs should be rather high: it was experimentally demonstrated that the optimum lies in the range of 1017–1019 cm−3, which corresponds to a conductivity of 10−2–101 Sm/cm. On the contrary, a too high concentration of point defects and, as a consequence, a high electroconductivity or low resistivity, reduces the influence of the surface on the bulk concentration of charge carriers in the grains and hinders the effects on the surface. A too low concentration of free charge carriers (n < 1016 cm−3, i.e., σ < 10−4–10−5 Sm/cm) is obviously also not acceptable [15].
As previously illustrated, conductometric sensors are based on processes occurring on the surface of the metal oxide and relating to adsorption–desorption phenomena and catalytic reactions. Unfortunately, in many cases, it is not known which crystallographic plane is optimal for gas-sensitive effects: we only know that these properties may be controlled through a change of the surface stoichiometry of the metal oxides by using various processing methods, or via the surface modification of metal oxides by catalytically active additives, such as noble or transition metals. However, the choice of optimal working conditions is empirical in nature, and often, the conditions found during such optimization are specific for the target gas and the technology selected for the synthesis and modification of the SMO [15]. However, studies have shown that the crystallographic structure of the metal oxides does not play a fundamental role: SMOs used in gas-sensing applications may have a cubic, hexagonal, orthorhombic, or tetragonal structure [15].
SMO gas sensors are robust and inexpensive; however, they currently lack sensitivity and selectivity to detect low vapor pressure explosives [32]. Introduction of additives into base metal oxides can change their features, such as, for example, the concentration of charge carriers, chemical and physical properties of the metal oxide matrix, electronic and physical–chemical properties of the surface (energetic spectra of surface states, energy of adsorption and desorption, sticking coefficients, etc.), surface potential and intercrystallite barriers, phase composition, sizes of crystallites, and others also [33,34]. Heterojunctions can increase SMOs’ sensitivity and are based on two different metal oxides admixed or layered together (p-n, n-n and p-p diodes made from a p- and a n-type semiconductor) [32,35,36].
The effect of the addition of metallic particles on the gas-sensing properties of SMOs has been widely studied for decades [33,37]. The influence of metallic particles addition has been classified as chemical or electronic according to two basic sensitization mechanisms. In the electronic mechanism, the reaction with the gas molecules takes place on the surface of the introduced clusters and not in the SMO. These clusters change their charge state, leading to a variation of the surface barrier height and producing a conductance change in the metal oxide. In this case, the base semiconductor has only a transducer role of the changes induced in the metallic particles by their interaction with the target gas. In the chemical mechanism, the metal oxide itself acts as a chemical catalyst: the role of the additive is to increase the reaction rate of the gas molecules, which are firstly adsorbed on the metallic cluster and, later, moved to the oxide surface. This process is the so-called spill-over process. Among metallic surface additives, silver is typically considered to be related with an electronic mechanism, whereas palladium, gold, and platinum are expected to lead to the chemical one [18,33,37,38]. There is an optimum concentration for catalytically active modifiers at which their effect is maximal: in the case of nanocrystalline SnO2 modified with Pd, Pt, and Au nanoparticles, this concentration is 0.2 to 3 wt % [39]. Upon exceeding the optimum impurity concentration, the effect decreases for various reasons: the catalytic conversion of the targeted gas on the modifier clusters without involving the semiconductor matrix, the decreasing portion of the semiconductor oxide-free surface and concentration of the adsorbed oxygen species on it, or the percolation of the noble metal clusters [39].
Among the different sensors features that can influence its response, it is now recognized that the lower the crystallite size is, the higher the response is [40]. On the contrary, the response time (the time needed by a sensor to achieve 90% of the total impedance change in the case of gas adsorption) and the recovery time (the time necessary to reach 90% of the total impedance variation in the case of gas desorption) are shortened by the decrease of the crystallite size [40]. However, it was also found that nanostructured films having a smaller crystallite size presented a stronger sensitivity to the air humidity of the surrounding atmosphere. This behavior was explained because the change of the shape from crystallites (t > 10 nm) to spherulites (t < 7–10 nm) is associated with an increased number of structural and electronic defects. The presence of a huge number of atomic steps and corners, typical of spherulite-type grains, could also be a reason favoring the interaction with water vapor [40]. Therefore, if the minimum sensitivity to air humidity and maximum stability of sensor parameters are required, the size of crystallites will be the result of a compromise between these two features [40]. In SMOs, formed by using thick-film technology (like, for example, screen-printing technique), the pore size is strongly correlated with the crystallite size. Then, the larger the crystallite size is, the larger is the pore size. This means that the requirement to increase the pore size for better gas permeability of the thick-film is in contradiction with the need to reduce the crystallite size for achieving a higher sensitivity [40].
Finally, a considerable contribution to the sensor signal is made by the acidic/basic and redox reactions that occur on the surface of the sensing material when interacting with the gas phase. The specificity or selectivity of these reactions is mainly determined by the nature of the available active sites on the surface [41]. Hydroxy groups, chemisorbed oxygen, and coordinatively unsaturated atoms can act as active sites on the oxide surface. The oxidation ability of the oxides is attributed to chemisorbed oxygen and variable valence metal atoms. In the second case, the efficiency of the charge transfer from the matrix to an active cation plays a great role too [41]. Experiment has shown that the use of metal oxide nanocomposites also allows significant increases in the stability of the grain size during annealing, limiting grain growth. Grain growth due to high working temperatures is one of the reasons for the instability of gas sensor parameters during their service life [14].
Thus, this paper aims at shortly reviewing the most recent SMOs nanocomposites able to sense explosives.

2. Most Promising Semiconducting Metal Oxides for Explosive Vapor Detection

2.1. Zinc Oxide Composites

ZnO is a n-type semiconductor with a wide band gap of 3.37 eV, high electron mobility, photoelectric response, and excellent chemical and thermal stability [42]. Therefore, zinc oxide has been extensively studied to detect vapors and gases such as ethanol, urea, TNT, and many more. Promising results with regards to explosives detection are described in ref. [9] where ZnO nanopowders doped with Fe, Co, and Ni were prepared. In this work, 50 mL of a solution of Zn(NO3)2 0.1 M in ethanol and 0.005 M of iron nitrate nonahidrate, cobalt nitrate tetrahydrate, or nickel acetate tetrahydrate were mixed with 50 mL of a 0.15 M solution in ethanol of LiOH.H2O. Both solutions were kept in a fridge at 0 °C and their mixing was done in an ice bath for 2 h. After precipitation, the solid residue was first washed in heptane, then in ethanol, repeatedly for 3 times. Finally, the powder was oven-dried at 60 °C overnight. Then, several thermal treatments at 200°, 400°, or 600 °C for 1 h were done (Figure 4). The sensors were produced by painting a paste obtained by dispersing the sensing material in ethanol (in a ratio of powder:ethanol equal to 4:1) over an alumina substrate with silver electrodes 200 μm-spaced. The sensors were tested with respect to ammonium nitrate, 2,4,6-trinitrotoluene, and purified after dissolution in ethanol, DNT, para-nitrotoluene (PNT), urea, picric acid (PA), and RDX. A fixed amount of explosive was placed in a closed glass container of 250 mL at room temperature and left for 48 h to ensure air saturation with explosive vapors before any measurement. The sensor response was determined according to Equation (4):
R e s p o n s e   ( % ) = ( R a R g ) R a × 100 ,
where R a and R g are the values of the sensor’s resistance under air and under the explosive’s vapor, respectively.
Impressive results were obtained at room temperature, either with nitroaromatic compounds, or with improvised explosives (Figure 5 and Figure 6). Moreover, response and recovery times of the sensors heated at 400 °C were very fast: 2 and 3 s, respectively, for nickel-doped ZnO sensor in the presence of 4.9 ppt of RDX. These results are due to the higher number of oxygen vacancies in the doped samples after the thermal treatment at 400 °C, with respect to the powders heated at 200 °C or 600 °C.
Silica nanosprings 80 μm thick were produced in about 15 min on gold-coated silica substrates in presence of a silica precursor [43]. Then, the springs were coated with ZnO nanoparticles by an atomic layer deposition (ALD) from a solution of zinc diethyl and deionized water at 175 °C under 1 Torr of Ar. Each ALD cycle consists of a pulse of zinc diethyl, followed by argon flow up to a pressure of 2.5 Torr for 10 s. Then, the cell is purged for 8 s, and distilled water is injected for 300 ms, prior to a new purge with a 20 s pulse of Ar. This cycle is repeated 150 times, leading to a uniform coating 75 nm thick. Finally, the ZnO-coated nanosprings were immersed in a solution of Pd acetylacetonate dissolved in ethanol (having a concentration of 1.95 × 10−2 M), dried, and heat-treated at 500 °C for 15 min under a hydrogen and nitrogen flow to reduce the palladium precursor in metallic palladium having an average size of 2.4 ± 1.3 nm (Figure 7).
Sensors responses were determined by means of a sourcemeter (Keithley 2400, Keithley Instruments, Inc., Cleveland, OH, USA) between 100 °C and 500 °C. The best results were obtained at 400 °C. The resistance value changed from some kΩ to some MΩ and from some kΩ to some tens of MΩ, respectively, for an average ZnO crystallite size of 8 nm and 15 nm. Explosives vapors were generated from the VaporJet™ system (MicroFab technologies Inc., Plano, TX, USA): drop-on-demand of the explosive solution (with a volume of 20–200 pL) were produced by a piezoelectric head on a heater which vaporizes them, creating a flow of gas sent to the sensors. Results obtained under few ppb of TATP and TNT (Figure 8) were encouraging and attributed to the catalytic decomposition of the vapors on the sensing material surface at the working temperature [43].
Core-shell ZnO/reduced graphene oxide (rGO) structures were recently manufactured and tested on DNT, TNT e RDX vapors [44]. 20 mg of graphene oxide (GO) were dispersed in 20 mL of dimethylformamide (DMF) and sonicated for 30 min. In the meantime, four different amounts of Zn(CH3COO)2.2H2O were dissolved in 100 mL of DMF: 0.115 g, 0.23 g, 0.46 g, and 069 g, respectively, in the solution N°1, N°2, N°3 and N°4. Then, the GO solution was slowly added to the solution of zinc precursor under stirring, and the mixture was heated at 95 °C for 5 h in a closed vessel (hydrothermal synthesis). After centrifugation, a white-greyish product was obtained which was ethanol and water-washed several times before drying in an oven at 60 °C, and going through final calcination at 350 °C for 6 h (Figure 9). ZnO nanospheres synthesis followed the same process without GO addition. The sensing material was mixed in deionized water in a ratio 4:1 and manually ground in a mortar for 15 min to obtain a paste to be painted on a ceramic substrate with silver interdigitated 200-μm-spaced electrodes. The sensors were tested with respect to 2,4,6-trinitrotoluene (TNT), DNT, para-nitrotoluene (PNT), picric acid (PA), and RDX. A certain amount of explosive was put in a 250 mL glass container at room temperature and left for 48 h to saturate air with vapors before any measurements. The sensor response was then determined according to Equation (5):
R e s p o n s e   ( % ) = ( I g I a ) I a × 100 ,
where I a and I g are the values of the current passing through the sensor under air and under the explosive’s vapor.
I-V curves (Figure 10) present a typical nonlinear behavior, due to the formation of a Schottky junction between the core made of ZnO grains and the rGO shell. Moreover, sensors’ response is negative on TNT, DNT, and RDX vapors exposure, while they are positive under PNT and PA vapors exposure (Figure 10). These results can be correlated with the ability of TNT, DNT, and RDX to easily withdraw electrons to the sensing material with respect to PNT and PA. Thus, ZnO/rGO sensor response after exposure to saturated explosives’ vapors were −56.8%, −58.4%, +20.2%, −80%, and +16.3%, respectively, for TNT, DNT, PNT, RDX, and PA. The sensitivity of the produced materials was excellent, keeping into consideration the vapor pressure of 9 ppb for TNT, 411 ppb for DNT, and of 4.9 ppt for RDX. In addition, response and recovery times were very fast, in the order of some seconds (often < 5 s) (Figure 10). Finally, among the different investigated compositions, the sensor deriving from the solution N°3 gave the best results, while the pristine materials (ZnO and rGO) did not present the same sensitivity as the ZnO/rGO core/shell structures. These results demonstrated that sensors based on Schottky junction are excellent candidates for traces detection [44].
Zinc oxide was also doped with 1% PdCl2, 1 at% TiO2, 1 at% WO3, 5 at% V2O5 and 5 at% Sb2O3 to detect 2,6-DNT, ammonium nitrate, picric acid (PA), and mining explosives (ME) (Table 4) [45]. The ZnO nanoparticles were produced by evaporation at high temperature and have a mean diameter of 35 nm. The sensing materials were painted on alumina tubes with Pt electrodes and the samples were sintered at 650 °C for 2 h after drying in air.
The explosives were put in a sealed container equipped with a Pt–Rh heater applied with 3.5 V while the sensors were applied with 10 V (the working temperature is unfortunately not specified). Some measurements were performed with 4 mg of explosive decomposed in a 60 mL glass flask from which a known volume of gas is sampled and injected in the chamber with the sensor to test (3.34, 10.0, 16.67, 50.0 and 83.34 μg/L) (Figure 11). When necessary, mass flows were used to dilute explosives’ vapors (Figure 12).
The sensors’ response was determined according Equation (6) for oxidizing gases:
S = R a R g ,
and according to Equation (7) for reducing gases:
S = R g R a ,
where R a and R g are the values of the sensor’s resistance under air and under gas vapor, respectively. Sensors response to the mix of gases derived from the thermal decomposition of the explosives are shown in Figure 13.
The static test results under the concentrations of 3.34 and 83.34 μg/L were analyzed by means of discriminant functional analysis (DFA, Figure 14). Figure 14a shows that the response of the sensors can be separated into four groups when the concentration of explosives vapor is higher. On the contrary, when the concentration is lower, there is some overlapping of the responses (Figure 14b). In this latter case, the probability of recognizing an explosive is only 65.6%, because of the low sensors’ response. Finally, when the concentration of vapors is 15.4 μg/L, the four explosives can be clearly distinguished. In conclusion, the sensors based on WO3-doped ZnO evidenced a good response to the three oxidizing gases (equal to 14, 8.5, and 7, respectively for NH4NO3, ME, and PA at the concentration of 83.34 μg/L). However, pure ZnO and TiO2-doped ZnO were better in 2,6-DNT (reducing gas) detection, showing a response of 2.5 and 3, respectively, at the concentration of 83.34 μg/L).

2.2. Zeolites and Metal Organic Frameworks (MOFs)

Zeolites (H-Zeolite, Socony Mobil (ZSM-5), and titanium silicate (TS-1)) were used on screen-printed films of WO3 and In2O3 on alumina substrates of 3 × 3 mm2 with gold electrodes and a platinum heater [46]. H-ZSM-5 zeolite was synthesized by thermally treating a powder of NH4-ZSM-5 at 100 °C for 8 h to remove first humidity and then, at 500 °C for 12 h, to eliminate ammonia. The inks were obtained by mixing the pure powders (In2O3, WO3, TS1 e H-ZSM) with an organic solvent (ESL-400, Agmet Ltd., King of Prussia, PA, USA) (Table 5). Some sensors were made by mixing the In2O3 powder with the two zeolites (30%) (Table 5, Figure 15).
After screen printing each single layer, the films were dried for 10 min by means of an infrared lamp and heated at 600 °C for 1 h. SEM observations revealed a film thickness of about 75 μm.
The sensors were tested at 350 °, 400 °, and 500 °C under a flow of 1000 cm3/min of NO2 (1 ppm), NH3 (50 ppm), or ethanol (EtOH, 100 ppm). Vapors of DMNB (2,3-dimethyl-2,3-dinitrobutane, 3 ppm), dinitromethane (MeNO2, 5000 ppm) and 2-ethylhexanol (296 ppm) were obtained from an air flow sent over the solid. Vapor pressure at 25 °C and 1 atm were assumed to calculate the experimental explosive vapor concentration.
The sensors’ response was determined according to Equation (8) for oxidizing gases:
S = R R 0 ,
and according to Equation (9) for reducing gases:
S = R 0 R ,
where R 0 and R are the values of the sensor’s resistance under air and under gas vapor, respectively.
The sensors were kept for 30 min under pure air flow before being in contact for 600 s with the gas analyte. Then, the surface was regenerated for 900 s. The measurements were conducted either under dry air or under air and 50% RH.
Except for NO2, all the tested gases were reducing ones, thus the sensors’ resistance decreased during the experiments. The optimum temperature was 350 °C, and the sensor response decreased with the increase in the working temperature, except with ammonia and ethanol (Figure 16).
The sensors based on pure WO3 and TS-1 zeolite appeared to be the most sensitive to NO2 (Figure 16). For low 2-ethylhexanol concentrations, the films of In2O3 + TS-1 (30%) were the most sensitive. However, pure indium oxide and pure tungsten oxide films showed higher responses to higher gas concentrations (Figure 17). The best results with DMNB were obtained with the pure In2O3 and the zeolite H-ZSM-5 (30%) sensors, but the response was only slightly dependent on the gas concentration (Figure 18). The best results with nitromethane were reached by the pure In2O3 and the zeolite H-ZSM-5 (30%) sensors.
To conclude, H-ZSM-5 zeolite demonstrated improved sensor responses to nitro-group containing materials and WO3 had good stability to humidity, and as reported in the literature, was very sensitive to NO2 gas.
MOFs are extended crystalline structures wherein metal cations or clusters of cations (“nodes”) are connected by organic “struts” or “linkers” ions or molecules. The variety of metal ions, organic linkers, and structural figures leads to an essentially infinite number of possible combinations [47]. MOFs are often compared to zeolites for their large internal specific surface areas, extensive porosity, and high degree of crystallinity. Therefore, MOFs and zeolites have been utilized for many of the same applications such as gas storage and separation as well as heterogeneous catalysis and gas sensors [47,48]. To this aim, ZIF-8-based nanocomposite membranes (Zeolitic Imidazolate Frameworks) were recently developed [28]. The method involves the atomic layer deposition (ALD) of ZnO nanowires (NWs) and their subsequent conversion to ZIF-8 using a 2-methylimidazole/methanol solution under solvothermal conditions at 100 °C for 24 h, leading to the coverage of ZnO nanowires with a thin ZIF-8 molecular sieve membrane. ZIFs structures represent a subfamily of Metal–Organic Frameworks (MOFs) having ultrahigh porosity, structural, and chemical tunability together with structural flexibility and huge equivalent internal specific surface area [28]. The optimized ZnO ZIF-8-based nanocomposite sensors showed a selective response at 300 °C to H2, in comparison with the pristine ZnO nanowires sensor, while showing negligible responses to C7H8 and C6H6. Although these sensors were not directly evaluated with respect to explosive vapors, the proposed strategy is rather promising to increase the selectivity of SMOs.

2.3. WO3 Composites

WO3-Cr1.65Ti0.35O3 (WO3-CTO) composites were prepared from WO3, Cr2O3, and TiO2 powders via a solid route and thermal treatment at 900 °C for 12 h [32]. The metal oxide paste was then screen-printed onto gold interdigitated patterned electrodes on 3 × 3 mm2 alumina substrates. After drying, the sensors were fired at 600 °C for 1 h. Table 6 displays the investigated sensors.
The sensors were exposed to four test gases (ethanol, ammonia, nitromethane, and nitrogen dioxide) associated with explosives at 300 °C, 400 °C, and 500 °C. The concentration of the gases used were 10%, 20%, 40%, 80%, and 100% of their source concentration (100 ppm for ethanol, 50 ppm for ammonia, and 7200 ppm for nitromethane (NM)). On the contrary, the concentration of nitrogen dioxide was 5%, 10%, 20%, 40%, and 50% of its source concentration (200 ppm). The sensor’s response (S) was calculated according to Equations (8) and (9), respectively, for oxidizing and reducing gases.
Currently a single sensor did not have the selectivity required for the detection of explosives; however, by using an array made of these seven sensors, the selectivity of explosive vapors among the investigated gases is possible. Both composites sensors and 2-layered SMOs showed higher responses to target gases with respect to pristine sensing materials due to the presence of n-p heterojunctions (Figure 19).

3. Conclusions

Outstanding progress has been made in the development of metal oxide gas sensors in the last decades, and it has been demonstrated that the use of nanocomposites in the development of conductometric metal oxide gas sensors for explosives detection is possible, and under optimal compositions, certain parameters of sensors can be effectively improved.
The incorporation of a second phase component in the sensor material, either as second metal oxide or as precious metal nanoparticles, can improve sensitivity by limiting crystallite/grain growth of the principal phase (and thus maintaining a high surface area), by adding a catalytic function and by exercising a favorable influence on the near-surface band structure. Close control of microstructure of both single oxides and of materials combinations can also greatly increase sensitivity. However, a persistent challenge still has to be solved: the interference from, and/or the degradation of gas responses by humidity [49]. As conductometric sensors offer attractive opportunities for addressing the urgent demands for innovative miniaturized devices for on-site detection of explosive materials at a cost that is much lower than current technologies, this drawback must be overcome.
To this aim, for example, experimental results showed that TiO2 nanowires had a significant reduction of cross-sensitivity to water vapor under UV illumination while the response to TNT showed a moderate increase. Photocatalytic oxidation of water molecules on the surface of TiO2 nanowires under UV illumination can be used to increase surface hydroxyl groups that eliminate the cross-sensitivity of the nanowires toward ambient humidity while maintaining a stable response to explosives [50].
Another possible solution to increase the selectivity of SMOs is the use of a catalytic filter on top of them [51,52]. The action of a gas filter may be two-fold: it aims at hindering the interfering gas from reaching the detection substrate, and at the same time, allowing the target gas to reach the sensor surface, and to transform the gas mixture to be detected into a mixture which increases the detection of the target gas.

Author Contributions

This paper is to be attributed in equal parts to the authors.

Funding

This research received no external funding.

Acknowledgments

Acknowledgements going to anonymous Reviewers who have contributed with their suggestions and comments to improve the quality of this paper, to Academic Editors and to all people of the MDPI structures for their valuable work.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Room temperature vapor concentration of explosives (A, B, and C—high, medium, and low, respectively) [5].
Figure 1. Room temperature vapor concentration of explosives (A, B, and C—high, medium, and low, respectively) [5].
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Figure 2. Molecular structure of some explosives: (a) military and (b) improvised ones. All compounds contain strongly electron-attracting NOx side groups (highlighted by green circles) [10].
Figure 2. Molecular structure of some explosives: (a) military and (b) improvised ones. All compounds contain strongly electron-attracting NOx side groups (highlighted by green circles) [10].
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Figure 3. Receptor function, transducer function, and utility factor as well as physicochemical and material properties of SMOs. Reprinted with permission from Ref. [14]. ( Δ φ s : change in work function, EC: conduction level, EF: Fermi level).
Figure 3. Receptor function, transducer function, and utility factor as well as physicochemical and material properties of SMOs. Reprinted with permission from Ref. [14]. ( Δ φ s : change in work function, EC: conduction level, EF: Fermi level).
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Figure 4. FESEM micrographs of heat treated at 400 °C for 1 h ZnO powders doped with: (a) Co; (b) Ni. Reprinted with permission from Ref. [9].
Figure 4. FESEM micrographs of heat treated at 400 °C for 1 h ZnO powders doped with: (a) Co; (b) Ni. Reprinted with permission from Ref. [9].
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Figure 5. (a) Response of two sensors based on ZnO (up) and Fe-ZnO (down) nanoparticles during three successive cycles of exposure to TNT, TNT (purified), DNT, PA, PNT, and RDX vapors (25 °C); (b) schematic diagram of the response changes of ZnO (up) and Fe-ZnO (down) nanoparticle-based sensors; (c) response time and (d) recovery time of the two sensors based on ZnO and Fe-ZnO nanoparticles toward room-temperature saturated vapors of TNT, TNT (purified), DNT, PA, PNT, RDX, AN, and urea. Reprinted with permission from Ref. [9].
Figure 5. (a) Response of two sensors based on ZnO (up) and Fe-ZnO (down) nanoparticles during three successive cycles of exposure to TNT, TNT (purified), DNT, PA, PNT, and RDX vapors (25 °C); (b) schematic diagram of the response changes of ZnO (up) and Fe-ZnO (down) nanoparticle-based sensors; (c) response time and (d) recovery time of the two sensors based on ZnO and Fe-ZnO nanoparticles toward room-temperature saturated vapors of TNT, TNT (purified), DNT, PA, PNT, RDX, AN, and urea. Reprinted with permission from Ref. [9].
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Figure 6. Sensor responses (a); response time (b); recovery time (c) of the two sensors based on Co-ZnO and Ni-ZnO nanoparticles toward room-temperature saturated vapors of TNT, TNT (purified), DNT, PA, PNT, RDX, AN, and urea. Reprinted with permission from Ref. [9].
Figure 6. Sensor responses (a); response time (b); recovery time (c) of the two sensors based on Co-ZnO and Ni-ZnO nanoparticles toward room-temperature saturated vapors of TNT, TNT (purified), DNT, PA, PNT, RDX, AN, and urea. Reprinted with permission from Ref. [9].
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Figure 7. (a) FESEM micrographs of a silica nanospring, (b); SEM micrograph of a ZnO-coated nanospring (c); TEM micrograph of silica nanosprings, without ZnO; (d) coated with Pd nanoparticles. Reprinted with permission from Ref. [43].
Figure 7. (a) FESEM micrographs of a silica nanospring, (b); SEM micrograph of a ZnO-coated nanospring (c); TEM micrograph of silica nanosprings, without ZnO; (d) coated with Pd nanoparticles. Reprinted with permission from Ref. [43].
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Figure 8. Relative change in conductance of a Pd/ZnO nanospring sensor upon exposure (pulses of 0.1 ms) to: TATP (35 ppb) (a); TNT (10 ppb and 5 ppb) (b). Reprinted with permission from Ref. [43].
Figure 8. Relative change in conductance of a Pd/ZnO nanospring sensor upon exposure (pulses of 0.1 ms) to: TATP (35 ppb) (a); TNT (10 ppb and 5 ppb) (b). Reprinted with permission from Ref. [43].
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Figure 9. (a,b) ZnO/rGO micrographs: SEM; (c,d) TEM; X-ray diffraction pattern; (e) Raman spectrum of ZnO/rGO powder (f). Reprinted with permission from Ref. [44].
Figure 9. (a,b) ZnO/rGO micrographs: SEM; (c,d) TEM; X-ray diffraction pattern; (e) Raman spectrum of ZnO/rGO powder (f). Reprinted with permission from Ref. [44].
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Figure 10. (a) Schematic view of the ZnO/rGO core/shell junction-based sensor; (b) typical I-V curves upon exposure to air and TNT saturated vapor; (c) current changes; (d) responses of the sensor towards room-temperature saturated vapors of TNT, DNT, PNT, RDX, and PA; (e) response times; (f) recovery times. Reprinted with permission from Ref. [44].
Figure 10. (a) Schematic view of the ZnO/rGO core/shell junction-based sensor; (b) typical I-V curves upon exposure to air and TNT saturated vapor; (c) current changes; (d) responses of the sensor towards room-temperature saturated vapors of TNT, DNT, PNT, RDX, and PA; (e) response times; (f) recovery times. Reprinted with permission from Ref. [44].
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Figure 11. Static-state experimental setup. Reprinted with permission from Ref. [45].
Figure 11. Static-state experimental setup. Reprinted with permission from Ref. [45].
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Figure 12. Dynamic sampling and low concentration experimental setup. Reprinted with permission from Ref. [45].
Figure 12. Dynamic sampling and low concentration experimental setup. Reprinted with permission from Ref. [45].
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Figure 13. The sensitivity of the six sensors to various concentrations of the four explosives: picric acid (a); mineral explosives (b); NH4NO3 (c); 2,6-DNT (d). Reprinted with permission from Ref. [45].
Figure 13. The sensitivity of the six sensors to various concentrations of the four explosives: picric acid (a); mineral explosives (b); NH4NO3 (c); 2,6-DNT (d). Reprinted with permission from Ref. [45].
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Figure 14. Discriminant functional analysis (DFA) results for static sampling experiments at the concentrations of: (a) 83.3 μg/L; (b) 3.34 μg/L. Reprinted with permission from Ref. [45].
Figure 14. Discriminant functional analysis (DFA) results for static sampling experiments at the concentrations of: (a) 83.3 μg/L; (b) 3.34 μg/L. Reprinted with permission from Ref. [45].
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Figure 15. SEM micrographs of: (a) W.5; (b) In.5 ; (c)W.5.TS1.2; (d) In.5.TS1.2; (e) In.5.ZSM.2; (f) In + TS1.5; (g) In + ZSM.5; (h) blank sensor substrate. Reprinted with permission from Ref. [46].
Figure 15. SEM micrographs of: (a) W.5; (b) In.5 ; (c)W.5.TS1.2; (d) In.5.TS1.2; (e) In.5.ZSM.2; (f) In + TS1.5; (g) In + ZSM.5; (h) blank sensor substrate. Reprinted with permission from Ref. [46].
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Figure 16. Sensors’ response under NO2 (0.05–0.5 ppm) at 350 °C, 400 °C, and 500 °C in dry air. Reprinted with permission from Ref. [46].
Figure 16. Sensors’ response under NO2 (0.05–0.5 ppm) at 350 °C, 400 °C, and 500 °C in dry air. Reprinted with permission from Ref. [46].
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Figure 17. Sensors’ response under 2-ethylhexanol (ca. 15, 30, 59, 89, 118, and 148 ppm) at 400 °C under dry air. Reprinted with permission from Ref. [46].
Figure 17. Sensors’ response under 2-ethylhexanol (ca. 15, 30, 59, 89, 118, and 148 ppm) at 400 °C under dry air. Reprinted with permission from Ref. [46].
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Figure 18. Sensors’ response under DMNB (4 different concentrations starting at about 1.5 ppm) at 350 °C in dry air. Reprinted with permission from Ref. [46].
Figure 18. Sensors’ response under DMNB (4 different concentrations starting at about 1.5 ppm) at 350 °C in dry air. Reprinted with permission from Ref. [46].
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Figure 19. Sensor response for an array of sensors exposed to NM at 300 °C, 400 °C, and 500 °C. Reprinted with permission from Ref. [32].
Figure 19. Sensor response for an array of sensors exposed to NM at 300 °C, 400 °C, and 500 °C. Reprinted with permission from Ref. [32].
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Table 1. Theoretical estimation of electron affinity of some important explosives [10].
Table 1. Theoretical estimation of electron affinity of some important explosives [10].
NameAggregate FormEa (eV)Vapor Pressure at 25 °C (ppb)Melting Point (°C)Boiling Point (°C)Product Gas
TNTSolid~2.39.5580295 (igniting)NO2
RDXSolid~1.20.006205235NO2
PETNSolid~1.60.18141150 (decomposes)NO2
ANSolid3.5812.3170210NO2
UNiSolid~3.70.009163unknownNO2
Table 2. Electron affinity of NO2 relative to other molecular constituents of ambient air [10].
Table 2. Electron affinity of NO2 relative to other molecular constituents of ambient air [10].
GasNameEa (eV)
N2Nitrogen−0.72
O2Oxygen0.448
H2OWater vapornegative
CO2Carbon dioxide−0.6
H2, HCHydrogen, most hydrocarbonsnegative
NO2Nitrogen dioxide2.273
O3Ozone2.103
Table 3. Some investigated SMOs in conductometric sensors, adapted from Ref. [15,29].
Table 3. Some investigated SMOs in conductometric sensors, adapted from Ref. [15,29].
Type of Metal OxideSMO
Binary oxidesZnO, SnO2, In2O3, WO3, TiO2, Ga2O3, Fe2O3, CuO, NiO, ZrO2, Co3O4, Cr2O3, Mn3O4
Ternary oxides and solid solutionsCdIn2O4, Cr2−xTixO3, NiTa2O6, CoTa2O6, CuTa2O6, BaSnO3, LaFeO3, CdFe2O4, Bi2Sn2O7, BixMoyO2, Sn1−xFexOy, NiFe2O4, CaFe2O4, ZnFe2O4; SnO2-Fe2O3, In2O3-Fe2O3, SnO2-CuO, SnO2-AgOx, In2O3-Ga2O3; TiO2-NiO; TiO2-V2O5; ZnO-CuO; SnO2-SiO2
More complex oxidesLi-SmFe2O4, YBa2Cu3O7−δ, Ni0.99Co0.01M0.01Fe1.99O4, BaSn0.95Zr0.05O3, Na0.1Nb0.1W0.8O3, CS4SiW12O40, CoMn0.65Fe1.35O4, BaTiO3-CuO-La2O3; In2O3-SnO2-TiO2; In2O3-ZnO-SnO2
Table 4. Investigated compositions [45].
Table 4. Investigated compositions [45].
Sensor’s NumberComponents of Raw MaterialEa (eV)
1Zn (1% PdCl2)Prepared from Zn nanoparticle flurry and then quick immersion of dry film into 1% PdCl2 solution and out.
2ZnPrepared from Zn nanoparticle flurry.
31 at% TiO2 + ZnPrepared from mixture flurry of TiO2 and Zn nanoparticles with atomic ratio Ti:Zn of 1:99.
41 at% WO3 + ZnPrepared from mixture flurry of WO3 and Zn nanoparticles with atomic ratio W:Zn of 1:99.
55 at% V2O5 + Zn (1% PdCl2)Prepared from mixture flurry of V2O5 and Zn nanoparticles with atomic ratio V:Zn of 5:95 and then quickly immersed into 1% PdCl2 solution and out.
65 at% Sb2O3 + ZnPrepared from mixture flurry of Sb2O3 and Zn nanoparticles with atomic ratio Sb:Zn of 5:95.
Table 5. Investigated compositions [46], (number in parentheses indicates the number of layers).
Table 5. Investigated compositions [46], (number in parentheses indicates the number of layers).
SensorMetal OxideOverlay
W.5WO3 (5)Nil
In.5In2O3 (5)Nil
W.5.TS1.2WO3 (5)TS-1 (2)
In.5.TS1.2In2O3 (5)TS-1 (2)
In.5.ZSM.2In2O3 (5)H-ZSM-5 (2)
In+TS1.5In2O3, 30% TS-1 (5)Nil
In+ZSM.5In2O3, 30% H-ZSM-5 (5)Nil
Table 6. Investigated compositions [32].
Table 6. Investigated compositions [32].
Sensor AbbreviationMetal Oxide (4 Layers)
WO3WO3 100%
CTOChromium titanium oxide (100%)
2L WO32 layers of WO3 over 2 layers of CTO
2L CTO2 layers of CTO over 2 layers of WO3
75% WO375% WO3, 25% CTO
75% CTO75% CTO, 25% WO3
50/5050% CTO, 50% WO3

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