Metal oxide semiconducting nanomaterials for air quality gas sensors: operating principles, performance, and synthesis techniques

Carbon based sensors for air quality monitoring networks; middle east perspective

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

Efficient detection of 45 ppb ammonia at room temperature using Ni-doped CeO2 octahedral nanostructures

To meet the requirements in air quality monitors for the public and industrial safety, sensors are required that can selectively detect the concentration of gaseous pollutants down to the parts per million (ppm) and ppb (parts per billion) levels. Herein, we report a remarkable NH3 sensor using Ni-doped CeO2 octahedral nanostructure which efficiently detects NH3 as low as 45 ppb at room temperature. The Ni-doped CeO2 sensor exhibits the maximum response of 42 towards 225 ppm NH3, which is ten-fold higher than pure CeO2. The improved sensing performance is caused by the enhancement of oxygen vacancy, bandgap narrowing, and redox property of CeO2 caused by Ni doping. Density functional theory confirms that O vacancy with Ni at Ce site (VONiCe) augments the sensing capabilities. The Bader charge analysis predicts the amount of charge transfer (0.04 e) between the Ni-CeO2 surface and the NH3 molecule. As well, the high negative adsorption energy (≈750 meV) and lowest distance (1.40 Å) of the NH3 molecule from the sensor surface lowers the detection limit. The present work enlightens the fabrication of sensing elements through defect engineering for ultra-trace detection of NH3 to be useful further in the field of sensor applications.

Preparation and NH3 gas-sensing properties of Ag/β-AgVO3 nanorods

NH3 gas sensors operating at room temperature, consisting of Ag nanoparticles decorated β-AgVO3 nanorods (Ag/β-AgVO3 NRs), were fabricated via a facile hydrothermal method without the need for a template. The surface characteristics and compositions of Ag/β-AgVO3 NRs were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Ag nanoparticles, ranging in diameter from approximately 20 to 40 nm, were dispersed on the surface of monoclinic β-AgVO3 NRs with diameters ranging from 50 to 105 nm and lengths from 0.3 to 1.3 μm. The NH3 gas sensing properties of Ag/β-AgVO3 NRs were studied under both dry air and humid conditions at room temperature. Comparative analysis demonstrated that the Ag/β-AgVO3 NRs exhibited a strong response to NH3 gas under 70% relative humidity (RH) at room temperature compared to α-AgVO3 NRs. Specifically, the response of the Ag/β-AgVO3 NRs to 5 ppm NH3 increased by 2.25 times as the RH varied from 20% to 80% at room temperature. This enhanced response was attributed to the effects of formation of nanoheterojunctions, nano-metallic Ag activity and the conductivity of NH4+ and OH- ions induced by the presence of humidity. The room temperature NH3 gas sensors based on Ag/β-AgVO3 NRs demonstrated strong responses to low NH3 concentrations, high selectivity, good reproducibility, and long-term stability, and show promise for the development of low-power and cost-effective NH3 gas sensors for practical applications even under high humidity.

Advanced NH3 Detection by 1D Nanostructured La:ZnO Sensors with Novel Intrinsic p-n Shifting and Ultrahigh Baseline Stability

Due to its stability, transportability, and ability to be produced using renewable energy sources, NH3 has become an attractive option for hydrogen production and storage. Detecting NH3 is then essential, being a toxic and flammable gas that can pose dangers if not properly monitored. ZnO chemiresistive sensors have shown great potential in real NH3 monitoring applications; yet, research and development in this area are ongoing due to reported limitations, like baseline instabilities and sensitivity to environmental factors, including temperature, humidity, and interferent gases. Herein, we suggest an approach to obtain sensors with competitive performance based on ZnO semiconducting metal oxides. For this purpose, one-dimensional nanostructured pure and La-doped ZnO films were synthesized hydrothermally. Incorporating large rare earth ions, like La, into the bulk lattice of ZnO is challenging and can lead to surface defects that are influential in gas-sensing reactions. The sensors experienced a temperature-induced p-n shifting at about 100 °C, verified by the Hall effect and AC impedance measurements. The doped sensor showed exceptional stepwise baseline stability and outstanding performance at a relatively low operating temperature (150 °C) with a sensing response of 91 at best (@ 50 ppm NH3) and recorded a tolerance to water vapor up to 70% RH. Alongside p-n shifting, the enhanced performance was discussed in correlation with La doping-triggered changes in the nanostructural and surfacial properties of the films. We validated the proposed technique by producing similar sensors and performing multiple replicates to ensure consistency and reproducibility. We also introduced the fill factor concept into the gas sensor field as a new trustworthy parameter that could improve sensor performance assessment and help rate sensors based on deviation from ideality.

In Situ Nanofiber Patch Boosts Postoperative Hepatocellular Carcinoma Immune Activation by Trimodal Combination Therapy

Poor clinical efficacy associated with postoperative hepatocellular carcinoma (HCC) often results from recurrence and metastasis. Hence, research has focused on establishing an effective multimodal therapy. However, complex combinations of active ingredients require multiple functions in therapeutic systems. Herein, a portable nanofiber patch composing germanium phosphorus (GeP) and anlotinib (AL) was designed to form a versatile platform for molecularly targeted photothermal-immune checkpoint blockade (ICB) trimodal combination therapy. The patches possess hydrophilic, satisfactory mechanical, and excellent photothermal conversion properties. Moreover, they achieve a penetrating and sustained drug release. The near-infrared light-assisted GeP-induced temperature increase regulates AL release, downregulating the expression of vascular-related factor receptors, triggering immunogenic cell death of tumor cells, and inducing dendritic cell maturation. Simultaneously, ICB therapy (programmed cell death ligand 1, PD-L1) was introduced to improve treatment outcomes. Notably, this trimodal combination therapy significantly inhibits vascular hypergrowth, enhances effector T-cell infiltration, and sensitizes the PD-L1 antibody response, boosting immunotherapy to suppress residual HCC recurrence and metastasis. Further validation of the genome sequencing results revealed cell pathways related primarily to regulatory immune effects. This study demonstrates the use of an effective and practical nanofiber patch to improve multimodal therapy of postoperative HCC, with high clinical translation value.

Gas-Sensing Properties and Mechanisms of 3D Networks Composed of ZnO Tetrapod Micro-Nano Structures at Room Temperature

Metal oxide semiconductors (MOSs) hold great promise for electronic devices such as gas sensors. The utilization of ZnO as a conductometric gas sensor material can be traced back to its early stages; however, its application has primarily been limited to high-temperature environments. A gas sensor based on highly porous and interconnected 3D networks of ZnO tetrapod (ZnO-T) micro-nano structures was fabricated via an easy chemical vapor deposition (CVD) method. Homemade instruments were utilized to evaluate the gas-sensing of the sample at room temperature. It exhibited good gas-sensing at room temperature, particularly with a response of up to 338.80% toward 1600 ppm ethanol, while also demonstrating remarkable repeatability, stability, and selectivity. Moreover, the unique gas-sensing properties of ZnO-T at room temperature can be reasonably explained by considering the effect of van der Waals forces in physical adsorption and the synergistic effect of carrier concentration and mobility. The aforementioned statement presents an opportunity for the advancement of gas sensors utilizing ZnO at room temperature.

Tuning atomic-scale mixing of nanoparticles produced by atmospheric-pressure spark ablation

Nanoparticles (NPs) mixed at the atomic scale have been synthesized by atmospheric-pressure spark ablation using pairs of Pd and Hf electrodes. Gravimetric analysis of the electrodes showed that the fraction of each material in the resulting mixed NPs can be varied from ca. 15-85 at% to 85-15 at% by employing different combinations of electrode polarities and thicknesses. These results were also qualitatively corroborated by microscopy and elemental analysis of the produced NPs. When using pairs of electrodes having the same diameter, the material from the one at negative polarity was represented at a substantially higher fraction in the mixed NPs regardless of whether a pair of thin or thick electrodes were employed. This can be attributed to the higher ablation rate of the electrodes at the negative polarity, as already known from earlier experiments. When using electrodes of different diameters, the fraction of the element from the thinner electrode was always higher. This is because thinner electrodes are ablated more effectively due to, at least in part, the increased importance of the associated heat losses compared to its thicker counterpart. In those cases, the polarity of the electrodes had a significantly smaller effect. Overall, our results demonstrate, for the first time, that spark ablation can be used to control atomic scale mixing and thus produce alloyed NPs with compositions that can be tuned to a good extent by simply using different combinations of electrode diameters and polarities. This expands the capabilities of the technique for producing mixed nanoparticle building blocks of well-defined composition that are highly desired for a wide range of applications.

Ultrafast Response and High Selectivity of Diethylamine Gas Sensors at Room Temperature Using MOF-Derived 1D CuO Nano-Ellipsoids

Environmental and health monitoring requires low-cost, high-performance diethylamine (DEA) sensors. Materials based on metal-organic frameworks (MOFs) can detect hazardous gases due to their large specific surface area, many metal sites, unsaturated sites, functional connectivity, and easy calcination to remove the scaffold. However, developing facile materials with high sensitivity and selectivity in harsh environments for accurate DEA detection at a low detection limit (LOD) at room temperature (RT) is challenging. In this study, p-type semiconducting porous CuOx sensing materials were synthesized using a simple solvothermal process and annealed in an argon atmosphere at three different temperatures (x = 400, 600, and 800 °C). Significant variations in particle size, specific area, crystallite size, and shape were noticed when the annealing temperature was elevated. Cu-MIL-53 annealed at 400 °C (CuO-400) has a typical nanoellipsoid (NEs) shape with a length of 61.5 nm and a diameter of 33.2 nm. Surprisingly, CuO-400 NEs showed an excellent response to DEA with an ultra-LOD (Rg/Ra = 7.3 @ 100 ppb, 55% relative humidity), excellent selectivity and sensitivity (Rg/Ra = 236 @ 15 ppm), exceptional long-term stability and repeatability, and a fast response/recovery period at RT, outperforming most previously reported materials. CuO-400 NEs have outstanding gas-sensing characteristics due to their high porosity, 1D nanostructure, unsaturated Cu sites (Cu+ and Cu2+), large specific surface area, and numerous oxygen vacancies. This study presents a generic approach to produce future CuO derived from Cu-MOFs-sensitive materials, revealing new insights into the design of effective sensors for environmental monitoring at RT.

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

Gas-Phase Interaction of CO, CO2, H2S, NH3, NO, NO2, and SO2 with Zn12O12 and Zn24 Atomic Clusters

Atmospheric pollutants pose a high risk to human health, and therefore it is necessary to capture and preferably remove them from ambient air. In this work, we investigate the intermolecular interaction between the pollutants such as CO, CO₂, H₂S, NH₃, NO, NO₂, and SO₂ gases with the Zn₂₄ and Zn₁₂O₁₂ atomic clusters, using the density functional theory (DFT) at the meta-hybrid functional TPSSh and LANl2Dz basis set. The adsorption energy of these gas molecules on the outer surfaces of both types of clusters has been calculated and found to have a negative value, indicating a strong molecular-cluster interaction. The largest adsorption energy has been observed between SO₂ and the Zn₂₄ cluster. In general, the Zn₂₄ cluster appears to be more effective for adsorbing SO₂, NO₂, and NO than Zn₁₂O₁₂, whereas the latter is preferable for the adsorption of CO, CO₂, H₂S, and NH₃. Frontier molecular orbital (FMO) analysis showed that Zn₂₄ exhibits higher stability upon adsorption of NH₃, NO, NO₂, and SO₂, with the adsorption energy falling within the chemisorption range. The Zn₁₂O₁₂ cluster shows a characteristic decrease in band gap upon adsorption of CO, H₂S, NO, and NO₂, suggesting an increase in electrical conductivity. Natural bond orbital (NBO) analysis also suggests the presence of strong intermolecular interactions between atomic clusters and the gases. This interaction was recognized to be strong and noncovalent, as determined by noncovalent interaction (NCI) and quantum theory of atoms in molecules (QTAIM) analyses. Overall, our results suggest that both Zn₂₄ and Zn₁₂O₁₂ clusters are good candidate species for promoting adsorption and, thus, can be employed in different materials and/or systems for enhancing interaction with CO, H₂S, NO, or NO₂.

Adsorption of air pollutants onto silver and gold atomic clusters: DFT and PNO-LCCSD-F12 calculations

We provide a comprehensive investigation of intermolecular interactions between atmospheric gaseous pollutants, including CH₄, CO, CO₂, NO, NO₂, SO₂, as well as H₂O and Ag_n (n = 1-22) or Au_n (n = 1-20) atomic clusters. The optimized geometries of all the systems investigated in our study were determined using density functional theory (DFT) with M06-2X functional and SDD basis set. The PNO-LCCSD-F12/SDD method was used for more accurate single-point energy calculations. Compared to their isolated states, the structures of the Ag_n and Au_n clusters undergo severe deformations upon adsorption of the gaseous species, which become more significant as the size of the clusters decreases. Considering that, in addition to adsorption energy, we have determined the interaction and deformation energy of all the systems. All our calculations consistently show that among the gaseous species examined, SO₂ and NO₂ exhibit a higher preference for adsorption on both types of clusters, with a slightly higher preference for the Ag clusters compared to the Au clusters, with the SO₂/Ag₁₆ system exhibiting the lowest adsorption energy. The type of intermolecular interactions was investigated through wave function analyses, including natural bond orbital (NBO) and quantum theory of atoms in molecules (QTAIM), showing that NO₂ and SO₂ are chemisorbed on the Ag_n and Au_n atomic clusters, whereas the other gas molecules exhibit a much weaker interaction with them. The reported data can be used as input parameters for molecular dynamics simulations to study the selectivity of atomic clusters towards specific gases under ambient conditions, as well as to design materials that take advantage of the studied intermolecular interactions.

Photoactivated Processes on the Surface of Metal Oxides and Gas Sensitivity to Oxygen

Photoactivation by UV and visible radiation is a promising approach for the development of semiconductor gas sensors with reduced power consumption, high sensitivity, and stability. Although many hopeful results were achieved in this direction, the theoretical basis for the processes responsible for the photoactivated gas sensitivity still needs to be clarified. In this work, we investigated the mechanisms of UV-activated processes on the surface of nanocrystalline ZnO, In₂O₃, and SnO₂ by in situ mass spectrometry and compared the obtained results with the gas sensitivity to oxygen in the dark and at UV irradiation. The results revealed a correlation between the photoactivated oxygen isotopic exchange activity and UV-activated oxygen gas sensitivity of the studied metal oxides. To interpret the data obtained, a model was proposed based on the idea of the generation of additional oxygen vacancies under UV irradiation due to the interaction with photoexcited holes.