Target Gas Concentration Research Articles

Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced Graphene Oxide and Metal Oxide Nanoparticles via Machine Learning.

Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to create wearable devices for detecting hazardous gases surrounding us. However, chemiresistive gas sensors typically suffer from poor selectivity and zero-cross selectivity toward similar types of gases. Herein, a flexible, RT operable chemiresistive gas sensors array is designed, featuring reduced graphene oxide (rGO) and rGO decorated with zinc oxide (ZnO), titanium dioxide (TiO2), and tin dioxide (SnO2) nanoparticles (NPs) on a flexible polyimide (PI) substrate. The sensor array consists of four different sensing layers capable of the selective identification of various hazardous gases such as NO2, NO, and SO2 using machine learning (ML). The gas sensor array exhibits a stable response even when mechanically deformed or exposed to high humidity (up to 60%). Each gas sensor, due to the different metal oxide NPs, shows unique responses in terms of sensitivity, responsiveness, response time, and recovery time to different gases. Consequently, the sensor array generates distinct response patterns that effectively differentiate between the target gases. By leveraging these distinctive recovery patterns and employing a data fusion approach in ML, specific concentrations of target gases can be distinguished. Using ML with fused array sensing data, the training and test accuracies achieved were 98.20 and 97.70%, respectively. This innovative combination of sensor arrays and ML offers significant potential for selective gas detection in environmental monitoring and personal safety applications.

Low-Temperature NO2 Gas-Sensing System Based on Metal-Organic Framework-Derived In2O3 Structures and Advanced Machine Learning Techniques.

In the bustling metropolis of tomorrow, where pollution levels are a constant concern, a team of innovative researchers embarked on a quest to revolutionize air quality monitoring. In pursuit of this objective, this study embarked on the synthesis of indium oxide materials via a straightforward solvothermal method purposely for NO2 detection. Through meticulous analysis of their gas-sensing capabilities, a remarkable discovery came to light. Among the materials tested, In2O3 (IO-2) exhibited exceptional sensitivity toward 100 ppm of NO2 gas at an optimal working temperature of 150 °C and even at room temperature (RT). The response value reached an impressive 12.69, showcasing the material's outstanding capability to detect NO2 gas even at 100 ppb. Further investigation revealed a significant linear relationship (R2 = 0.89454) and commendable reproducibility, highlighting IO-2's potential as a reliable and stable sensing material. Moreover, machine learning techniques were utilized to predict the response characteristics of the sensing materials to various environmental conditions, concentrations of target gases, and operational parameters. This predictive capability can guide the design of more efficient and robust gas sensors, ultimately contributing to improved safety and environmental monitoring. As the demand for efficient, portable, and eco-friendly electronics continues to grow, these findings contribute to the development of sustainable and high-performance materials that can meet the needs of modern technology.

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

Real-Time Monitoring of Toxic Gases using Artificial Neural Networks with SnO2-based Thick Film Gas Sensors

The present study aims to develop an advanced system for real-time identification and measurement of harmful gases using Pd-doped SnO2-based thick film gas sensors integrated with Artificial Neural Networks. These sensors, renowned for their exceptional sensitivity and selectivity, undergo testing in a meticulously controlled gas chamber environment. Here, gas concentrations, temperature, and humidity are meticulously controlled. The gas chamber setup allows for mixing gases like carbon monoxide, nitrogen dioxide, and sulfur dioxide with nitrogen gas to achieve desired concentrations ranging from to . Temperature is kept at and relative humidity is maintained between to . The sensitivity analysis of the sensor demonstrates its adeptness in detecting low concentrations of target gases, with sensitivity increasing as gas concentrations rise, also the selectivity assessments highlight their ability to accurately differentiate between target gases and common interferents, ensuring precise detection even in complex gas mixtures. Response time testing indicates rapid detection capabilities, crucial and useful for emergencies. The Artificial Neural Network model, trained via the backpropagation algorithm, demonstrates remarkable accuracy, precision, recall, and F1 scores in predicting gas concentrations. The findings indicate that this integrated system offers a reliable and efficient solution for toxic gas detection, with potential applications in industrial safety and environmental monitoring.

Pre-pulse-driven temperature modulation based on semiconductor gas sensor: A low-temperature strategy for gas detection

Semiconductor gas sensor has the advantages of low cost, simple detection principle and fast detection speed, so it has been widely used in gas detection in industrial processes. Sensors are usually maintained at high operating temperatures to maintain high performance, which is not only high power consumption but also the risk of explosion when the target gas concentration is too high. In this paper, the method of pre-pulse-driven temperature modulation is proposed to solve this problem. The sensor structure with ceramic-based micro-hot plate substrate has faster temperature change speed, which lays the foundation for high response. This method keeps the sensor operating at low temperature and obtains high response when it is in contact with the target gas. In addition, it is accompanied by the reduction of power consumption and the improvement of response speed. In this paper, SnO2 sensor is used to verify the effectiveness of the pre-pulse-drivn temperature modulation strategy, and then In2O3 and ZnO sensors are used to verify its universality. Finally, the effect of oxygen accumulation on the sensitization of pre-pulse-driven temperature modulation method is analyzed. It has been verified that pre-pulse-driven is an effective method to reduce temperature and improve sensitivity, which is worthy of promotion and further optimization.

UV-activated semiconductor gas sensor response measurement for formaldehyde detection

The gas sensing characteristics of ultraviolet light-emitting diodes (UV-LEDs)-activated semiconductor sensors were investigated to detect formaldehyde (HCHO) as a model volatile organic compound (VOC). As the major drawback of formaldehyde gas sensors is the low durability of the sensing material, efforts were made to establish a suitable metal oxide semiconductor and to enhance its stability throughout the sensing process. In2O3 nanoparticles (NPs) as the sensing material showed restructuring caused by exposure to different concentrations of formaldehyde in the presence of UV radiation when its morphology and structure were analyzed. UV radiation in the presence of formaldehyde was found to play a critical role in nanoparticles agglomeration, as well as its response inconsistency and instability.The novel method of measuring UV radiation elimination in the initial stages of the response curve was deemed effective for sensing layer preservation in this study. The scanning electron microscopy (SEM) results showed that this approach prevented any notable deformation in the nanoparticles’ shape and size and resulted in reproducible responses to various formaldehyde concentrations (10–50 ppm) in the steady-state and transient (5 min exposure) phases of the response curve. Hence, a short-time UV exposure has the potential to be applied in practical gas sensing applications in which the sensor can be automatically turned off a few minutes after any target gas detection. The gas sensor response slope in the transient phase of the response curve was found to be a very good indicator of target gas concentration and to obtain fast and consistent results while preserving the sensing material from restructuring. The findings of this study can be incorporated into the development of UV-LED-activated gas sensors where stable and fast response measurements of VOCs are required.

Cu-Pt/CrN Fuel Cell Gas Sensor Achieves ppb-Level H2S Detection at Room Temperature.

Fuel cell gas sensors have emerged as promising advanced sensing devices owing to their advantageous features of low power consumption and cost-effectiveness. However, commercially available Pt/C electrodes pose significant challenges in terms of stability and accurate detection of low concentrations of target gases. Here, we introduce an efficient Cu-Pt/CrN-based fuel cell gas sensor, designed specifically for the ultrasensitive detection of hydrogen sulfide (H2S) at room temperature. Compared to the commercial Pt/C sensor, the Cu-Pt/CrN sensor exhibits excellent sensitivity (0.26 μA/ppm), with an increase in the selectivity by a factor of 2.5, and demonstrates good stability over a 2 month period. The enhanced sensing performance can be attributed to the modulation of the electronic arrangement of Pt by Cu, resulting in an augmentation of H2S adsorption. The Cu-Pt/CrN fuel cell gas sensor provides an opportunity for detecting parts per billion-level H2S in various applications.

In situ synthesis of long tubular water-dispersible polyaniline with core/shell gold and silver@graphene oxide nanoparticles for gas sensor application

Gold nanoparticles (Au NPs) with graphene oxide (GO) shell (Au@GO), silver nanoparticles (Ag NPs) with GO shell (Ag@GO), and gold silver nanoparticles (AuAgNPs) with GO shell (AuAg@GO) were synthesized employing a cationic surfactant. The prepared core@shell structures were used for in situ synthesis of long tubular polyaniline structures employing cetyl trimethyl ammonium bromide (CTAB) as a soft template. This process led to a notable enhancement in the tubular nanostructure of PANI, extending its length beyond 10 μm, in the case of using core/shell Au@GO, Ag@GO, and AuAg@GO structures. To evaluate their applicability and compatibility, the dispersibility of these nanocomposites was assessed in three distinct solvents: water, dimethyl sulfoxide (DMSO), and N-Methyl-2-pyrrolidone (NMP). Subsequently, the dedoping of PANI within the prepared nanocomposites was scrutinized using UV–Visible (UV–Vis) spectroscopy, which revealed a reduction in the I750/I315 ratio from 1.00 to 0.66 when subjected to water and NMP solvents, respectively. Notably, the dedoping of the AuAg@GO/PANI nanocomposite was predominantly observed in NMP, attributable to the presence of hydrogen bonding interactions and the basic properties of NMP. In terms of ionic conductivity, it was observed that the prepared nanocomposite exhibited its highest conductivity in a water-based medium, registering at 1982 μs. Furthermore, the AuAg@GO/PANI nanocomposite exhibited superior sensing capabilities in comparison to PANI-based gas sensor devices, particularly when exposed to acetone, CO2, NO2, and H2S. Remarkably, at room temperature (25 °C), the AuAg@GO/PANI nanocomposite displayed rapid response and recovery times, with values of 279 s, 431 s, 335 s, and 509 s for 1 ppm concentrations of CO2, NO2, H2S, and acetone, respectively. The sensitivity of these sensors towards acetone, CO2, NO2, and H2S, was quantified by analyzing the slope of the response versus the target gas concentration, revealing the AuAg@GO/PANI nanocomposite to exhibit the highest sensitivity, particularly towards NO2.

Simultaneous Detection of CO and NO2 Gases using Interaction Analysis of SnS2 Sensor Array Response

Developing a multi-analyte gas sensing system that simultaneously detects trace levels of CO and NO2 at low temperatures is necessary for the Internet of Things (IoT) based air quality monitoring applications. Nevertheless, gas sensors operating at low temperatures are nonspecific and rarely detect target gases at lower ppb levels in the air. Herein, an array of two SnS2 sensors with different bias voltages has been developed and characterized upon exposure to individual and binary mixtures of CO and NO2 gases at different concentrations. The developed gas sensors array achieved the lower detection limit of 45 ppb for NO2 and 150 ppb for CO. Further, co-adsorption-induced interaction analysis was carried out to predict the target gas concentration in the binary mixture using the mixed gas response. The mean absolute percentage error of 7.86% is observed in predicting the target gas concentrations in the binary mixture, which indicates the high prediction accuracy of proposed method. As a minimal resource intensive approach, the proposed method can be used in air quality monitoring applications that require low-power and low-cost sensors.

Multivariate analysis and digital twin modelling: Alternative approaches to evaluate molecular relaxation in photoacoustic spectroscopy

A comparative analysis of two different approaches developed to deal with molecular relaxation in photoacoustic spectroscopy is here reported. The first method employs a statistical analysis based on partial least squares regression, while the second method relies on the development of a digital twin of the photoacoustic sensor based on the theoretical modelling of the occurring relaxations. Methane detection within a gas matrix of synthetic air with variable humidity level is selected as case study. An interband cascade laser emitting at 3.345 µm is used to target methane absorption features. Two methane concentration ranges are explored targeting different absorptions, one in the order of part-per-million and one in the order of percent, while water vapor absolute concentration was varied from 0.3 % up to 2 %. The results achieved employing the detection techniques demonstrated the possibility to efficiently retrieve the target gas concentrations with accuracy > 95 % even in the case of strong influence of relaxation effects.

Devising technological solutions for gas sensors based on zinc oxide for use at critical infrastructure facilities

This paper reports a study of a gas sensor based on nanostructured zinc oxide in order to establish the conditions for its production and operating characteristics under the influence of the target gas ethanol. The studied samples were produced by magnetron sputtering on direct current. The method of forming the device structure was chosen among others due to the fact that it has a high rate of deposition at low values of the working gas pressure, there is no overheating of the substrate, a low degree of contamination of the obtained films, the possibility of obtaining samples of uniform thickness on a large area of the substrate. A VUP-5M vacuum unit with an original material-saving magnetron was used to obtain the films. Studies of the effect of temperature on the resistance of a gas sensor based on ZnO have been carried out. It was established that the change in the resistance of the tested sample depends on the temperature of the substrate. The resistance of the gas sensor in atmospheric air decreases with increasing substrate temperature from room temperature (25 °C) to 200 °C. A further increase in temperature from 200 °C leads to an increase in the resistance of the structure until it stabilizes in the temperature range of 300–400 °C. It was established that the operating temperature range of the gas sensor based on ZnO is within 300–400 °C. The characteristics of the gas sensor based on ZnO were studied and the working temperature of the sensor was determined for the rapid identification of ethanol in atmospheric air at a target gas concentration of 500 ppm. It was established that for rapid operation of the instrument structure, the temperature of the substrate should be 400 °C, a decrease or increase in temperature leads to a decrease in the sensitivity of the sensor to the target gas. It was established that the gas sensor demonstrates stability and a consistent sensitivity response upon repeated exposure to the target gas

Suppress ambient temperature interference strategy based on SnO2 gas semiconductor sensor using dynamic temperature modulation mode and principal component analysis algorithm

Semiconductor gas sensors have been widely used in environmental monitoring, toxic gas monitoring, respiratory marker monitoring and other fields due to their low price and high sensitivity etc. An easily overlooked parameter, ambient temperature, is a major factor affecting the accuracy of the sensor. In this paper, a new idea to suppress ambient temperature interference is proposed. Dynamic temperature modulation method is used to extend the dimension of output information, so that both ambient temperature and target gas concentration can be expressed simultaneously. The ambient temperature component is separated by coordinate transformation and component elimination. The data inverse transformation is carried out to achieve the purpose of suppressing the ambient temperature interference. Compared with the performance optimization of gas-sensing materials and additional temperature sensors, the first advantage of this method is to reduce the complexity and power consumption of sensor devices. In addition, the use of PCA algorithm also reduces the complexity of the recognition algorithm. At the same time, more intuitive data reduces the cost of parameter optimization.

Optimization of the Technology of Creating Sensitive Gas Sensors Based on Zinc Oxide

The main achievements in the development of resistive type gas sensors are analyzed, in particular, the creation of nanostructures based on metal oxides, which make it possible to significantly improve the performance characteristics of the sensors. Experimental samples of the gas sensor based on ZnO were obtained by magnetron sputtering on direct current. The effectiveness of the gas sensor system for recognition and analysis of gases and their mixtures has been established. A study of the sensitivity of experimental samples to the influence of the target gas CO was carried out. The target gas concentration varied from 50 to 150 ppm. It was established that the ZnO-based gas sensor exhibits the highest sensitivity at a target gas concentration of 100 ppm. The sensitivity of the gas sensor increases with increasing exposure time to the target gas.

Reconfigurable Manipulation of Oxygen Content on Metal Oxide Surfaces and Applications to Gas Sensing.

Oxygen vacancies and adsorbed oxygen species on metal oxide surfaces play important roles in various fields. However, existing methods for manipulating surface oxygen require severe settings and are ineffective for repetitive manipulation. We present a method to manipulate the amount of surface oxygen by modifying the oxygen adsorption energy by electrically controlling the electron concentration of the metal oxide. The surface oxygen control ability of the method is verified using first-principles calculations based on density functional theory (DFT), X-ray photoelectron spectroscopy (XPS), and electrical resistance analysis. The presented method is implemented by fabricating oxide thin film transistors with embedded microheaters. The method can reconfigure the oxygen vacancies on the In2O3, SnO2, and IGZO surfaces so that specific chemisorption dominates. The method can selectively increase oxidizing (e.g., NO and NO) and reducing gas (e.g., H2S, NH3, and CO) reactions by electrically controlling the metal oxide surface to be oxygen vacancy-rich or adsorbed oxygen species-rich. The proposed method is applied to gas sensors and overcomes their existing limitations. The method makes the sensor insensitive to one gas (e.g., H2S) in mixed-gas environments (e.g., NO2+H2S) and provides a linear response (R2 = 0.998) to the target gas (e.g., NO2) concentration within 3 s. We believe that the proposed method is applicable to applications utilizing metal oxide surfaces.

Hollow-waveguide-based light-induced thermoelastic spectroscopy sensing.

In this Letter, a hollow waveguide (HWG)-based light-induced thermoelastic spectroscopy (LITES) gas sensing is proposed. An HWG with a length of 65 cm and inner diameter of 4 mm was used as the light transmission medium and gas chamber. The inner wall of the HWG was coated with a silver (Ag) film to improve reflectivity. Compared with the usually used multi-pass cell (MPC), the HWG has many advantages, such as small size, simple structure and fast filling. Compared with a hollow-core anti-resonant fiber (HC-ARF), the HWG has the merits of easy optical coupling, high system stability, and wide transmission range. A diode laser with output wavelength of 1.53 µm and a quantum cascade laser (QCL) with output wavelength of 4.58 µm were selected as the sources of excitation to target acetylene (C2H2) and carbon monoxide (CO), respectively, to verify the performance of the HWG-based LITES sensor in the near-infrared and mid-infrared regions. The experimental results showed that the HWG-based LITES sensor had a great linear responsiveness to the target gas concentration. The minimum detection limit (MDL) for C2H2 and CO was 6.07 ppm and 98.66 ppb, respectively.

Improving the manufacturing technology of sensing gas sensors based on zinc oxide by using the method of magnetron sputtering on direct current

A gas sensor based on zinc oxide obtained by magnetron spraying at direct current was investigated. There are methods of deposition of zinc oxide nanostructures such as thermal evaporation, chemical vapor deposition, organometallic chemical vapor deposition, magnetron sputtering, pulsed laser deposition, and hydrothermal process. The least investigated is magnetron sputtering. To obtain films, a vacuum unit VUP-5M with an original material-saving magnetron was used. Studies into the sensitivity and speed of the gas sensor based on ZnO with respect to the target gas – ethanol of different concentrations – were carried out. The resulting experimental dependences of the sensitivity of the gas sensor on the concentration of the target gas demonstrate that with increasing concentration of the target gas, the resistance decreased while the sensitivity of the sample increased. It was established that the change in the resistance of the test sample is proportional to the change in the concentration of the target gas. After the sensor surface becomes saturated with adsorbed molecules, the resistance no longer decreases, even if the gas concentration continues to increase. The reaction of the gas sensor to the target gas – ethanol – at concentrations above 150 ppm was almost absent. The time required to achieve the maximum response value should be lower at higher target gas concentrations. Sensitivity reaction repeatability studies were conducted to measure the resistance of a gas sensor based on ZnO in a target gas atmosphere with a concentration of 150 ppm. It was found that the gas sensor demonstrates excellent stability and consistent sensitivity reaction when re-exposed to the target gas – ethanol. It was established that the reaction time of a gas sensor based on ZnO to the target gas at each repeated exposure does not exceed 10 s. This repeatability index allows us to assert the stability of the ZnO-based gas sensor in an ethanol atmosphere under standard conditions

Gas-triggered resistive switching and chemiresistive gas sensor with intrinsic memristive memory

Combining gas sensing and resistive switching within a single device allows for its utilization as a gas sensor with built-in memory, capable of remembering a temporal change of the target gas concentration. In this work, coexistence of the hydrogen gas sensing and resistive switching in a capacitor-like Pt/TiO2/Pt structure is examined in detail. In particular, influence of the forming process on the sensor’s response to hydrogen and influence of the hydrogen concentration on the resistive switching properties are studied. An equivalent device model based on the obtained data is proposed. The built-in memory operation, i.e. triggering the resistive switch by a change of the hydrogen gas concentration, is demonstrated for various devices at various temperatures and various humidity levels. It is shown that the response magnitude in the high resistance state (HRS) and variance of the voltage which triggers the set process are critical parameters for a reliable device operation.

Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

Hybrid Simulated Annealing Particle Swarm Optimization Support Vector Machine Based Temperature-Pressure Error Compensation Approach for TDLAS Gas Detection

ABSTRACT The target gas concentration in TDLAS (Tunable Diode Laser Absorption Spectroscopy) gas detection is nonlinearly related to the photodetector output signal, which is effortlessly disturbed by the temperature and pressure inside the gas absorption cell, and it will decrease the detection accuracy of the system. This paper proposes an SA-PSO-SVM (Simulate Anneal-Particle Swarm Optimization-Support Vector Machine) based temperature-pressure error compensation approach to improve the accuracy of TDLAS gas detection. First, an experimental system concerning the effects of gas temperature-pressure on the absorption line distortion was built. Meanwhile, a method for processing and exploring normalized data about the temperature and pressure effects on the gas concentration was proposed. Second, an SA-PSO-SVM-based model of temperature-pressure compensation for TDLAS gas detection was constructed, and a multi-point correction was performed. Finally, the developed model was compared with traditional BP (Back Propagation), SVM, and PSO-SVM-based temperature-pressure compensation models for TDLAS gas detection, to verify the effectiveness in terms of mean absolute error, mean relative error, and mean square error, which could significantly improve the detection accuracy of TDLAS system. The results provide guidance and lay a technical foundation for the study of multi-component gas cross-aliasing absorption line separation.

Ammonia Sensing Properties of SnS₂/SnO₂ Nanocomposite-Based Gas Sensor by SnS₂ Annealing

Tin disulfide has been demonstrated effective for ammonia detection, while its potential application has been seriously limited by high working temperature and low sensitivity. In this article, SnS2/SnO2 nanocomposites are prepared through a two-step method. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analysis show that the material would be transformed from SnS2 to SnS2/SnO2 composites by increasing annealing temperature. Gas testing results have shown that SnS2/SnO2 nanocomposites exhibit high sensitivity and good stability to ammonia, and the response to 100-ppm ammonia at 80 °C can reach 5.91. The sensor response was relatively stable at the target gas concentration. The high response of SnS2/SnO2 composites to ammonia at low temperatures might be due to SnS2 surface defects and heterostructure. From the perspective of atom and energy, adsorption energy has been simulated and calculated by the first principle analysis, to some extent, which proved that SnS2/SnO2 nanocomposite can improve the response to ammonia gas.