No abstract

No abstract

No abstract

Realizing the diagnosis of lung cancer at an inchoate stage is significant to get valuable time to conduct curative surgery. In this work, we relied on a density functional theory (DFT)-proposed Ru−SnS 2 monolayer as a novel, promising biosensor for lung cancer diagnosis through exhaled gas analysis. The results indicated that the Ru−SnS 2 monolayer has admirable adsorption performance for three typical volatile organic compounds (VOCs) of lung cancer patients, which therefore results in a remarkable change in the electronic behavior of the Ru-doped surface. As a consequence, the conductivity of the Ru−SnS 2 monolayer increases after gas adsorption based on frontier molecular orbital theory. This provides the possibility to explore the Ru−SnS 2 monolayer as a biosensor for lung cancer diagnosis at an early stage. In addition, the desorption behavior of three VOCs from the Ru−SnS 2 surface is studied as well. Our calculations aim at proposing novel sensing nanomaterials for experimentalists to facilitate the progress in lung cancer prognosis.

No abstract

No abstract

SF6 decomposition components detection is a key technology to evaluate and diagnose the insulation status of SF6-insulated equipment online, especially when insulation defects-induced discharge occurs in equipment. In order to detect the type and concentration of SF6 decomposition components, a Ni-modified carbon nanotube (Ni-CNT) gas sensor has been prepared to analyze its gas sensitivity and selectivity to SF6 decomposition components based on an experimental and density functional theory (DFT) theoretical study. Experimental results show that a Ni-CNT gas sensor presents an outstanding gas sensing property according to the significant change of conductivity during the gas molecule adsorption. The conductivity increases in the following order: H2S > SOF2 > SO2 > SO2F2. The limit of detection of the Ni-CNT gas sensor reaches 1 ppm. In addition, the excellent recovery property of the Ni-CNT gas sensor makes it easy to be widely used. A DFT theoretical study was applied to analyze the influence mechanism of Ni modification on SF6 decomposition components detection. In summary, the Ni-CNT gas sensor prepared in this study can be an effective way to evaluate and diagnose the insulation status of SF6-insulated equipment online.

Hexagonal-Boron nitride nanotubes (h-BN) decorated with transition metals have been widely studied due to their enhanced physicochemical properties. In this paper, Pt cluster-modified h-BN is proposed as a sensitive material for a novel gas sensor for the online malfunction monitoring of oil-immersed transformers. The inner oil is ultimately decomposed to various gases during the long-term use of oil-immersed transformers. Exposure to excessively high temperatures produces the alkanes CH4 and C2H6, whereas different degrees of discharge generate H2 and C2H2. Therefore, the identification of H2, CH4, and C2H2 gas efficiently measures the quality of transformers. Based on the density functional theory, the most stable h-BN doped with 1–4 Pt atoms is employed to simulate its adsorption performance and response behavior to these typical gases. The adsorption energy, charge transfer, total density of states, projected density of states, and orbital theory of these adsorption systems are analyzed and the results show high consistency. The adsorption ability for these decomposition components are ordered as follows: C2H2 > H2 > CH4. Pt cluster-modified h-BN shows good sensitivity to C2H2, H2, with decreasing conductivity in each system, but is insensitive to CH4 due to its weak physical sorption. The conductivity change of Ptn-h-BN is considerably larger upon H2 than that upon C2H2, but is negligible upon CH4. Our calculations suggest that Pt cluster modified h-BN can be employed in transformers to estimate their operation status.