Metal Oxide Semiconductor Gas Sensors Research Articles

Synergistic adsorption effect on Co3O4(1 1 0) surface to promote the ethanol sensing properties: Experiment and theory

A deep understanding of the relationship between the properties and structure of transition metal oxide semiconductor gas sensors is essential for optimizing sensor design. Comparing and analyzing the sensing properties of nanomaterials with different morphologies and crystal facets by combining the experiment analysis and the density functional theory (DFT) calculations could identify and understand the corresponding mechanisms at the atomic or ionic level. Here, grown Co3O4 nanosheet (Co3O4-NS) with exposed (111) facets, and Co3O4 nanorods (Co3O4-NR) with exposed (110) facets on alumina ceramic tubes were prepared and studied in sensing ethanol. The adsorption characteristics of ethanol on the sensor surface were analyzed by DFT calculation. The results demonstrated that the sensing performance of Co3O4-NR was superior to Co3O4-NS. The adsorption of hydroxyl groups in ethanol was better than the methyl groups on the surface. The oxygen and hydrogen atoms from the hydroxyl groups in ethanol had the lowest adsorption energies (Eads = −3.44 eV) when they interact with the Co atom and the indirectly adjacent low-coordinate oxygen atoms on the (110) surface, respectively. The synergistic adsorption of target gases by exposed surface atoms induced superior sensing properties of the material.

NiO-CuO hydrangeas-like composite derived from Ni/Cu bimetallic MOFs for sensitive detection of H2S at room temperature

Highly sensitive and selective detection of trace-level concentration H2S remains a major challenge for metal oxide semiconductor gas sensor due to sulfuration-induced poisoning. Herein, a strategy to adjust the reaction site by constructing heterojunction with mixed-metal oxide is proposed. In this work, NiO-CuO hydrangeas-like composite is prepared by a solvothermal reaction and subsequent pyrolysis of bimetallic Ni-Cu-BTC in air. The characterization demonstrates that NiO-CuO hydrangeas has a large specific surface area (27.85 m2/g) and porosity (0.14 m3/g), which can provide abundant adsorption sites and diffusion pathways for gas molecules. Moreover, the sensing test results indicate that NiO-CuO sensors exhibit recoverable characteristic and superior sensing performance. The response value of 2NiO-CuO sensor to 1 ppm H2S in dry air at room temperature (25℃) is 3.99, which can be improved to 10.77 in 40 % humidity condition, making it suitable for practical application. Particularly, the sensor has a low detection limit of 50 ppb, a wide linear detection range (50 ppb-2 ppm), good repeatability, selectivity and long-term stability. The improvement of gas sensing response can be attributed to the porous structure, multiple p-p heterojunctions and synergistic effects between NiO and CuO.

Machine Learning-Assisted Gas-Specific Fingerprint Detection/Classification Strategy Based on Mutually Interactive Features of Semiconductor Gas Sensor Arrays

A high-performance machine learning-assisted gas sensor strategy based on the integration of supervised and unsupervised learning with a gas-sensitive semiconductor metal oxide (SMO) gas sensor array is introduced. A 4-SMO sensor array was chosen as a test sensor system for detecting carbon monoxide (CO) and ethyl alcohol (C2H5OH) mixtures using 15 different combinations. Gas sensing detection/classification was performed with different numbers of gas sensor and machine learning algorithms. K-Means clustering was successfully employed to rationally identify the similarity features of targeted gases among 4 different groups, i.e., matrix gas, two single-component gases, and one two-gas mixture, based on only unlabeled voltage-based gas sensing information. Detailed classification was performed through a multitude of supervised algorithms, i.e., 2-layer artificial neural networks (ANNs), 4-layer deep neural networks (DNNs), 1-dimensional convolutional neural networks (1D CNNs), and 2-dimensional CNNs (2D CNNs). The numerical-based DNNs and image-based CNNs are shown to be excellent approaches for gas detection and classification, as indicated by the highest accuracy and lowest loss indicators. Through the analysis of the influence of the number of sensors on the arrayed gas sensor system, the application of machine learning methodology to an arrayed gas sensor system demonstrates four unique features, i.e., a data augmentation methodology, machine learning approach of combining K-means clustering and neural networks, and a systematic approach to optimized sensor combinations, potentially leading to the practical sensor networks based on chemical sensors. Even two SMO sensor combinations are shown to be highly effective in gas discrimination against diverse gas environments assisted through numeric-based DNNs and image-based 1D CNNs, overcoming the simple clustering proposed through the unsupervised K-means clustering.

Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor.

Metal oxide semiconductor (MOS) gas sensors have many advantages, but the main obstacle to their widespread use is the cross-sensitivity observed when using this type of detector to analyze gas mixtures. Thermal modulation of the heater integrated with a MOS gas sensor reduced this problem and is a promising solution for applications requiring the selective detection of volatile compounds. Nevertheless, the interpretation of the sensor output signals, which take the form of complex, unique patterns, is difficult and requires advanced signal processing techniques. The study focuses on the development of a methodology to measure and process the output signal of a thermally modulated MOS gas sensor based on a B-spline curve and artificial neural networks (ANNs), which enable the quantitative analysis of volatile components (ethanol and acetone) coexisting in mixtures. B-spline approximation applied in the first stage allowed for the extraction of relevant information from the gas sensor output voltage and reduced the size of the measurement dataset while maintaining the most vital features contained in it. Then, the determined parameters of the curve were used as the input vector for the ANN model based on the multilayer perceptron structure. The results show great usefulness of the combination of B-spline and ANN modeling techniques to improve response selectivity of a thermally modulated MOS gas sensor.

UV-activated efficient formaldehyde gas sensor based on cauliflower-like graphene-modified In-doped ZnO at room temperature

So far, for photoelectric metal oxide semiconductor gas sensors, it is a huge bottleneck to achieve high response performance and low detection limit of formaldehyde at room temperature. This work introduced the synthesis of cauliflower-like graphene-modified In-doped ZnO (GR/In-ZnO) composites via a facile one-pot method with a large comparative surface area for low-concentration formaldehyde sensing at room temperature. GR/In-ZnO-300 showed the highest response to 10 ppm formaldehyde, up to 1891%, which was 12 times that of In-ZnO-300. In addition, the theoretical detection limit of GR/In-ZnO-300 was as low as 13 ppb, and the response at this concentration was 137%. Through the characterization of Surface Photovoltage (SPV), Transient Photovoltage (TPV), Surface Photocurrent (SPC), X-ray Photoelectron Spectroscopy (XPS), and nitrogen gas adsorption isotherms, good formaldehyde sensing performance of GR/In-ZnO-300 was attributed to the excellent separation and transmission capacity of photogenerated carriers, the high specific surface area (109.2 m2/g), and abundant oxygen defects on the surface. This work provides a feasible strategy for the design of low-concentration formaldehyde sensing materials with a rapid response at room temperature.

Concept and realization of a modular and versatile platform for metal oxide semiconductor gas sensors

Abstract To meet the requirements for high quality data capturing of metal oxide semiconductor (MOS) gas sensors, a platform is presented that can handle a large variety of sensors. Besides manufacturer modes, the platform is able to run sensors in temperature cycled operation (TCO). A system concept is presented that provides hardware modules to adapt to different MOS types like analog/digital or thin film/thick film. For analog sensors, an analog frontend with two different power variants handles the different power demands of thick film and thin film sensors. The concept and realization of the analog frontend is shown. For digital sensors, a generalized design with digital signal input sections was developed to receive data via I2C, SPI, or UART. A single microcontroller model constitutes the core of each board variant and it is flashed with a unified firmware which manages the module specific tasks. For continuity and usability, a graphical user interface (GUI) is presented that allows the user to modify measurement parameters and monitor the measurement. GUI and firmware are tuned to one another and exchange data and information to perform user commands. The validation of the accuracy of the analog temperature control is discussed and the successful usage of the system in various applications like indoor air quality (IAQ) is shown.

Application of Two-Dimensional Materials towards CMOS-Integrated Gas Sensors.

During the last few decades, the microelectronics industry has actively been investigating the potential for the functional integration of semiconductor-based devices beyond digital logic and memory, which includes RF and analog circuits, biochips, and sensors, on the same chip. In the case of gas sensor integration, it is necessary that future devices can be manufactured using a fabrication technology which is also compatible with the processes applied to digital logic transistors. This will likely involve adopting the mature complementary metal oxide semiconductor (CMOS) fabrication technique or a technique which is compatible with CMOS due to the inherent low costs, scalability, and potential for mass production that this technology provides. While chemiresistive semiconductor metal oxide (SMO) gas sensors have been the principal semiconductor-based gas sensor technology investigated in the past, resulting in their eventual commercialization, they need high-temperature operation to provide sufficient energies for the surface chemical reactions essential for the molecular detection of gases in the ambient. Therefore, the integration of a microheater in a MEMS structure is a requirement, which can be quite complex. This is, therefore, undesirable and room temperature, or at least near-room temperature, solutions are readily being investigated and sought after. Room-temperature SMO operation has been achieved using UV illumination, but this further complicates CMOS integration. Recent studies suggest that two-dimensional (2D) materials may offer a solution to this problem since they have a high likelihood for integration with sophisticated CMOS fabrication while also providing a high sensitivity towards a plethora of gases of interest, even at room temperature. This review discusses many types of promising 2D materials which show high potential for integration as channel materials for digital logic field effect transistors (FETs) as well as chemiresistive and FET-based sensing films, due to the presence of a sufficiently wide band gap. This excludes graphene from this review, while recent achievements in gas sensing with graphene oxide, reduced graphene oxide, transition metal dichalcogenides (TMDs), phosphorene, and MXenes are examined.

Electronic nose and wireless sensor network for environmental monitoring application in pulp and paper industry: a review.

Pulp and paper industries emit various odorous gases during the pulp production and paper-making phase, which are unpleasant and have harmful effects on the human body. The working staffs are continuously exposed to these gases and develop various health issues. Hence, regular monitoring and analysis of such gases are necessary to avoid any sudden high concentration exposure and to prevent adverse health effects on the staff. An electronic nose (EN) has an array of gas sensors with an alert system for early detection of gases. Various ENs have been developed for varying applications till date. The detailed knowledge of the sensors used, their sensitivity and technology is helpful in development of any EN. The objective of this study is to comprehensively review various developed ENs with respect to their gas sensing and pattern recognition (PR) technologies. The information on gases released from pulp and paper industries is also compiled. The evolution of EN technology, its various applications, challenges in developing EN and its utility in safeguarding the industrial workers' life have been described. Further, gap analysis among previously developed EN, contemporary EN and wireless sensor network (WSN) is elaborated. It will facilitate future researchers for better selection of sensors and PR technologies while developing EN. The commonly used sensing technologies are described with their advantages, disadvantages and working principles. Metal oxide semiconductor (MOS) gas sensor and ANN algorithm show better result and hence recommended in the development of EN, whereas ZigBee protocol has been widely used for WSN.

Deep Learning Based Calibration Time Reduction for MOS Gas Sensors with Transfer Learning

In this study, methods from the field of deep learning are used to calibrate a metal oxide semiconductor (MOS) gas sensor in a complex environment in order to be able to predict a specific gas concentration. Specifically, we want to tackle the problem of long calibration times and the problem of transferring calibrations between sensors, which is a severe challenge for the widespread use of MOS gas sensor systems. Therefore, this contribution aims to significantly diminish those problems by applying transfer learning from the field of deep learning. Within the field of deep learning, transfer learning has become more and more popular. Nowadays, building a model (calibrating a sensor) based on pre-trained models instead of training from scratch is a standard routine. This allows the model to train with inherent information and reach a suitable solution much faster or more accurately. For predicting the gas concentration with a MOS gas sensor operated dynamically using temperature cycling, the calibration time can be significantly reduced for all nine target gases at the ppb level (seven volatile organic compounds plus carbon monoxide and hydrogen). It was possible to reduce the calibration time by up to 93% and still obtain root-mean-squared error (RMSE) values only double the best achieved RMSEs. In order to obtain the best possible transferability, different transfer methods and the influence of different transfer data sets for training were investigated. Finally, transfer learning based on neural networks is compared to a global calibration model based on feature extraction, selection, and regression to place the results in the context of already existing work.

Oxygen vacancy engineering on cerium oxide nanowires for room-temperature linalool detection in rice aging

It is a huge challenge for metal oxide semiconductor gas sensors to inspect volatile organic compounds (VOCs) at room temperature (RT). Herein, the effective utilization of cerium oxide (CeO2) nanowires for RT detection of VOCs was realized via regulating its surface chemical state. Oxygen vacancy engineering on CeO2 nanowires, synthesized via hydrothermal method, can be manipulated by annealing under various controlled atmospheres. The sample annealed under 5%H2+95%Ar condition exhibited outstanding RT sensing properties, displaying a high response of 16.7 towards 20 ppm linalool, a fast response and recovery time (16 and 121 s, respectively), and a low detection of limit of 0.54 ppm. The enhanced sensing performance could be ascribed for the synergistic effects of its nanowire morphology, the large specific surface area (83.95 m2/g), and the formation of extensive oxygen vacancy accompanied by an increase in Ce3+. Additionally, the practicability of the sensor was verified via two varieties of rice (Indica and Japonica rice) stored in various periods (1, 3, 5, 7, 15, and 30 d). The experimental results revealed that the sensor was able to distinguish Indica rice from Japonica rice. Accordingly, the as-developed sensor delivers a strategic material to develop high-performance RT electronic nose equipment for monitoring rice quality.

Fabrication of a humidity-resistant formaldehyde gas sensor through layering a molecular sieve on 3D ordered macroporous SnO2 decorated with Au nanoparticles

The development of highly-sensitive and moisture resistant semiconductor metal oxide (SMO) gas sensors for practical application remains a challenge. Here, a three-dimensional ordered macroporous (3DOM) SnO2 decorated with Au nanoparticles (NPs) was obtained via a facile self-assembly template method and the Au content was optimized. The resulting 3DOM Au/SnO2 gas sensor displayed remarkable performance, with its response and lower actual detection limit to formaldehyde 10.2 times higher and 500 times lower, respectively, compared to the undecorated 3DOM SnO2 sensor at 110 °C. Meanwhile, the fast response-recovery process and the repeatability, stability, and selectivity of the sensor indicate that it can meet the requirements for practical application. Density functional theory (DFT) calculations revealed that the Au/SnO2 NPs have strong adsorption energies and electrical conductivity, allowing for selective formaldehyde detection. Further, the 3MCM-48 layer protects the 3DOM Au/SnO2 layer from moisture, allowing at least 80 % of the initial response value to formaldehyde to be maintained even at> 90 % relative humidity. Hence, the proposed strategy represents, for the first time, a universal and effective way to achieve high response and moisture-resistant formaldehyde sensors and demonstrates the potential of SMO gas sensors for indoor air monitoring.

Novel combined waveform temperature modulation method of NiO-In2O3 based gas sensor for measuring and identifying VOC gases

The extremely widely applied metal oxide semiconductor (MOS) gas sensors suffer from underdeveloped selectivity, and dynamic measurement methods are expected to solve this issue. In this paper, the In 2 O 3 and NiO-In 2 O 3 gas sensors were prepared, and then their common gas sensing performances were measured. The response and response time of the NiO-In 2 O 3 gas sensor were more excellent, and thus it was chosen for dynamic measurement. We perform periodic temperature modulated dynamic measurement of six common VOCs. By comparing and analyzing the response curves under common heating waveforms, a combined waveform of rectangular wave and triangular wave is customized as the heating waveform. We can obtain response curves with rich features and high amplitude, which is beneficial to the qualitative and quantitative identification of VOCs. The gas types are first identified employing a support vector machine (SVM) with 100% accuracy with a stepwise identification strategy. Subsequently, the data set is processed applying principal component analysis (PCA) and the principal components with a cumulative contribution above 90% are selected for concentration prediction. Polynomial fitting is used for quantitative analysis of VOCs with a relative error of regarding 5%. The results are indicative that the combination of rectangular-triangular wave temperature modulation and stepwise identification scheme can effectively solve the issue of underdeveloped selectivity of gas sensors. • The sensing properties of In 2 O 3 and NiO-In 2 O 3 composites to VOCs were compared and NiO-In 2 O 3 was more excellent. • A combined waveform of rectangular and triangular was customized as the heating waveform. • The gas types were identified employing a support vector machine (SVM) with 100% accuracy. • Concentration prediction was performed applying a combined algorithm with a relative error of regarding 5%.

Modeling Interfacial Interaction between Gas Molecules and Semiconductor Metal Oxides: A New View Angle on Gas Sensing.

With the development of internet of things and artificial intelligence electronics, metal oxide semiconductor (MOS)-based sensing materials have attracted increasing attention from both fundamental research and practical applications. MOS materials possess intrinsic physicochemical properties, tunable compositions, and electronic structure, and are particularly suitable for integration and miniaturization in developing chemiresistive gas sensors. During sensing processes, the dynamic gas-solid interface interactions play crucial roles in improving sensors' performance, and most studies emphasize the gas-MOS chemical reactions. Herein, from a new view angle focusing more on physical gas-solid interactions during gas sensing, basic theory overview and latest progress for the dynamic process of gas molecules including adsorption, desorption, and diffusion, are systematically summarized and elucidated. The unique electronic sensing mechanisms are also discussed from various aspects including molecular interaction models, gas diffusion mechanism, and interfacial reaction behaviors, where structure-activity relationship and diffusion behavior are overviewed in detail. Especially, the surface adsorption-desorption dynamics are discussed and evaluated, and their potential effects on sensing performance are elucidated from the gas-solid interfacial regulation perspective. Finally, the prospect for further research directions in improving gas dynamic processes in MOS gas sensors is discussed, aiming to supplement the approaches for the development of high-performance MOS gas sensors.

Bimetal Ag NP and Au NC modified In2O3 for ultra-sensitive detection of ppb-level HCHO

Detecting trace ppb-level of formaldehyde (HCHO) in indoor ambient and human exhaled breath using metal oxide semiconductor (MOS) gas sensor has attracted increasing attention. Herein, Ag nanoparticles (Ag NP) modified In2O3 and Au nanocage (Au NC) modified In2O3 as well as bimetal Ag NP and Au NC co-modified In2O3 were successfully prepared via one-step microwave-assisted solvothermal method and were used to construct semiconductor gas sensor. It is exciting to observe that the sensor based on Ag6Au1/In2O3 exhibits the highest response of 277 toward 5 ppm HCHO at low operating temperature (170 °C), which is about 7 times, 38 times and 102 times higher than 6 wt. % Ag/In2O3, 1 wt. % Au/In2O3 and pristine In2O3, respectively. Moreover, the selectivity and detection limit (down to 26 ppb) of Ag6Au1/In2O3 sensor to HCHO have been proven to be greatly improved. This excellent sensing performance indicates that the proposed bimetal Ag and Au co-modification with synergistic catalysis effect offers an effective design for highly sensitive and efficient HCHO detection devices. Moreover, the mechanism of HCHO sensing with Au and Ag co-loaded In2O3 was explored by the calculations obtained from first-principle density functional theory studies.

TinyML Model for Classifying Hazardous Volatile Organic Compounds Using Low-Power Embedded Edge Sensors: Perfecting Factory 5.0 Using Edge AI

In this letter, we present an intelligent chemical sniffer, capable of detecting hazardous volatile organic compounds (VOCs) using artificial intelligence deployed at the edge. The proposed solution utilizes a multichannel metal–oxide–semiconductor gas sensor for generating unique signature responses from the organic compounds. These signature responses are then used for training a quantized neural network model in the cloud and then deployed onto an embedded development platform for edge inference. In this approach, we utilized TensorFlow Lite for microcontrollers, an edge computing framework (developed by Google) to quantize a 32-bit floating point precision neural network model into an 8-bit integer precision model for deployment on low-power and low-memory footprint embedded edge devices. Our quantized model was able to successfully classify all the six VOCs (i.e., Xylene, Hexane, Acetone, Toluene, Methanol, and Butanol) with accuracies of 99.8 and 100% on the validation and test datasets, respectively. More than 50% reduction in on-device RAM and Flash memory usage were measured for the 8-bit quantized model when compared with the equivalently performing 32-bit floating point model for relatively same inference speed on the edge sensing device. The proposed solution paves the way for the mass-scale deployment of such intelligent chemical sensing solutions in the next-generation automated smart factories.

Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition

The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

Wearable system for outdoor air quality monitoring in a WSN with cloud computing: Design, validation and deployment

Breathing poor-quality air is a global threat at the same level as unhealthy diets or tobacco smoking, so the availability of affordable instrument for the measurement of air pollutant levels is highly relevant for human and environmental protection. We developed an air quality monitoring platform that comprises a wearable device embedding low-cost metal oxide semiconductor (MOS) gas sensors, a PM sensor, and a smartphone for collecting the data using Bluetooth Low Energy (BLE) communication. Our own developed app displays information about the air surrounding the user and sends the gathered geolocalized data to a cloud, where the users can map the air quality levels measured in the network. The resulting device is small-sized, light-weighted, compact, and belt-worn, with a user-friendly interface and a low cost. The data collected by the sensor array are validated in two experimental setups, first in laboratory-controlled conditions and then against referential pollutant concentrations measured by standard instruments in an outdoor environment. The performance of our air quality platform was tested in a field testing campaign in Barcelona with six moving devices acting as wireless sensor nodes. Devices were trained by means of machine learning algorithms to differentiate between air quality index (AQI) referential concentration values (97% success in the laboratory, 82.3% success in the field). Humidity correction was applied to all data.

Humidity-Independent, Highly Sensitive and Selective NO2 Sensor Based on In2O3 Nanoflowers Decorated With Graphite Nanoflakes

Metal oxide semiconductor (MOS) gas sensor is an effective tool for NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> detection. However, their performance is seriously influenced by humidity due to water vapor poisoning, and it remains a challenge to develop an anti-humidity NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> MOS sensor. In this work, a humidity-independent, highly sensitive and selective NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensor based on In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> nanoflowers decorated with graphite nanoflakes was fabricated, which exhibited excellent anti-humidity and stability (response fluctuation within 7%) in a wide relative humidity (RH) range of 20% - 90%. The fabricated sensor had a high response of 10 to ppb level NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (RH 80%). Besides, the sensor exhibited good selectivity, low operating temperature (75 °C) and rapid response speed (50 s to 5 ppm NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). The reason for the excellent anti-humidity function was investigated. Large amounts of polar groups were formed on the graphite nanoflakes after hydrothermal treatment, which enhanced the interaction between the graphite and In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> and made the surface of the composites have a strong water absorption function. Moreover, the intrinsic hydrophobic property of the graphite can effectively block the interference of water vapor to the moisture-sensitive In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> . Our work provides a new idea for enhancing the anti-humidity performance of the MOS sensors and can broaden their applications in a variety of complex environments.

Ethanol gas sensing with lower temperature and higher response based on three-dimensional multilevel materials composed of micron hollow carbon spheres@SnO2 nanoparticles

Metal oxide semiconductor gas sensors (MOS) based on tin oxide have excellent performance in ethanol gas detection and have been widely used in industrial production, environmental protection, and road safety detection. However, conventional tin oxide sensors can only show excellent detection performance for ethanol gas above 250 °C. In this work, nano-size tin oxide particles were grown on the micron hollow carbon spheres derived from the biomaterial chlorella by a gel method. This three-dimensional hierarchical porous structure can enrich ethanol gas at a certain temperature. Ethanol gas sensor fabricated with this material has better sensing performance at low temperature (168–200 °C).

Synthesis of Pr-doped ZnO nanospindles by one-pot precipitation as a triethylamine sensor

The development of high performance triethylamine (TEA) sensors for the protection of human safety is essential. Herein, we reported a promising semiconductor metal oxide (SMO) gas sensor based on Pr-doped ZnO nanospindles as the sensing materials for the detection of TEA. The elemental composition and micromorphology of the as-prepared samples were systematically analyzed. The gas sensing performance of the pristine, 1 mmol%, 2 mmol%, and 3 mmol% Pr-ZnO sensor was evaluated. Among them, the 2 mmol% Pr-ZnO sensor exhibited the highest response value for TEA gas, and reached 158.0 towards 50 ppm TEA at 200 °C, which was about 10.0-fold higher than the response value of the pristine ZnO sensor. In addition, the 2 mmol% Pr-ZnO sensor also showed good reproducibility, rapid response/recovery time (10/46 s), theoretical limit of detection (168 ppb), outstanding long-term stability, and excellent selectivity for TEA gas.