Keywords

3.1 Introduction

Metal oxide-based semiconductors (MOSs) are known for their tunable morphological, optical, and electrical properties and, hence, most popular for gas-sensing applications. Among various MOSs, n-type metal oxides such as WO3, In2O3, ZnO, Fe2O3, and SnO2 are known for their promising performances in various gas-sensing applications (Yuan et al. 2019; Ren et al. 2020; Li et al. 2021; Ma et al. 2020; Zhao et al. 2020; Zhou and Zhang 2021). The gas-sensing applications of p-type metal oxides are relatively limited due to their low sensitivity as compared to n-type metal oxides. Design and construction of nanomaterials with special morphology and nanostructure has a crucial role in enhancing the selectivity and limit of detection (LoD) (Zhou and Zhang 2021). Generally, three-dimensional (3D) nanostructures with variable geometry and crystal facets provide higher surface area and a greater number of active sites which results in efficient gas-sensing applications (Zhou and Zhang 2021). In this chapter, we will briefly discuss about the recent advances in 3D nanostructure-based materials for biological gas-sensing applications.

3.1.1 Principles of Gas Sensing

The general functioning principles of a MOSs-constructed device for gas-sensing detection comprise the variations of electrical properties. So, when a specific gas comes in contact with the sensor, it will change the resistance of materials which is sensitive to gases (Neri 2015; Zhang et al. 2020). If the chemical adsorption of the gas molecules on the semiconductor surface is strong enough, then they exchange electrons between them. This directed to an alteration in the concentrations of charge carriers and as a result conductivity of the materials also changes (Fig. 3.1a) (Neri 2015). The amount of gas adsorbed directly depends on the concentration of the target gas, which in terms set the affiliation between the resistance value and concentration of target gas.

Fig. 3.1
2 illustrations. 1. A circuit. Labels read electrodes, gas molecules, sensing material, substrate, and signal. 2. Diagrams of 2 sensors in color-coded spherical pairs. The labels read n type semiconductor, p type semiconductor, core, electron depletion layer, and hole accumulation layer.

(a) Detection circuit of a MOSs-based sensor [Reproduced with permission from Ref. (Zhang et al. 2020), copyright Elsevier] and (b) working mechanism for MOSs-based sensors. [Reproduced with permission from Ref. (Kim and Lee 2014), copyright Elsevier]

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The mechanistic path involving n-type MOSs is shown in Fig. 3.1b (Kim and Lee 2014; Gurlo et al. 2005). In this type of sensing device, the oxygen molecules from air are adsorbed on the surface and interact with the electrons of sensing materials. These adsorbed oxygen molecules ionize to O2−, O−, and O2− depending on the working temperature. These working temperatures are generally high for MOSs-based sensing devices (Zhou and Zhang 2021). The interlinkage within the sensing surface and target gas results in the formation of a depletion layer, which further consequences in the increase of resistance due to an increase in potential barriers. Based on these mechanisms, reducing gases such as CO, H2, acetone, ethanol, etc. reduces the resistance, whereas oxidizing gases such as SO2, NOX, O3, etc. increase the resistance (Zhang et al. 2020).

Besides MOSs, for other gas-sensing materials based on carbon and MXene, the oxygen molecules adsorption is not necessary for sensing. Herein, the mechanism involves physisorption of target gases on the surface of sensing devices, and they interact through van der Walls or the donor-acceptor interaction. Presence of various functional groups such as -OH, −COOH, and = O also increases the gas molecules adsorption. Also, the number of adsorption sites on the sensing material surfaces and higher binding energy between target gases and sensing surfaces gives rise to efficient detection of gas molecules (Zhou and Zhang 2021; Degler et al. 2019).

3.2 Analytical Performance of Gas Sensors

The performance of sensor devices can be identified by numerous constraints which include the limit of detection, selectivity, sensitivity, cross-sensitivity, response and recovery times, stability, and lifetime. Based on these constraints, the functioning qualities of gas-sensing devices are quantified.

The lowermost concentration of the gas analyte that can be measured by the sensing devices is termed as the limit of detection (LoD). The LoD is a central constraint to know the aptness of any type of gas sensor. For example, when gas sensors are premeditated for air quality nursing, based on their LoD values, it can be checked if they are suitable for the sensing or not. Table 3.1 shows the threshold boundaries of the key air pollutants set by the National Ambient Air Quality Standards (NAAQS) in the USA and also by the European Union (EU) in Europe (Isaac et al. 2022). Although Table 3.1 only shows the LoD values, it is necessary to consider other parameters such as selectivity, cost-effectiveness, and robustness before employing the sensors for air quality nursing.

Table 3.1 European Union and US agencies approved threshold limits for some common pollutants with LoD of best-performing sensors reported in the literature
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The sensitivity of a sensing device can be demarcated as the alteration in electrical signal with respect to the concentration of the target gas. It is represented as the ratio between the response signal when the sensing device is exposed to the target gas analyte and the response signal in the presence of air:

$$ S=\frac{R_g}{R_a} $$
(3.1)

where Rg represents the resistance of air containing a fixed concentration of target gas and Ra represents the resistance in pure air. The sensitivity of a sensing device is dependent on external factors such as humidity and operating temperature and also on the intrinsic properties of the sensing materials (Wang et al. 2010).

The dynamic behavior of a gas sensor can be expressed by its response and recovery times. The definition of response time is the time required to attain a stable signal by the sensing devices when exposed to a specific amount of target gas, whereas the recovery time is the time required for a sensing device to reappearance to its original value in the absence of target gas. Typically, the response time is calculated when the signal takes 90% of the saturation signal and the response time is 10% of the saturation signal. This is for the reason that vast majority of the sensing devices take quite a few hours to reach the concluding saturation value due to slower kinetics after certain time (Isaac et al. 2022).

The aptitude of a gas sensor to identify a target gas among other various gases is titled its selectivity. It is a crucial parameter because numerous gaseous species present with the target gas can change the resistance with a similar way as of the target gas (Seinfeld and Pandis 1998). The reproducibility or stability of a gas sensor is also important for real-world applications as it reflects the ability of the sensing devices to reproduce the same signal when exposed to the target gas several times (Bochenkov and Sergeev 2010).

3.3 3D Nanostructures for Gas Sensing

Although many low-dimensional sensing devices including nanostructures of 0D, 1D, and 2D have been utilized in gas sensing, the main issues which hinder the functioning of these gas-sensing devices are structural stability, dispersibility, and sensibility. However, 3D nanostructures developed by incorporation of low-dimensional particles can overcome the abovementioned drawbacks with excellent gas-sensing applications.

3.3.1 3D Gas Sensors for Environmental Applications

Pollution of air by pollutant gas such as CO, CO2, SO2, NOx, CH4, etc. is a serious global concern as it is harmful to the environment as well as humans due to hasty industrialization, usage of agrochemicals, and the incineration of fuels. Besides this, other gaseous pollutants such as NH3, benzene series, CH2O, etc. originating from industries also severely affect the human health when the exposed limit is exceeded (Mirzaei et al. 2016). Therefore, it is essential to oversee the concentration of these harmful gases for better environment.

3D nanostructures create a better ohmic contact with the substrate it is grown as compared to 1D nanostructures. For example, it was found that 3D hierarchical superstructure of ZnO grown over an FTO substrate has a lower ohmic resistance as compared to 1D ZnO nanowires as reported by Ansari et al. (Ansari et al. 2018). Besides, 3D hierarchical superstructure of ZnO with a large surface area and higher electron mobility results in better NH3 sensing as compared to 1D nanowires of ZnO. Besides morphology, the defects and porosity existing in the 3D ZnO mesoporous thin films are also beneficial for sensing of NO2 gas (Patil et al. 2018; Mane et al. 2021). Also, the 3D morphology of ZnO gives rise to porous structure and high surface area as compared to lower-dimensional ZnO nanostructures which facilitate better transport of mass and diffusion of gas in the sensing material (Mane et al. 2021). Krishnakumar et al. premeditated the effect of various 3D nanostructures of ZnO (flowerlike, starlike, spherical) for sensing of CO gas. All these nanostructures were prepared by a microwave-assisted method. It was found that the nanostructure with flowerlike morphology is more efficient for CO gas sensing than the other two nanostructures due to its trivial crystallite size, larger surface area, and modified barrier in potential (Krishnakumar et al. 2009; Feng et al. 2005). Figure 3.2 shows the sensitivity of ethanol sensing with 3D flowerlike nanostructures of ZnO. SnO2 nanostructures also have potential applications as gas sensors due to its upright selectivity as well as sensitivity to various gases. SnO2 with different morphologies have been extensively explored for gas sensing (Firooz et al. 2009; Periyasamy and Kar 2020; Wang et al. 2016; Yuliarto et al. 2015). Also, 3D nanostructures of WO3 are efficient in sensing NO2 gas with superior selectivity (Cao et al. 2020).

Fig. 3.2
5 illustrations. 1 and 2. S E M scans of linear hair like nanostructures. 3. A line graph with an increasing trend. 4. A graph plots sensitivity versus time with 4 peaks. 5. 4 diagrams of ethanol sensing mechanism. Markings read ethanol, O 2, V B, C B, E F, e negative, O 2 negative, and O negative.

(a, b) SEM images of flowerlike ZnO nanostructures composed of nanorods, (c) the sensitivity of sensors vs ethanol concentration, (d) the sensitivity response of sensors vs ethanol concentration, and (e) the mechanism involving the sensing of ethanol. [Reprinted with permission from Ref. (Feng et al. 2005), copyright AIP Publishing]

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Although, many gas sensors reported in the literature are effective in terms of lower value of LoD and good selectivity, however, operating temperatures for these sensors are very high. For actual environmental applications, gas sensors with lower operating temperature are needed to construct a long-term stable monitoring device.

3.3.2 3D Gas Sensors for Breath Analysis

Exhaled breath samples of human are very complex as it consists of carbon dioxide, nitrogen, oxygen, nitric oxide, and thousands of volatile organic compounds (VOCs). Among them, many VOCs have clinical significance as a biomarker for identification of some specific diseases (Alam et al. 2019). For example, a high level of acetone, nitric oxide, ammonia, hydrogen sulfide, and toluene are typically associated with diabetes, asthma, renal disease, halitosis, and lung cancer, respectively (Awano et al. 2008; Shin et al. 2012; Narasimhan et al. 2001; Gouma and Kalyanasundaram 2008; Peng et al. 2009; Yoon and Lee 2017). Table 3.2 shows some exhaled breath biomarkers related to diseases with their permissible limit (Das and Pal 2020). So, analyzing human respired breath samples through gas sensing characterizes a simple, economical, and faster method for disease diagnosis and prevention. However, the concentrations of disease-associated biomarker are very little (in ppb level). Therefore, gas sensor used for this type of operation should be highly selective toward a specific VOC with very low LoD (Konvalina and Haick 2014).

Table 3.2 Exhaled breath biomarkers related to diseases and their permissible limit
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Using metal-organic framework (MOF) templates, Koo et al. synthesized PdO nanoparticle-functionalized Co3O4 hollow nanocages (Koo et al. 2017). The morphological features are shown in Fig. 3.3a and b. The mechanism involving acetone sensing is shown in Fig. 3.3c. In this study, acetone sensing was demonstrated to be highly selective against other interfering gases by using hollow Co3O4 nanocages with high surface area and higher catalytic activity (Fig. 3.3d). Moreover, as shown in Fig. 3.3e, the sensor was also capable of distinguishing diabetic exhaled breath from that of normal people. Similarly, PdO-loaded NiO/NiCo2O4 truncated nanocages, SnO2 multichannel nanofibers loaded with PtO2 catalysts, and 3D inverse opal In2O3 microspheres show excellent acetone-sensing capabilities due to larger specific surface area, size-tunable pores, periodic porous structure, and highly 3D interconnection (Chen et al. 2018; Jeong et al. 2018; Wang et al. 2018).

Fig. 3.3
5 illustrations. 1 S E M scan illustrates nanocages. 2. T E M scan illustrates a single nanocage. 3. A diagram sensing mechanism in a nanocage. 4 and 5. A graphical illustration of sensing mechanism. It plots principal component 2 versus 1 versus 3.

(a) SEM image of PdO nanoparticle-functionalized Co3O4 hollow nanocages, (b) TEM image of PdO nanoparticle-functionalized Co3O4 hollow nanocage, (c) schematic illustration of acetone-sensing mechanism for PdO nanoparticle-functionalized Co3O4 hollow nanocage, (d) acetone sensing in presence of various interfering gases, and (e) acetone sensing of exhaled breath. [Reprinted with permission from Ref. (Konvalina and Haick 2014), copyright American Chemical Society]

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The abnormal concentration of hydrogen sulfide (H2S) in exhaled breath is a biomarker for diseases such as ischemia, asthma, Down syndrome, Alzheimer’s disease, and halitosis (Jha et al. 2008; Huber et al. 2017; Kamoun et al. 2003; McGeer and McGeer 2010; Choi et al. 2014). For selective H2S sensing, Yang et al. designed and fabricated inverse opal 3D WO3 structures modified with ZnO@Au nanoparticles derived from MOF (Yang et al. 2020). The detection limit was 50 ppb at 170 °C and attributed to increased adsorption energies, a higher negative charge of O, and improved conductivity. Similarly, ZnO encapsulated in MOFs containing Pt nanoparticles showed good H2S-sensing response (Zhou et al. 2022). Smaller Pt nanoparticles present in the MOF cavities activated the H2S sensing. The molecular-sieving effect of MOF (ZIF-8) resulted in high selectivity. It was also found that MOF-derived metal oxides are effective in gas sensing because of their high surface area (Wang et al. 2021; Shi et al. 2022; Wu et al. 2022).

3.3.3 3D Gas Sensors for Food Quality and Safety

Thousands of people are affected by food poisoning every year by consuming food contaminated by parasites, bacteria, and viruses. Early recognition of spoiled food can prevent many serious health issues. The spoiled food which creates poison and the vapors released from this poison can be distinguished using gas sensors (McCabe-Sellers and Beattie 2004).

Many kinds of seafood including fish, due to biodegradation during spoilage, release gases like dimethylamine (DMA) and trimethylamine (TMA) (Messenger et al. 2013). These gases can be used a marker for determining the freshness of this kind of foods. As reported by Sui et al., 3D α-MoO3 flowers with hierarchical structure can effectively detect TMA among various other VOCs with LoD as low as 0.5 ppm (Sui et al. 2015). Literature report suggests that 3D MoO3-based semiconductor is very effective for various amine-based gas sensing (Malik et al. 2021). Due to excellent permeability involved to yolk-shell structure, the gas sensor based on yolk-shell nanoboxes of SnO2/Au/Fe2O3 can detect a LoD of 50 ppb with a rapid response and recovery properties (Liu et al. 2019). ZIF-8-derived ZnO-CsPbBr3 (Z-CPB) polyhedrons were found to be effectual in sensing TEA with response time of 2 seconds (Liu et al. 2022). The morphological features of Z-CPB are shown in Fig. 3.4a, b. The selectivity was very prominent for TEA for all the sensors as related to other analytes such as formaldehyde, ethanol, acetone, benzene, and acetonitrile as shown in Fig. 3.4c. In the mechanism, when CsPbBr3 and ZnO come in contact, the free electrons from CsPbBr3 transfer to ZnO until they reach equilibrium. Thus, at the interface, an electron depletion layer formed for CsPbBr3 and an electron accumulation layer formed for ZnO. This interface between materials leads to the formation of an n-n heterojunction where more oxygen molecules can be adsorbed onto ZnO. The abundant electron resources on ZnO make it easier for this extra oxygen to be absorbed, improving gas-sensing performance (Fig. 3.4d). Spoiled meat product also releases gas like H2S. Mesoporous hierarchical architectures of SnO2 were fabricated using waste scallion root bio-template (Song et al. 2020). The working temperature of the gas detection system was set at 92 °C, which allowed for the successful detection of H2S concentration with a limit of 0.5 ppb. This SnO2-based sensor was effective in tracking H2S concentrations during its decay process in fresh pork and human exhaled breath and within an environment containing high humidity levels. Similarly, Cr2O3-based sensors were synthesized following the same bio-template method and successfully monitored the chicken freshness (Song et al. 2022).

Fig. 3.4
4 illustrations. 1 S E M scan illustrates cuboidal crystal-like structures. 2. S E M scan illustrates a single crystal-like structure. 3. A bar graph plots response versus samples. Peak is illustrated by Z C P B in sample 1. 4. An energy level diagram titled before contact and after contact.

(a, b) SEM images of Z-PCB, (c) selectivity of as-prepared sensors to different gases at 180 °C, and (d) energy-level diagram of Z-CPB. [Reprinted with permission from Ref. (Liu et al. 2022), copyright Elsevier]

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From the reported literature, metal oxide-based gas sensors are far ahead of other sensing materials for various applications. However, 3D morphology-based gas sensors are not explored much as compared to 0D, 1D, and 2D materials. Further exploration on 3D nanostructure-based materials may result in better selectivity, sensitivity, response, and low working temperature.

3.4 Summary and Outlook

This chapter has summarized some topical 3D nanostructure-based materials for applications in gas sensing. Metal oxide-based semiconductors are found to be dominant in many sensing applications due to the ease in synthesis and fabrication, morphological tuning, good sensitivity, and faster response time. As most of the gas-sensing devices have higher operating temperature, the stability of MOSs-based materials in high temperature makes them suitable candidate for gas sensing. Besides pristine nanomaterials, heterojunctions, surface functionalization, doping, and composite materials also tailored the gas-sensing ability of many materials. It was found that the morphological and structural strategy such as exposed facet, porosity, hollow structure, and hierarchical structure can lead the way to a greater number of active sites and higher activity. In disease diagnosis, noninvasive exhaled breath gas sensors make them an ideal alternate to current standard methods. Still there are many critical issues which need to be improved further to get better selectivity, sensitivity, and response. Simultaneous detection of multiple gas analytes in a single device will be more beneficial. Long-term stability of these devices in ambient atmosphere is very poor which limits the applicability outside of lab environment. Materials with low operating temperatures will decrease the power consumption with a better stability, and this could be achieved by hybridization of metal oxides with various carbonaceous materials, chalcogenides, MXenes, metal nitrides, etc. Looking toward the future and with the recent global pandemic, innovations in this field will certainly lead to rapid, portable, and inexpensive diagnostics and accessibility all over the world.