DFT-based study on H2S and SOF2 adsorption on Si-MoS2 monolayer

Gui, Yingang; Liu, Daikun; Li, Xiaodong; Zhou, Qu

doi:10.1016/j.rinp.2019.102225

Cited by 49 publications

(24 citation statements)

References 39 publications

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“…The separation distance corresponds to the width between the two As shown in Table 2, we calculated E f orm for the four Ga doping models. It was evident that the most favorable doping position had the lowest formation energy, which means that the reaction occurred most easily [42,43]. The T Mo site had the smallest formation energy of −1.75 eV.…”

Section: Geometric Structure Of Gas Molecules Mos2 and Ga-mos2 Monolayermentioning

confidence: 98%

First-Principle Insight into Ga-Doped MoS2 for Sensing SO2, SOF2 and SO2F2

Hou

Peng

et al. 2021

Nanomaterials

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First-principle calculations were carried out to simulate the three decomposition gases (SO2, SOF2, and SO2F2) of sulfur hexafluoride (SF6) on Ga-doped MoS2 (Ga-MoS2) monolayer. Based on density functional theory (DFT), pure MoS2 and multiple gas molecules (SF6, SO2, SOF2, and SO2F2) were built and optimized to the most stable structure. Four types of Ga-doped positions were considered and it was found that Ga dopant preferred to be adsorbed by the top of Mo atom (TMo). For the best adsorption effect, two ways of SO2, SOF2, and SO2F2 to approach the doping model were compared and the most favorable mode was selected. The adsorption parameters of Ga-MoS2 and intrinsic MoS2 were calculated to analyze adsorption properties of Ga-MoS2 towards three gases. These analyses suggested that Ga-MoS2 could be a good gas-sensing material for SO2 and SO2F2, while it was not suitable for SOF2 sensing due to its weak adsorption. This work provides a theoretical basis for the development of Ga-MoS2 materials with the hope that it can be used as a good gas-sensing material for electrical equipment.

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Section: Geometric Structure Of Gas Molecules Mos2 and Ga-mos2 Monolayermentioning

confidence: 98%

First-Principle Insight into Ga-Doped MoS2 for Sensing SO2, SOF2 and SO2F2

Hou

Peng

et al. 2021

Nanomaterials

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“…However, the position of the convolution core far away from the center was almost zero (the value of the far end of the convolution core caused by the change of unit displacement and unit pressure is almost zero). This means that the inversion of equations was impossible, as some unknown coefficients in some equations are close to 0, which greatly affects solution accuracy [18].…”

Section: Inversion Methods Of Reservoir Average Pore Pressure Changesmentioning

confidence: 99%

An Improved New Convolutional Neural Network Method for Inverting the Pore Pressure in Oil Reservoir by Surface Vertical Deformation

Wang

et al. 2021

Lithosphere

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Average pore pressure in oil formation is an important parameter to measure energy in the formation and the capacity of injection–production. In past studies, average pore pressure mainly depends on pressure build-up test results, which have a high cost and need a long testing time, so it is not conducive to utilize. In this paper, the vertical deformation of the Earth’s surface was used to calculate changes in reservoir pore pressure. We set up the marker stake for measuring ground displacement and measured the vertical deformation at the in situ position. Furthermore, we provided an improved new convolutional neural network (CNN), which adopted image-to-image mode, removed pooling layers and full connection layers, and used a new loss function considering the boundary influence coefficient matrix. Then, the machine learning method was used to invert surface vertical deformation to change pore pressure in the oil reservoir. The method was tested in the block of the Sazhong X development zone, in the Daqing Oilfield of China. The average pore-pressure change values of 20 grids whose area was 900×900 m each were determined by the inversion of the corresponding surface vertical deformation at 27 marker stakes. The pore pressure change accuracy reached 82.34%. This study provides a new method for calculating average pore pressure in terms of an oil block. At the same time, it also provides a new technical method for inversion calculations.

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“…For detection of SF 6 decomposed gases, impurity‐doped MoS 2 was widely proposed and investigated in order to explore its sensing application upon typical gases. Gui et al [133, 134] using DFT method reported the adsorption performances of Si‐ and Co‐doped MoS 2 monolayer upon H 2 S, SOF 2 and SO 2 F 2 . Chen et al [135] reported the Pt‐ and Au‐doped MoS 2 monolayer for sensing SO 2 , SOF 2 and SO 2 F 2 as well.…”

Section: Application Of Nanomaterials‐based Gas Sensorsmentioning

confidence: 99%

Nanomaterials‐based gas sensors of SF ₆ decomposed species for evaluating the operation status of high‐voltage insulation devices

et al. 2019

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In recent years, many novel sensing nanomaterials were explored experimentally and theoretically as chemical gas sensors to detect sulphur hexafluoride (SF 6) decomposed species with rapid response, high sensitivity and selectivity, as well as low cost, in order to estimate and guarantee the operation status of SF 6 insulation devices. However, there has been no systemic review yet to comprehensively understand their sensing mechanism and conclude their sensing application. Such kinds of nanomaterials include traditional carbon-based materials such as carbon nanotube and graphene, metal oxides such as SnO 2 , ZnO and TiO 2 , transition metal dichalcogenides such as MoS 2 monolayer and MoTe 2 monolayer as well as graphene-like InN monolayer. A systemic review would be significant to give a profound insight into the exploration of nanomaterial-based chemical gas sensors in the field of electrical engineering and to help youth scholars first stepping in this field rapidly master the progress inside. Thus, the authors attempt to complete this review here. They are hopeful that this review could provide some guidance to fulfil such purpose in the near future, and it would be full of perspective to explore more novel nanomaterials as defect indicators in SF 6 insulated devices. Furthermore, it is their hope that this review could broaden the sensing application of admirable nanomaterials in many fields such as environmental monitoring and pollutant scavenging.

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DFT-based study on H2S and SOF2 adsorption on Si-MoS2 monolayer

Cited by 49 publications

References 39 publications

First-Principle Insight into Ga-Doped MoS2 for Sensing SO2, SOF2 and SO2F2

First-Principle Insight into Ga-Doped MoS2 for Sensing SO2, SOF2 and SO2F2

An Improved New Convolutional Neural Network Method for Inverting the Pore Pressure in Oil Reservoir by Surface Vertical Deformation

Nanomaterials‐based gas sensors of SF ₆ decomposed species for evaluating the operation status of high‐voltage insulation devices

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DFT-based study on H2S and SOF2 adsorption on Si-MoS2 monolayer

Cited by 49 publications

References 39 publications

First-Principle Insight into Ga-Doped MoS2 for Sensing SO2, SOF2 and SO2F2

First-Principle Insight into Ga-Doped MoS2 for Sensing SO2, SOF2 and SO2F2

An Improved New Convolutional Neural Network Method for Inverting the Pore Pressure in Oil Reservoir by Surface Vertical Deformation

Nanomaterials‐based gas sensors of SF 6 decomposed species for evaluating the operation status of high‐voltage insulation devices

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Nanomaterials‐based gas sensors of SF ₆ decomposed species for evaluating the operation status of high‐voltage insulation devices