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In order to study the novel gas detection or sensing applications of gallium nitride monolayer (GaN-ML), we mainly focused on the structural, energetic, electronic and magnetic properties of toxic gas molecules (CO, NO) adsorbed on pristine, single vacancy (N-vacancy, Ga-vacancy) defected, and metals (Al, Fe, Pd and Pt) doped GaN-ML using density functional theory (DFT-D2 method) in this work. The calculations demonstrate that pristine GaN-ML is extremely insensitive to CO and NO together with the existence of a weak physisorption nature due to small adsorption energy, charge transfer, and long adsorption distance. It is found that both N-vacancy defected GaN-ML and Fe-doped GaN-ML can significantly increase the adsorption energy and charge transfer for CO. The CO adsorption induces the metallic characteristics of N-vacancy GaN-ML to be converted to the half-metallic characteristics together with 100% spin polarization, and it also drastically changes the magnetic moment, implying that N-vacancy GaN-ML exhibits excellent sensitivity to CO. However, Fe-doped GaN-ML is not conducive to CO detection. Moreover, N-vacancy defected and Pt-doped GaN-ML greatly improve the adsorption ability for NO compared to other substrates, and the presence of stronger orbital hybridization suggests that the interaction between them is chemisorption. Therefore, N-vacancy defected GaN-ML and Pt-doped GaN-ML can serve as potential materials in future NO sensing devices., Comment: 24 pages, 2 tables, 8 figures

Point of colsest Approche algorithm (PoCA) based on the formalism of muon radiogra- phy using Multiple Coulomb scattering (MCS) as information source is previously used to obtain the reconstruction image of high Z material. The low accuracy of reconstruction image is caused by two factors: the flux of natural muon and the assumption of single scattering in PoCA algo- rithm. In this paper, the maximum likelihood method based on the characteristics of Gaussian-like distribution of muon tracks by MCS is used to predict the optimal track of outgoing muon. The receiver operating characteristic (ROC) and the localization ROC (LROC) are used as two analysis methods to evaluate the quality of reconstruction image. From the results of simulation, the perfect discrimination of longitudinal materials could be well achieved by maximum likelihood algorithm and the discriminate ratio that is predicted by the maximum likelihood method is about 15% higher than that of predicted by PoCA algorithm method. It is seen that the maximum likelihood method can greatly improve the accuracy of reconstruction image., Comment: 12 pages,11 figures. arXiv admin note: substantial text overlap with arXiv:1608.01160

Muon tomography is developing as a promising system to detect high-Z (atomic number) material for ensuring homeland security. In the present work, three kinds of spatial locations of materials which are made of aluminum, iron, lead and uranium are simulated with GEANT4 codes, which are horizontal, diagonal and vertical objects, respectively. Two statistical algorithms are used with MATLAB software to reconstruct the image of detected objects, which are the Point of Closet Approach (PoCA) and Maximum Likelihood Scattering-Expectation Maximization iterative algorithm (MLS-EM), respectively. Two analysis methods are used to evaluate the quality of reconstruction image, which are the Receiver Operating Characteristic (ROC) and the localization ROC (LROC) curves, respectively. The reconstructed results show that, compared with PoCA algorithm, MLS-EM can achieve a better image quality in both edge preserving and noise reduction. And according to the analysis of ROC (LROC) curves, it shows that MLS-EM algorithm can discriminate and exclude the presence and location of high-Z object with a high efficiency, which is more flexible with an different EM algorithm employed than prior work. Furthermore the MLS-EM iterative algorithm will be modified and ran in parallel executive way for improving the reconstruction speed.