Experimental Analysis of Modified CNTs-Based Gas Sensor

As a significant equipment in power system, the operation condition of transformers directly determines the safety of power system. Therefore, it has been an indispensable measure to detect and analyze the dissolved gases in transformers, aiming to estimate the early potential faults in oil‐insulated transformers. In this chapter, the adsorption processes between modified carbon nanotubes (CNTs) (CNTs‐OH, Ni‐CNTs) and dissolved gases in transformers oil including C2H2, C2H4, C2H6, CH4, CO, and H2 have been simulated based on the first principle theory. Meanwhile, the density of states (DOS), adsorption energy, charge transfer amount, and adsorption distance of adsorption process between CNTs and dissolved gases were calculated. Moreover, two kinds of sensors, mixed acid‐modified CNTs and NiCl2‐modified CNTs, are prepared to conduct the dissolved gases response experiment. Then, the gas response mechanisms were investigated. Finally, the results between response experiment and theoretical calculation were compared, reflecting a good coherence with each other. The CNTs gas sensors possess a relatively high sensitivity and fine linearity, and could be employed in dissolved gas analysis equipment in transformer

Application of Graphene Gas Sensors in Online Monitoring of SF6 Insulated Equipment

Graphene is an allotrope of carbon apart from graphite, diamond, fullerene and carbon nanotubes. Because graphene has unique mechanical, structural, thermal and electrochemical properties and can present the stability characteristics of these features, it becomes two‐dimensional (2‐d) materials which can alter three‐dimensional (3‐d) carbon nanotubes composite materials and has important research value. Pristine graphene and graphene films doped with Au nanoparticles were synthesized by the chemical reduction method. Their corresponding gas sensors were both fabricated by the traditional drop coating method and then used as an adsorbent for the detection of H2S, SO2, SOF2 and SO2F2 at room temperature. Theoretical simulation was also investigated when the decomposed gaseous components of sulfur hexafluoride (SF6), namely, H2S, SO2, SOF2 and SO2F2, were adsorbed on pristine and Au‐embedded graphene based on DFT. In order to interpret the adsorption processes between Au‐doped graphene and gas molecules, this chapter discussed the charge transfer mechanism on the adsorption surface for further investigation

Application of TiO2 Nanotubes Gas Sensors in Online Monitoring of SF6 Insulated Equipment

Titanium dioxide nanotube arrays (TNTAs) are a typical three-dimensional nanomaterial. TNTA has rich chemical and physical properties and low manufacturing costs. Thus, TNTA has broad application prospects. In recent years, research has shown that because of its large specific surface area and nanosize effect, the TNTAs have an enormous potential for development compared with other nanostructure forms in fields such as light catalysis, sensor, and solar batteries. TNTAs have become the hotspot of international nanometer material research. The tiny gas sensor made from TNTA has several advantages, such as fast response, high sensitivity, and small size. Several scholars in this field have achieved significant progress. As a sensitive material, TNTA is used to test O2, NO2, H2, ethanol, and other gases. In this chapter, three SF6 decomposed gases, namely SO2, SOF2 and SO2F2, are chosen as probe gases because they are the main by-products in the decomposition of SF6 under PD. Then, the adsorption behaviors of these gases on different anatase (101) surfaces including intrinsic, Pt-doped and Au-doped, are studied using the first principles density functional theory (DFT) calculations. The simulation results can be used as supplement for gas-sensing experiments of TNTA gas sensors. This work is expected to add insights into the fundamental understanding of interactions between gases and TNTA surfaces for better sensor design

The SF6 Decomposition Mechanism: Background and Significance

Gas Insulated Switchgear (GIS) has been widely used in substations. The insulating medium used in GIS is sulfur hexafluoride (SF6) gas. However, the internal insulation defect existed in GIS would inevitably lead to partial discharge (PD), and cause the composition of SF6 to SOF2, SO2F2 and SO2 and other characteristic component gases. The decomposition phenomenon would greatly reduce the insulation performance of SF6 insulated equipment, and even paralyze the whole power supply system. In this chapter, we first discuss the objective existence, decomposition mechanism and harmness of insulation defects. Then the methods for insulation defects detection used to avoid the insulation accidents are introduced. Comparing all of the detection methods, diagnosing the insulation defect through analyzing the decomposed gases of SF6 by chemical gas sensors is the optimal method due to its advantages, such as high detection accuracy and stability, signifying the importance of developing chemical gas sensor used in SF6 insulated equipment. In conclusion, there kinds of gas sensor material, carbon nanotubes, graphene, are chosen as the gas sensing materials to build specific gas sensors for detecting each kind of SF6 decomposed gases, and then enhance the gas sensitivity and selectivity by material modification

Application of CNTs Gas Sensor in Online Monitoring of SF6 Insulated Equipment

The detection and analysis of SF6 decomposition components are of great significance in online condition assessment and fault diagnosis of GIS. Considering the shortcomings of general detection methods, carbon nanotubes (CNTs) gas sensor was studied to detect the SF6 decomposition components because of its advantages in large surface activity and abundant pore structure, et al. The large surface area has a strong adsorption and desorption capacity. In this chapter, SF6 decomposed gases, namely SO2F2, SOF2, SO2, H2S and CF4 are chosen as probe gases because they are the main by-products in the decomposition of SF6 under partial density (PD). First, the properties and preparation methods of CNTs are introduced to verify the advantages of CNTs for SF6 decomposition components detection. Then, both theoretical calculation and sensing experiment were adopted to study the microadsorption mechanism and macrogas-sensing properties. Based on the intrinsic CNTs, study for SF6 decomposition components adsorption, Pd, Ni, Al, Pt and Au metal doping CNTs and plasma-modified CNTs are discussed in order to enhance the gas sensing and selectivity of CNTs

Comparative Study of Materials to SF6 Decomposition Components

In order to judge the inside insulation fault of SF6 insulated equipment, the gas-sensing properties to a series of characteristic SF6 decomposition components, SOF2, SO2F2, SO2, H2S, CF4, HF, and SF6, have been studied. In this study, a comparative study of these gas-sensing materials has been made in theoretical and experimental fields to find the optimal gas-sensing material, and put forward the effective approaches to improve the gas-sensing properties of materials

Application of Virtual Simulation Technology in Theory and Experiment Teaching of Air Pollution Control Engineering

Virtual reality technology provides great convenience for humans to explore the macro and micro worlds due to its extremely realistic experience, and it will be seen in all walks of life in the future. This paper focuses on the analysis of the current situation of virtual simulation technology in the teaching application of air pollution control engineering theory teaching and experimental teaching, as well as the advantages and disadvantages of application. Furthermore, the development and prospect of virtual simulation technology in air pollution control engineering theory and experimental teaching are summarized. Keywords: virtual simulation technology, air pollution control engineering, theoretical teaching, experimental teaching DOI: 10.7176/JEP/13-29-08 Publication date:October 31st 202

AOB Nitrosospira cluster 3a.2 (D11) dominates N2O emissions in fertilised agricultural soils.

CRediT authorship contribution statement Na Deng: Writing – review & editing, Methodology, Investigation, Data curation. Cecile Gubry-Rangin: Writing – review & editing, Methodology, Conceptualization. Xiao-Tong Song: Writing – review & editing, Methodology, Data curation. Xiao-Tang Ju: Writing – review & editing, Conceptualization. Si-Yi Liu: Methodology, Data curation. Ju-Pei Shen: Writing – review & editing, Data curation. Hong-jie Di: Writing – review & editing. Li-Li Han: Writing – review & editing, Methodology. Li-Mei Zhang: Writing – review & editing, Methodology, Data curation, Conceptualization.Peer reviewe

Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric

Combining the Color and Event cameras (also called Dynamic Vision Sensors, DVS) for robust object tracking is a newly emerging research topic in recent years. Existing color-event tracking framework usually contains multiple scattered modules which may lead to low efficiency and high computational complexity, including feature extraction, fusion, matching, interactive learning, etc. In this paper, we propose a single-stage backbone network for Color-Event Unified Tracking (CEUTrack), which achieves the above functions simultaneously. Given the event points and RGB frames, we first transform the points into voxels and crop the template and search regions for both modalities, respectively. Then, these regions are projected into tokens and parallelly fed into the unified Transformer backbone network. The output features will be fed into a tracking head for target object localization. Our proposed CEUTrack is simple, effective, and efficient, which achieves over 75 FPS and new SOTA performance. To better validate the effectiveness of our model and address the data deficiency of this task, we also propose a generic and large-scale benchmark dataset for color-event tracking, termed COESOT, which contains 90 categories and 1354 video sequences. Additionally, a new evaluation metric named BOC is proposed in our evaluation toolkit to evaluate the prominence with respect to the baseline methods. We hope the newly proposed method, dataset, and evaluation metric provide a better platform for color-event-based tracking. The dataset, toolkit, and source code will be released on: \url{https://github.com/Event-AHU/COESOT}