Gas Mixture Research Articles

Enhancing the light yield of He:CF4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_4$$\\end{document} based gaseous detector

The CYGNO experiment aims to build a large (O(10)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(10)$$\\end{document} m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}) directional detector for rare event searches, such as nuclear recoils (NRs) induced by dark matter (DM), such as weakly interactive massive particles (WIMPs). The detector concept comprises a time projection chamber (TPC), filled with a He:CF4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_4$$\\end{document} 60/40 scintillating gas mixture at room temperature and atmospheric pressure, equipped with an amplification stage made of a stack of three gas electron multipliers (GEMs) which are coupled to an optical readout. The latter consists in scientific CMOS (sCMOS) cameras and photomultipliers tubes (PMTs). The maximisation of the light yield of the amplification stage plays a major role in the determination of the energy threshold of the experiment. In this paper, we simulate the effect of the addition of a strong electric field below the last GEM plane on the GEM field structure and we experimentally test it by means of a 10 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 10 cm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} readout area prototype. The experimental measurements analyse stacks of different GEMs and helium concentrations in the gas mixture combined with this extra electric field, studying their performances in terms of light yield, energy resolution and intrinsic diffusion. It is found that the use of this additional electric field permits large light yield increases without degrading intrinsic characteristics of the amplification stage with respect to the regular use of GEMs.

Decay characteristic of gas arc in C4F7N/N2 and C4F7N/CO2 gas mixture by Thomson scattering

Abstract C4F7N/N2 and C4F7N/CO2 gas mixtures are considered potential alternative gases to SF6, and it is of particular significance to investigate the plasma decay process in these mixtures for evaluating their circuit breaker breaking performance. To comprehend the decay process of electron density(ne) in arc plasma within C4F7N/N2 and C4F7N/CO2 gas mixtures, an arc-generating circuit and a Thomson scattering experiment platform were established. Through coherent Thomson scattering diagnosis of gas arc plasma under various gas mixture conditions, a series of electron density results over time and space were obtained. The findings suggest that the initial electron density of the arc plasma diminishes with an increase in the proportion of C4F7N, and electron density decay is further accelerated as the proportion of C4F7N increases. Moreover, it was observed that the electron density decay rate is higher in the C4F7N/N2 mixture compared to the C4F7N/CO2 mixture. Notably, the electron decay rate in a 50% C4F7N/N2 mixture closely resembles that observed in pure SF6.

Green Pathway of Urea Synthesis Through Plasma–Ice Interaction: Optimization and Mechanistic Insights With N2 + CO2 and NH3 + CO2 Gas Mixtures

ABSTRACTThis study presents a green approach for urea synthesis using plasma–ice interaction with N₂ + CO₂ and NH₃ + CO₂ gas mixtures. Plasma characterization via electrical and optical emission spectroscopy revealed that urea formation involves complex reactions driven by high‐energy species, producing reactive nitrogen and carbon intermediates. Plasma‐treated ice exhibited increased pH, electrical conductivity (EC), and reduced oxidation–reduction potential (ORP). Optimization of parameters, especially treatment time, significantly influenced urea formation. The NH₃ + CO₂ plasma produced a higher urea concentration (7.7 mg L−1) compared to the N₂ + CO₂ plasma (0.55 mg L−1) due to ammonia's greater reactivity. This method offers a sustainable, energy‐efficient alternative to traditional urea synthesis processes.

In Situ Exsolved CuZn Alloy Electrocatalysts for CO2 Conversion to Tunable Syngas Production

AbstractAlloying is considered as a promising method for syngas (H2 and CO mixture gas) generation. However, it is challenging to elucidate the relationship between the ratios of syngas and alloy components for CO2 reduction reaction. Herein, through tuning the Cu/Zn ratio on CuZn alloy, the H2/CO ratios in CuZn‐2 (Cu/Zn = 0.77) can be tuned in a noticeable range between 0.8 and 5.8 at −0.88 to −1.28 V versus RHE, and a durable stability of 12 h. The CuZn‐2 displays better performance and enhanced dynamics than other samples. Electrochemical impedance spectroscopies and Tafel results reveal that CuZn‐2 behaves kinetically optimized performance. Density functional theory calculations results reveal that CuZn alloy exhibits middle desorption intensity of *H for HER compared with pure Zn and Cu. CuZn alloy effectively decreases the energy barrier of CO2 molecules activated to *COOH relative to Zn, and also the alloy renders it easier for *CO desorb to generate CO for Cu. In situ Fourier transform infrared spectroscopies also observe a significant intensity of *CO on CuZn‐2. This work demonstrates that alloying Cu and Zn can create active sites, in which Cu acts as an auxiliary active site further facilitating *CO generation.

Machine Learnable Language for the Chemical Space of Nanopores Enables Structure-Property Relationships in Nanoporous 2D Materials.

The synthesis of nanoporous two-dimensional (2D) materials has revolutionized fields such as membrane separations, DNA sequencing, and osmotic power harvesting. Nanopores in 2D materials significantly modulate their optoelectronic, magnetic, and barrier properties. However, the large number of possible nanopore isomers makes their study onerous, while the lack of machine-learnable representations stymies progress toward structure-property relationships. Here, we develop a language for nanopores in 2D materials, called STring Representation Of Nanopore Geometry (STRONG), that opens the field of 2D nanopore informatics. We show that STRONGs are naturally suited for machine learning via recurrent neural networks, predicting formation energies/times of arbitrary nanopores and transport barriers for CO2, N2, and O2 gas molecules, enabling structure-property relationships. The machine learning models enable the discovery of specific nanopore topologies to separate CO2/N2, O2/CO2, and O2/N2 gas mixtures with high selectivity ratios. We also enable the rapid enumeration of unique configurations of stable, functionalized nanopores in 2D materials via STRONGs, allowing systematic searching of the vast chemical space of nanopores. Using the STRONGs approach, we find that a mix of hydrogen and quinone functionalization results in the most stable functionalized nanopore configuration in graphene, a discovery made feasible by expedited chemical space exploration. Additionally, we also unravel the STRONGs approach as ∼1000 times faster than graph theory algorithms to distinguish nanopore shapes. These advances in the language-based representation of 2D nanopores will accelerate the tailored design of nanoporous materials.

Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines

In this study, we aim to develop advanced machine learning regression models for the prediction of hydrate temperature based on the chemical composition of sweet gas mixtures. Data were collected in accordance with the BOTAS Gas Network Code specifications, approved by the Turkish Energy Market Regulatory Authority (EMRA), and generated using DNV GasVLe v3.10 software, which predicts the phase behavior and properties of hydrocarbon-based mixtures under various pressure and temperature conditions. We employed linear regression, decision tree regression, random forest regression, generalized additive models, and artificial neural networks to create prediction models for hydrate formation temperature (HFT). The performance of these models was evaluated using the hold-out cross-validation technique to ensure unbiased results. This study demonstrates the efficacy of ensemble learning methods, particularly random forest with an R2 and Adj. R2 of 0.998, for predicting hydrate formation conditions, thereby enhancing the safety and efficiency of gas transport and processing. This research illustrates the potential of machine learning techniques in advancing the predictive accuracy for hydrate formations in natural gas pipelines and suggests avenues for future optimizations through hybrid modeling approaches.

Predicting interfacial tension in brine-hydrogen/cushion gas systems under subsurface conditions: Implications for hydrogen geo-storage

Underground hydrogen storage (UHS) critically relies on cushion gas to maintain pressure balance during injection and withdrawal cycles, prevent excessive water inflow, and expand storage capacity. Interfacial tension (IFT) between brine and hydrogen/cushion gas mixtures is a key factor affecting fluid dynamics in porous media. This study develops four machine learning models— Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP)—to predict IFT under geo-storage conditions. These models incorporate variables such as pressure, temperature, molality, overall gas density, and gas composition to evaluate the impact of different cushion gases. A group-based data splitting method enhances the realism of our tests by preventing information leakage between training and testing datasets. Shapley Additive Explanations (SHAP) reveal that while the MLP model prioritizes gas composition, the RF model focuses more on operational parameters like pressure and temperature, showing distinct predictive dynamics. The MLP model excels, achieving coefficients of determination (R2) of 0.96, root mean square error (RMSE) of 2.10 mN/m, and average absolute relative deviation (AARD) of 3.25%. This robustness positions the MLP model as a reliable tool for predicting IFT values between brine and hydrogen/cushion gas (es) mixtures beyond the confines of the studied dataset. The findings of this study present a promising approach to optimizing hydrogen geo-storage through accurate predictions of IFTs, offering significant implications for the advancement of energy storage technologies.

A Study of Heat Recovery and Hydrogen Generation Systems for Methanol Engines

Biofuels are the most essential types of alternative fuels, which currently have significant potential to reduce CO2 emissions compared to fossil fuels. Methanol is a more efficient fuel than petrol due to its physicochemical properties, such as a higher latent heat of vaporization, research octane number, and heat of combustion of the fuel–air mixture. Also, biomethanol is cheaper than traditional petrol and diesel fuel for agricultural countries. The authors have proposed a new approach to improve the characteristics and efficiency of methanol diesel engines by using biomethanol mixed with hydrogen instead of pure biomethanol. Using a hydrogen–biomethanol mixture in modern engines is an effective method because hydrogen is a carbon-free, low-ignition, highest-flame-rate, high-octane fuel. A small quantity of hydrogen added to biomethanol and its combustion in an engine with a heat exchanger increases the combustion temperature and heat release, increases engine power, and reduces fuel consumption. This article presents experimental results of methanol combustion and a hydrogen-in-methanol mixture if hydrogen was retained due to the utilization of the heat of the exhaust gases. The tests were carried on a single-cylinder experimental engine with an injection of liquid methanol and gaseous hydrogen mixtures. The experiments showed that green hydrogen generated onboard the car due to the utilization of heat significantly reduced fuel costs of engines of vehicles and technological installations. It was established a hydrogen gaseous mixture addition of up to 5% by mass to methanol requires a corresponding change in the coefficient of excess air to λ = 1.25. Also, using an additional hydrogen mixture requires adjustment at the ignition moment in the direction of its decrease by 4–5 degrees of the engine crankshaft. Hydrogen gas mixture addition reduced methanol consumption, reaching a maximum reduction of 24%. The maximum increase in power was 30.5% based on experimental data. The reduction in the specified fuel consumption, obtained after experimental tests of the methanol research engine on the stand, can be implemented on the vehicle engines and technological installations equipped with an onboard heat recovery system. Such a system, due to the utilization of heat and the supply of additional hydrogen, can be implemented for engines that work on any alternative or traditional fuels.

Numerical Simulation of Heat Transfer Characteristics of CO2 Gas Mixture Injection into Wellbores

Numerical Simulation of Heat Transfer Characteristics of CO₂ Gas Mixture Injection into Wellbores

Room Temperature Based SnO2/MoS2 Chemiresistive Sensor for Highly Selective Detection of Acetone for Disease Management through Simulated Gas Mixture Analysis

Room Temperature Based SnO₂/MoS₂ Chemiresistive Sensor for Highly Selective Detection of Acetone for Disease Management through Simulated Gas Mixture Analysis

Influence of on-site sampling conditions on the representativeness of the sample for the analysis of trace VOCs in raw renewable gases.

In the context of the energy transition and in order to fully manage the integration of renewable and/or low-carbon gases into the gas mix, these new renewable gases need to be characterized, including volatile organic compounds (VOCs) at trace levels that may have an impact on different stages of the gas chain event at low concentrations. This study focuses on sampling because it is the first step in any method for analyzing trace VOCs and its careful execution is essential to ensure reliable results even if the on-site conditions can be variable, such as the external temperature. The stabilization time, the effect of external temperature, and the impact of using an intermediate sample cylinder prior to transfer to tubes were hence studied in the laboratory using a standard gas mixture representative of renewable gases. The latter was also studied using a real sample. To perform this study, VOCs were sampled in Tenax® TA tubes and then analyzed by thermal desorption hyphenated to comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. The results showed that 45min were required to stabilize the sampling system and that external temperature had little influence, limited to the heaviest compounds. Finally, the use of a cylinder to temporarily collect gas prior to transfer to tubes led to a loss of compounds, particularly when the cylinder was stored before the transfer.

Carbon dioxide injection into the Achimov formations using reinjection technology by the example of Ach3-4 formation in the Novo-Urengoy area of the Urengoy field

The prerequisites for the study are the calculation results for the cycling process, where carbon dioxide is proposed as the injection agent into the Achimov formations instead of dry gas, with the goal of increasing the condensate recovery factor. The work is focused on the efficiency assessment of carbon dioxide reinjection technology and reducing carbon footprint at a late stage of field development. The research object is the Аch3-4 formation within the Novo-Urengoy license area of the Urengoy field. The leading method to identify this problem is the results of the full-scale composite dynamic model in the ECLIPSE 300 format. The model takes into account the history of field development on depletion. The articles deals with two schemes for injecting carbon dioxide into the formation. In the first scheme, pure carbon dioxide is injected in a closed-loop system, but carbon neutrality through storage is not achieved. In the second scheme, carbon dioxide is injected using reinjection technology. Once injection begins, gas production stops. Only the condensate separated from the formation gas during low-temperature separation is sold and sent for further processing. After allocation of the condensate, the mixture of natural gas and carbon dioxide, in a specific proportion, is sent to the compressor station for reinjection into the formation in a gaseous state. Injecting pure carbon dioxide achieves a condensate recovery factor similar to that of gas injection with a 30 % carbon dioxide mixture. However, this option is less economically viable compared to the base and other scenarios due to high capital costs for upgrading the existing gas processing equipment (requiring the construction of an amine treatment unit). With carbon dioxide injection using reinjection technology, in addition to recovering extra condensate that had condensed during natural depletion, a reduction in the carbon footprint is also achieved. To maximize the condensate recovery factor, the optimal concentration of carbon dioxide in the injection mixture has been determined. The optimal timing for the start of injection was identified to maximize gas recovery. Economic efficiency is expected from the additional recovery of condensate trapped in the reservoir and from achieving carbon neutrality through the monetization and storage of carbon dioxide.

Modeling of adsorption-controlled binary gas transport in ultratight porous media

Organic-rich ultratight porous media such as shales have complicated pore types and sizes, dictating their unique fluid transport and storage mechanisms. This paper introduces a diffusion-based binary gas (CH4 and CO2) transport model in such porous media, incorporating key transport and storage mechanisms. Binary gas mixture within the pore volume is divided into two domains: the free-phase and the sorbed-phase, both of which contribute to gas mass transport. Thermodynamic properties of the free phase are determined using the Peng-Robinson equation of state, while a modified extended Langmuir isotherm describes the sorbed phase. The bulk diffusion, Knudsen diffusion, and viscous diffusion are reconciled to describe binary gas transport in the free phase, while the surface diffusion is considered in the sorbed phase. The mass flux is finally related to the effective diffusion coefficient and the free-phase concentration gradient through coupling all transport mechanisms via the equivalent resister network model. The governing equations are solved numerically using a finite difference approach to simulate co-diffusion and counter-diffusion of a binary gas mixture in two nanoporous media, shale and coal.The results reveal that surface diffusion is more pronounced in coal than in shale due to coal's higher adsorption capacity. In co-diffusion cases, both free-phase and sorbed-phase concentrations decrease over time. However, the decline of sorbed-phase concentrations is slower than that of the free-phase. Moreover, CO2 tends to remain adsorbed within the coal matrix due to its stronger adsorption affinity, unlike CH4, which is more likely to flow out. In counter-diffusion cases, injected CO2 first accumulates near the fracture region and then diffuses further into the matrix. Despite shale's pore volume being twice that of coal, similar amounts of CO2 are injected, which can be attributed to coal's higher CO2 adsorption capacity.

Inkjet-Printed Localized Surface Plasmon Resonance Subpixel Gas Sensor Array for Enhanced Identification and Visualization of Gas Spatial Distributions from Multiple Odor Sources

The visualization of the spatial distributions of gases from various sources is essential to understanding the composition, localization, and behavior of these gases. In this study, an inkjet-printed localized surface plasmon resonance (LSPR) subpixel gas sensor array was developed to visualize the spatial distributions of gases and to differentiate between acetic acid, geraniol, pentadecane, and cis-jasmone. The sensor array, which integrates gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), and fluorescent pigments, was positioned 3 cm above the gas source. Hyperspectral imaging was used to capture the LSPR spectra across the sensor array, and these spectra were then used to construct gas information matrices. Principal component analysis (PCA) enabled effective classification of the gases and localization of their sources based on observed spectral differences. Heat maps that visualized the gas concentrations were generated using the mean squared error (MSE) between the sensor responses and reference spectra. The array identified and visualized the four gas sources successfully, thus demonstrating its potential for gas localization and detection applications. The study highlights a straightforward, cost-effective approach to gas sensing and visualization, and in future work, we intend to refine the sensor fabrication process and enhance the detection of complex gas mixtures.

A Carbon Capture and Utilization Process for the Production of Solid Carbon Materials from Atmospheric CO2 - Part 1: Process Performance.

The successful operation of a process that converts atmospheric CO2 into solid carbon products is presented as an alternative to fossil based solid carbon production. In a first step, CO2 is removed from the atmosphere by a direct air capture (DAC) unit. The gas is then mixed with hydrogen and enters a methanation unit. Depending on the operation conditions, gas mixtures consisting of mainly methane with either H2 or CO2 as side-component are obtained. After precipitating the water formed during the methanation step, the remaining gas mixture is fed into a bubble column reactor filled with liquid tin. During the rise of the gas bubbles, methane is thermally split up into hydrogen and solid carbon. The latter is continuously removed from the liquid metal surface as a fine powder by pneumatic conveying. This article is the first of two articles, focusing on the performance of the methanation and methane pyrolysis steps. The experimental results are complemented by thermodynamic analyses and reaction modelling. A detailed analysis of the solid carbon product of the process is presented in the second part.

Species-Based Modeling of Binary Gas Mixture Transport in Nanoporous Media with Adsorption

Species-Based Modeling of Binary Gas Mixture Transport in Nanoporous Media with Adsorption

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.

A Carbon Capture and Utilization Process for the Production of Solid Carbon Materials from Atmospheric CO2 - Part 2: Carbon Characterization.

This article is the second part of a study reporting the results of a novel carbon capture and utilization (CCU) process, which converts atmospheric CO2 into solid carbon materials. The CCU process combines direct air capture (DAC) with catalytic methanation, which is then followed by methane pyrolysis in a reactor filled with liquid tin. While Part 1 discussed the performance of the overall process and individual process steps regarding conversions and yields, Part 2 characterizes the solid carbon products obtained under various synthesis conditions. The effects of the pyrolysis temperature and the composition of the gas mixture from the methanation step on the solid carbon product are analyzed. Carbon powder is synthesized from methane with either H2 or CO2 as most important impurity. Carbon samples are characterized using SEM, TEM, Raman spectroscopy, XPS and XRD analysis. Very thin, disordered carbon flakes, soot aggregates and large carbon onions are identified as the main solid products of the process. Their formation mechanisms in the liquid metal-filled pyrolysis reactor are discussed.

Elucidating redox pathways for N2O selective catalytic reduction with NO and NH3 over Fe-chabazite zeolites

We combined kinetics and in situ X-ray absorption spectroscopy to investigate redox pathways of N2O-selective catalytic reduction (SCR) by NH3 and NO over Fe-exchanged Chabazite zeolites, a strategy to mitigate emissions of greenhouse gas N2O and toxic NOx from sources such as NH3 engines. N2O-SCR occurs via FeII/FeIII redox cycles with N2O as the oxidant, and parallel reactions involving NO+NH3 or NH3 as reductants, where in situ XAS shows that most Fe ions are redox-active. NO co-reduces FeIII with NH3 rather than oxidizing to NO2. Net rates of N2O-SCR in gas mixtures containing NH3 versus NO+NH3 are both limited by FeII oxidation, resulting in similar apparent activation energies. In contrast, reduction pathway selectivity reflects the intrinsic kinetics of FeIII reduction steps with NO+NH3 versus NH3, implying that reduction involving NO+NH3 dominates at lower temperatures and higher NO pressures. This work provides fundamental insights into kinetics and mechanisms of N2O-SCR.

Discovery of a novel binary azeotrope with positive synergistic insulation strength as eco-friendly SF6-alternative

Abstract Sulfur hexafluoride (SF6), as the arc extinguishing and insulating medium, is broadly applied in power equipment. While it is very essential to find an environmentally friendly substitute gas for SF6 due to its strong greenhouse effect. Although there are many potential alternatives, most have relatively high boiling points that cannot meet the minimum operating temperature of the power equipment. In the past, the liquefaction temperature of the mixture was reduced by mixing with buffer gases, such as CO2, N2, or dry air. But this method cannot take the insulation performance requirements of eco-friendly insulating gas into account. Therefore, given the current problems and challenges, a novel approach is presented to exploring new eco-friendly gases by incorporating the azeotropic theory. The liquefaction temperature of the gas mixture can be reduced simultaneously while the insulation strength is increased. In this approach, a theoretical prediction model E-PPR78 was introduced to predict the vapor-liquid equilibrium data and RC318 was found to exhibit azeotrope behavior with HFO-1336mzz(E) at 17%. Furthermore, the predicted vapor-liquid equilibrium data obtained were validated against cyclic-analytical experimental results, affirming the efficacy of the proposed model. Subsequently, to assess its insulation performance, breakdown tests were conducted under both AC and lightning impulse voltages, revealing significant positive synergistic effects in the gas mixture. The possible mechanisms of this positive synergistic effect were also discussed. This study offers an innovative way of tackling high boiling point issues plaguing eco-friendly gases. The findings may also enlighten the design of eco-friendly switchgear that utilizes HFO-1336mzz(E) mixtures to replace traditional SF6 gases.