Target Gas Research Articles

Insights into Target Gas-Oxygen Interactions in Highly Sensitive Gas Sensors Using Data-Driven Methods.

In the gas-sensing mechanism of a metal-oxide-semiconductor (n-type) gas sensor, oxygen adsorption or desorption on the oxide surface leads to an increase or decrease in the resistance of the gas sensor system. Additionally, oxygen can be adsorbed again at the location where initially adsorbed oxygen reacted with the target gas. Thus, the adsorption-desorption equilibrium of the reducing gas on the oxide surface is a significant factor in determining the sensitivity and reaction rate. In particular, for ultralow-concentration gas measurements, the relative concentration of oxygen was very high. To design an ultrasensitive gas sensor, not only the reaction of the target gas but also the competing reaction between the target gas and oxygen must be considered. Although qualitative investigations of these complex relationships have been performed according to the gas concentration and flow rate, reliable quantitative results are limited. In this study, a quantitative approach was used to understand the correlation between oxygen and a target gas by applying data analysis methods. We investigated the behavior of oxygen and the target molecules depending on the gas concentration and flow rate using the parts per billion level of the acetone gas sensor. Initial response data according to various detection conditions were processed using principal component analysis and K-means clustering; as a result, four types of reaction behaviors were inferred for 15 types of reaction conditions. Furthermore, the response time, depending on the detection conditions, can be distinguished using the suggested categorization. Our investigation suggests a possibility beyond simple optimization through the data analysis of the gas-sensing results.

Pulse-Driven MEMS NO2 Sensors Based on Hierarchical In2O3 Nanostructures for Sensitive and Ultra-Low Power Detection

As the mainstream type of gas sensors, metal oxide semiconductor (MOS) gas sensors have garnered widespread attention due to their high sensitivity, fast response time, broad detection spectrum, long lifetime, low cost, and simple structure. However, the high power consumption due to the high operating temperature limits its application in some application scenarios such as mobile and wearable devices. At the same time, highly sensitive and low-power gas sensors are becoming more necessary and indispensable in response to the growth of the environmental problems and development of miniaturized sensing technologies. In this work, hierarchical indium oxide (In2O3) sensing materials were designed and the pulse-driven microelectromechanical system (MEMS) gas sensors were also fabricated. The hierarchical In2O3 assembled with the mass of nanosheets possess abundant accessible active sites. In addition, compared with the traditional direct current (DC) heating mode, the pulse-driven MEMS sensor appears to have the higher sensitivity for the detection of low-concentrations of nitrogen dioxide (NO2). The limit of detection (LOD) is as low as 100 ppb. It is worth mentioning that the average power consumption of the sensor is as low as 0.075 mW which is one three-hundredth of that in the DC heating mode. The enhanced sensing performances are attributed to loose and porous structures and the reducing desorption of the target gas driven by pulse heating. The combination of morphology design and pulse-driven strategy makes the MEMS sensors highly attractive for portable equipment and wearable devices.

Indoor Air Quality Monitoring System with High Accuracy of Gas Classification and Concentration Prediction via Selective Mechanism Research.

The efficacy of sensors, particularly sensor arrays, lies in their selectivity. However, research on selectivity remains notably obscure and scarce. In this work, indoor pollutants (C7H8, HCHO, CH4, and NO2) were chosen as the target gas. Following the screening of six oxides from previous work, temperature-programmed desorption/reduction experiments were conducted to delve into the origins of selectivity. The results explicate the superiority of NiO in detecting toluene and unveil the distinctive NO2 sensing mechanism of WO3 sensors. Based on the sensor array comprising these oxides, it can clearly detect low concentrations of C7H8 (S = 1.6 to 50 ppb), HCHO (S = 1.4 to 50 ppb), and NO2 (S = 3.3 to 50 ppb), which satisfies the requisites of indoor air monitoring. Meanwhile, three machine learning models (Extreme Gradient Boosting, Support Vector Machine, and Back Propagation Neural Network) are employed for gas classification. The classification accuracies of these models are 95.45%, 100%, and 100%, while the R2 values of the concentration prediction are 99.65%, 94.9%, and 98.04%, respectively, indicating the rationality of material selection. Furthermore, it can still achieve relatively high accuracy in gas classification (94.12%) and concentration prediction (89.36%), even for gas mixtures of four gases. Finally, an indoor air quality monitoring system is developed, which enables real-time monitoring of indoor gas quality through the Internet of Things.

Pd-W18O49 Nanowire MEMS Gas Sensor for Ultraselective Dual Detection of Hydrogen and Ammonia.

Demand for real-time detection of hydrogen and ammonia, clean energy carriers, in a sensitive and selective manner, is growing rapidly for energy, industrial, and medical applications. Nevertheless, their selective detection still remains a challenge and requires the utilization of diverse sensors, hampering the miniaturization of sensor modules. Herein, a practical approach via material design and facile temperature modulation for dual selectivity is proposed. A Pd nanoparticles-decorated W18O49 nanowire gas sensor is prepared for dual detection of hydrogen and ammonia. The sensor exhibits distinct operating temperatures for ultraselective detection of hydrogen (125°C) and ammonia (225°C), with high responses of 35.3 and 133.8, respectively. This dual selectivity with high sensitivity is attributed to enhanced oxygen adsorption, the chemical affinity of sensing materials for target gases, and distinct reactivity profiles of gases. The proposed sensor is further integrated into a microelectromechanical system, enabling its small size, low power consumption, and rapid temperature modulation. Moreover, the practical feasibility of this sensor platform for smart energy monitoring systems is demonstrated by assessing its sensing properties in electrochemical ammonia oxidation reaction systems. This work can provide a practical approach for developing a single gas sensor with multiple functionalities for application in electronic nose systems.

Dual-channel infrared OPO lidar optical system for remote sensing of greenhouse gases in the atmosphere: Design and characteristics

Current global warming and climate change under the greenhouse effect call for a thorough understanding of the spatial distribution of greenhouse gases in different atmospheric layers. Lidar systems are the most effective for remote greenhouse gas monitoring. We design a two-channel infrared OPO lidar optical system for remote DIAL/DOAS sensing of carbon dioxide and water vapor in the atmosphere. In this work, optimal geometric parameters of the transceiving channel of the lidar optical system are chosen, a need to focus laser radiation at a distance of 1 km from an observation point is demonstrated, and numerical simulations confirm the possibility of detecting lidar signals ranging from 10−7 to 10−10 W in the informative spectral range 4800–5100 cm−1 (1960–2083 nm). Laboratory experiments with the main components of the lidar system with experimentally confirmed parameters, which simulate atmospheric measurements of CO2 absorption at a calibrated sensing wavelength of 2005 nm (4987 cm−1) (pressure of 1 atm; CO2 concentration corresponding to the midlatitude summer background atmosphere) in the informative spectral range of the lidar system, enable selecting a pair of wavelengths with resonant absorption of the target gas near 2005 nm to study the background state of the atmosphere in the surface layer. Efficiency of the lidar optical system is confirmed by in situ test experiments, where backscattering signals from a topographic target with an albedo of ∼0.15 spaced 168 m apart from an observer are recorded at 60 mV when operating along a horizontal atmospheric path. The lidar system we design can be used in measuring complexes at carbon test sites. It can also be used for atmospheric monitoring in industrial centers, at background measuring stations, and in swamp areas.

Effects of Al doping on structural, optical, morphological, and low-concentration CO2 gas sensing properties of ZnO nanoparticles synthesized by sol-gel method

Abstract This work examines the morphological, structural, optical, and gas-sensing characteristics of ZnO thin films doped with Al that were created by the sol-gel spin coating technique. The thin films, doped with varying aluminum concentrations (0%, 2%, and 5%), were characterized using XRD, UV-visible spectroscopy, and FE-SEM to assess their crystallinity, band gap, and surface morphology. XRD analysis confirmed the incorporation of Al into the ZnO lattice without forming secondary phases, while UV-visible spectroscopy revealed an increase in transmittance and band gap with higher Al doping. FE-SEM images showed a transition from agglomerated grains to smoother surfaces with increased Al content. Gas sensing performance was evaluated using low-concentration CO2 as the target gas. The results demonstrated that Al doping significantly enhances the CO2 sensing response, with the 5% Al-doped ZnO exhibiting the optimal sensitivity due to increased carrier concentration and improved surface interaction. These findings suggest that Al-doped ZnO thin films are promising candidates for efficient CO2 gas sensors, combining enhanced structural and optical properties with superior gas sensing capabilities.

VOC Detection with Zinc Oxide Gas Sensors: A Review of Fabrication, Performance, and Emerging Applications

Energy‐efficient, high‐specificity gas sensors provide practical suitability for stability and response factors. The recognition of ignitable gases (methane (CH <sub>4</sub>), propane (C <sub>3</sub>H <sub>8</sub>), and hydrogen (H <sub>2</sub>) and harmful gases (carbon oxide (CO) and hydrogen sulfide (H <sub>2</sub>S)) in an enclosed and out‐of‐door space are essential to safeguard the human lives and infrastructural spaces. One of the crucial conductive‐type metal oxide semiconductor (MOS) gas sensors yielding wide applications is zinc oxide (ZnO). This study highlights the various types of ZnO gas sensors, their fabrication techniques, and specific vital characterizations. The devices based on MOS are utilized to sense various target gases through redox reactions. The variation in oxide surface with target gas interactions is transduced to a change of sensor conductance. This review also provides insight into integrating ZnO gas sensors with technologies such as materials engineering, the Internet of things and big data. Moreover, this review addresses ZnO gas sensors' challenges and future directions.

Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms

Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas.

Strong Metal–Support Interaction Induces Dual Reaction Sites for Ultrasensitive and Stable NO2 Detection in Extreme Environments

AbstractThe tripartite combination of a high density and stability of surface reaction sites, complemented by the longevity and efficient transfer of interface carriers, along with the effective adsorption and activation of target gas molecules, jointly determines the efficiency of chemiresistors. In this work, the strong metal–support interaction (SMSI) of Pt─Ce3+ is formed by the NaBH4 reduction method and thus prepares metal‐dynamically stable PtNR─CeO2 with Pt─Ce3+ and Pt─Pt reaction sites. The formation of SMSI induces the recombination of the chemical structure of the CeO2 surface causing the localization of electron‐rich Ce3+, which greatly increases the charge concentration at the interface. The experimental findings of the strong interaction of Pt─Ce3+ bonding and the catalytic effect of Pt optimized the adsorption mode of PtNR─CeO2 on target gas, which maintains stable catalytic activity at 20–500 °C and achieving a notable detection response value of 1.43 for 20 ppb NO2. This work offers fresh insights into designing stable oxide chemiresistors with efficiency and stability by customizing reaction sites at the atomic level.

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.

High-sensitivity multi-gas detection using dual-ridge metasurface emitters with polarization-distinguishable emission spectra

The metasurface thermal emitter offers an energy-efficient, compact, and sensitive solution as a radiation source for non-contact gas detection, enabling the “molecular fingerprint” technique to be widely applied, from medical diagnostics to environmental monitoring. However, most narrowband emitters are designed for a single target gas, hindering the miniaturization of multi-gas detection systems. In this work, a one-dimensional dual-ridge grating emitter is employed, achieving dual-band and tri-band polarization-distinguishable emission spectra through the excitation of Fabry-Perot (FP) resonances and quasi-bound states in the continuum (qBICs). These emission spectra can be readily matched to multiple non-overlapping absorption peaks of gases such as CH4, CO2, CO, NO, and NH3 within the 3–6 µm range, thereby reducing the impact of mixed gases on measurements. Compared to conventional metal-dielectric-metal structures, the use of a single metal layer results in lower material losses, enabling higher Q-factors and more pronounced directional radiation intensity variations. Furthermore, adjusting the asymmetry to modulate the qBIC-excited absorption peaks does not affect the Q-factor of the FP resonance absorption, thus achieving high-sensitivity multi-band gas detection. This work provides a promising approach for the miniaturization and integration of multi-gas channel detection, facilitating more accurate and sensitive sensing strategies.

Rational MOF Membrane Design for Gas Detection in Complex Environments.

Metal-organic frameworks (MOFs) hold significant promise in the realm of gas sensing. However, current understanding of their sensing mechanisms remains limited. Furthermore, the large-scale fabrication of MOFs is hampered by their inadequate mechanical properties. These two challenges contribute to the sluggish development of MOF-based gas-sensing materials. In this review, the selection of metal ions and organic ligands for designing MOFs is first presented, deepening the understanding of the interactions between different metal ions/organic ligands and target gases. Subsequently, the typical interfacial synthesis strategies (gas-solid, gas-liquid, solid-liquid interfaces) are provided, highlighting the potential for constructing MOF membranes on superhydrophobic and/or superhydrophilic substrates. Then, a multi-scale structure design strategies is proposed, including multi-dimensional membrane design and heterogeneous membrane design, to improve sensing performance through enhanced interfacial mass transfer and specific gas sieving. This strategy is anticipated to augment the task-specific capabilities of MOF-based materials in complex environments. Finally, several key future research directions are outlined with the aim not only to further investigate the underlying sensing principles of MOF membranes but also to achieve efficient detection of target gases amidst interfering gases and elevated moisture levels.

Ultrasensitive detection of hazardous gas at room temperature enabled by MOF@MXene 0D-2D heterostructure

Achieving high sensitivity in detecting trace concentrations of toxic gases, particularly under room temperature (RT) conditions, remains a significant challenge. Herein, a 0D-2D heterostructure that can detect ppb-level H2S at RT is proposed by self-assembling cobalt-based metal–organic framework (Co-MOF) on Ti3C2Tx MXene. Co-MOFs with high specific surface areas can capture and concentrate target gas molecules, enhancing host-guest interactions and thereby boosting the selectivity and sensitivity. MXene nanosheets with high conductivity enable rapid electron transport at heterointerface, hence efficiently accelerating the reaction kinetics. Thereby, the as-prepared chemiresistive gas sensor based on Co-MOF@MXene 0D–2D heterostructure possessed excellent sensitivity against interfering gases and delivered an excellent response value of 11.1 to 400 ppb H2S at RT. The judicious design of MOF@MXene heterostructure may spur advanced hybrid material systems for superior sensing applications.

Single walled carbon nanotubes/ZnO nanowires heterostructure array for NO2 gas sensing at low ppm levels

A layer-by-layer ZnO nanowire (NW)-Single Walled Carbon Nanotube (CNT) hybrid gas sensor has been proposed for effective detection of NO2 gas under a low exposure limit of 5 ppm. The fabricated hybrid sensor displayed good sensor response (S %), good selectivity and fast response time towards the target gas. S % value of 36.4 % under 5 ppm NO2 level at 250 °C and good selectivity response towards different interfering gases like SO2, CO and H2 have been observed. Meanwhile, fast response time values of 44.08 s (response) and 62.11 s (recovery) were observed at 300 °C under 5 ppm NO2 level. The sensor baseline resistance of the hybrid heterostructure (HS) was also found to be lower when compared to that of a bare ZnO NW sensor. In addition, the presence of Single Walled CNT in the HS was also shown to reduce the operational temperature of the sensor. Temperature played a significant role by controlling the adsorption and desorption kinetics of gas molecules on the surface. Moreover, the formation of p-n interface between Single Walled CNT (p-type) and ZnO NW (n-type) might have modulated the potential charge barrier on interacting with the adsorbed gases which inturn might have impacted the response of the hybrid sensor. In addition, the large surface areas offered by both NWs and nanotubes might have improved the gas adsorption capability thereby enhancing the overall hybrid sensor’s response.

Gas classification and concentration prediction in open environments using class anchor clustering-initialized temporal convolutional network

Identifying target gases and predicting their concentrations in open environments are critical tasks, particularly due to fluctuating environmental conditions and the presence of unknown gases. This study aims to address these challenges by developing a model that improves both gas classification and concentration prediction. We introduce two key components: Quantile-Dynamic Associated Features (Q-DAF) and the Class Anchor Clustering-Initialized Temporal Convolutional Network (CAC-InitTCN). Q-DAF dynamically enhances gas signal feature extraction, optimizing the ability of CAC-InitTCN to handle complex and changing gas environments. CAC-InitTCN is designed to efficiently process time-series data, allowing it to manage gas concentration fluctuations while mitigating the interference of unknown gases. CAC-InitTCN was evaluated on two distinct datasets, achieving classification accuracy rates of 93.62 % and 98.05 %, respectively. In gas concentration prediction tasks, the model demonstrated RMSE values of 0.2102 and 0.0392, and R2 scores of 0.9085 and 0.9784, after data normalization. These results demonstrate the capability of CAC-InitTCN in handling real-time gas fluctuations and recognizing unknown gases, making it a promising approach for open-environment gas detection.

Rapid and high-accuracy concentration prediction of gas mixtures based on PMH-TCN

In industrial environments, the flammable gas ethylene (C2H4) and toxic gas carbon monoxide (CO) often coexist. To ensure worker safety, it is essential to predict these gas concentrations quickly and accurately. Many studies lack prediction accuracy or speed due to insufficient time feature extraction and loss of critical features. In this work, a novel approach called the Parametric Rectified Linear Unit (PReLU)-based Multi-Head Attention Temporal Convolutional Network (PMH-TCN) model was devised to efficiently and precisely predict gas concentrations in mixtures. The model is capable of accurately predicting the gas concentration within 5 s after the introduction of the target gas. In the 5-fold cross-validation, the values of Adjusted R-Square (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the PMH-TCN model reached 0.9850, 0.0448, and 0.0651 respectively. It was shown that the PMH-TCN model offered a very efficient method for the quick identification of electronic noses.

Adsorption and gas sensing properties of Cun, Agn and Rhn (n=1–3) clusters doped with WTe2 for transformer winding deformation fault gases

In order to solve the problem that the initial WTe2 has poor adsorption effect on transformer winding deformation fault gas, this paper uses density functional theory (DFT) to study the adsorption behavior of three target gases (CO, C2H2 and CO2) on the surface of WTe2 substrate doped with three metal clusters: Cun, Agn, and Rhn (n=1–3). By analyzing the adsorption distance, adsorption energy, DCD and DOS of the three systems, the effect of metal cluster doping on the adsorption of fault gas by WTe2 was revealed. The results show that metal cluster doping can effectively improve the conductivity of WTe2 substrate and make its band structure more compact. CO and C2H2 have good adsorption characteristics in the three doping systems, while CO2 can be effectively adsorbed by Rh cluster-doped WTe2. In addition, this article also uses work function and band gap to analyze the effect of gas adsorption on substrate conductivity, and discusses the practical application prospects of the three metal cluster doping systems from the perspectives of recovery time and sensitivity, and achieved good results. This paper provides theoretical guidance for exploring the application prospects of WTe2 substrate in transformer winding deformation fault diagnosis and gas detection.

Gas sensitivity evaluation of decomposition gases in environmentally friendly insulating devices (C4F7N/CO2) (Chromium cluster-modified BNNTs surface interface at the atomic scale)

Under the global trend of green development, the research and development of environmentally friendly gas-insulated electrical equipment and the associated technical issues in the power systems industry have become important research topics. This study investigates the monitoring technology for decomposition gases (CO, C2F6, and COF2) under insulation defect conditions in environmentally friendly GIS equipment based on the green insulating gas mixture C4F7N/CO2. Initially, chromium clusters (Crn, n = 1–4) were used to modify one-dimensional boron nitride nanotubes (BNNTs), describing the changes in semiconductor electronic properties and magnetism at the microscopic interface. Interestingly, the electronic properties of the system changed significantly after modification, with increased electron mobility and enhanced conductivity. Subsequently, adsorption tests were conducted on three potential target gases, revealing that Cr-BNNT exhibited excellent adsorption for COF2, with an adsorption energy reaching -9.555 eV. This indicates its potential as a clean material for addressing COF2 (toxic) and CO leaks. Lastly, and most importantly, in the assessment of sensing performance, it has been discovered that Cr-BNNT holds promise as a gas sensor for C2F6. Furthermore, to meet diverse sensing requirements and environmental conditions, Cr3-BNNT and Cr4-BNNT have demonstrated potential as sensor array materials for the detection of CO (300 K, 0.145 μs) and COF2 (500 K, 3 min) gas concentrations, respectively. Our study contributes to the advancement of novel eco-friendly insulating gases and explores the use of new materials for monitoring the insulation performance and condition assessment of environmentally friendly GIS equipment.

Ag modified ZnO nanoflower gas sensitive sensor for selective detection of n-butanol

Ag modified ZnO nanoflowers were successfully prepared by sunlight induced solvent reduction method. The samples were characterized by x-ray diffractometer, field emission scanning electron microscope, transmission electron microscope and energy dispersive x-ray spectrum, and the results confirmed the presence of Ag nanoparticles on the ZnO nanoflower. The gas sensing performance of the materials was studied at different operating temperatures and different n-butanol concentrations. The results showed that the Ag modified ZnO nanoflower sensor responded to 50 ppm n-butanol up to 147.17 at 280 °C, and the Ag modified ZnO nanoflower sensor exhibited excellent repeatability, stability and response recovery time. In addition, different target gases were employed for the selectivity study of the Ag modified ZnO nanoflower. It can be found that the Ag modified ZnO nanoflower had good selectivity for n-butanol. The improved response of the Ag modified ZnO nanoflower sensor was attributed to the catalytic effect of Ag nanoparticles. The results indicate that the Ag modified ZnO nanoflower will become a very promising sensing material for n-butanol gas detection.

Experimental Study on the Optimal Operating Envelops of Ejection Supercharging Technology for Unconventional Natural Gas

Abstract Ejection supercharging technology could make use of the energy of high pressure gas wells to assist the development of low pressure unconventional natural gas. But at present, the process of adaptability analysis is complicated, the period is long, and the process implementation is difficult in process application. In this paper, based on the production situation of unconventional natural gas, experiments are carried out to investigate the impact of structural and operational parameters on the performance of ejection supercharging. According to analyze the relationship among high, middle and low pressure inlet conditions and their influence on operational performance, as well as the dimensionless processing of pressure parameters, the optimal operating envelops of Ejection supercharging technology for unconventional natural gas is established. Furthermore, A chart is proposed with variable parameters under different conditions to determine the target gas wells for this technology, allowing for a rapid analysis and determination of whether gas wells are suitable for Ejection supercharging technology. And it also get the pressure reduction range of the process acting on the low pressure well, which provides a reference for analyzing the application effect of the process. Finally, The results of the case analysis demonstrate a 100% match rate with the chart established in this paper. The research results greatly simplify the process of well selection, and transform the analysis for specific working conditions one by one into a unified analysis for multiple working conditions, consequently lays a solid foundation for the widespread application of the ejection supercharging technology for unconventional natural gas.