The Experts below are selected from a list of 310803 Experts worldwide ranked by ideXlab platform
Magali Benoit - One of the best experts on this subject based on the ideXlab platform.
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Equilibrium shape of core(Fe)–shell(Au) nanoparticles as a function of the metals volume ratio
Journal of Applied Physics, 2020Co-Authors: Anne Ponchet, Segolene Combettes, Patrizio Benzo, Nathalie Tarrat, Marie-josé Casanove, Magali BenoitAbstract:The equilibrium shape of nanoparticles is investigated to elucidate the various core–shell morphologies observed in a bimetallic system associating two immiscible metals, iron and gold, that crystallize in the bcc and fcc lattices, respectively. Fe–Au core–shell nanoparticles present a crystalline Fe core embedded in a polycrystalline Au shell, with core and shell morphologies both depending on the Au/Fe volume ratio. A model is proposed to calculate the energy of these nanoparticles as a function of the Fe volume, Au/Fe volume ratio, and the core and shell shape, using the density functional theory-computed energy densities of the metal surfaces and of the two possible Au/Fe interfaces. Three driving forces leading to equilibrium shapes were identified: the strong adhesion of Au on Fe, the Minimization of the Au/Fe interface energy that promotes one of the two possible interface types, and the Au surface energy Minimization that promotes a 2D–3D Stranski–Krastanov-like transition of the shell. For a low Au/Fe volume ratio, the wetting is the dominant driving force and leads to the same polyhedral shape for the core and the shell, with an octagonal section. For a large Au/Fe ratio, the surface and interface energy Minimizations can act independently to form an almost cube-shaped Fe core surrounded by six Au pyramids. The experimental nanoparticle shapes are well reproduced by the model, for both low and large Au/Fe volume ratios.
Clayton G Webster - One of the best experts on this subject based on the ideXlab platform.
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a class of null space conditions for sparse recovery via nonconvex non separable Minimizations
Results in Applied Mathematics, 2019Co-Authors: Hoang Tran, Clayton G WebsterAbstract:Abstract For the problem of sparse recovery, it is widely accepted that nonconvex Minimizations are better than l 1 penalty in enhancing the sparsity of solution. However, to date, the theory verifying that nonconvex penalties outperform (or are at least as good as) l 1 Minimization in exact, uniform recovery has mostly been limited to separable cases. In this paper, we establish general recovery guarantees through null space conditions for nonconvex, non-separable regularizations, which are slightly less demanding than the standard null space property for l 1 Minimization.
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a class of null space conditions for sparse recovery via nonconvex non separable Minimizations
arXiv: Optimization and Control, 2017Co-Authors: Hoang Tran, Clayton G WebsterAbstract:For the problem of sparse recovery, it is widely accepted that nonconvex Minimizations are better than $\ell_1$ penalty in enhancing the sparsity of solution. However, to date, the theory verifying that nonconvex penalties outperform (or are at least as good as) $\ell_1$ Minimization in exact, uniform recovery has mostly been limited to separable cases. In this paper, we establish general recovery guarantees through null space conditions for nonconvex, non-separable regularizations, which are slightly less demanding than the standard null space property for $\ell_1$ Minimization.
Anne Ponchet - One of the best experts on this subject based on the ideXlab platform.
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Equilibrium shape of core(Fe)–shell(Au) nanoparticles as a function of the metals volume ratio
Journal of Applied Physics, 2020Co-Authors: Anne Ponchet, Segolene Combettes, Patrizio Benzo, Nathalie Tarrat, Marie-josé Casanove, Magali BenoitAbstract:The equilibrium shape of nanoparticles is investigated to elucidate the various core–shell morphologies observed in a bimetallic system associating two immiscible metals, iron and gold, that crystallize in the bcc and fcc lattices, respectively. Fe–Au core–shell nanoparticles present a crystalline Fe core embedded in a polycrystalline Au shell, with core and shell morphologies both depending on the Au/Fe volume ratio. A model is proposed to calculate the energy of these nanoparticles as a function of the Fe volume, Au/Fe volume ratio, and the core and shell shape, using the density functional theory-computed energy densities of the metal surfaces and of the two possible Au/Fe interfaces. Three driving forces leading to equilibrium shapes were identified: the strong adhesion of Au on Fe, the Minimization of the Au/Fe interface energy that promotes one of the two possible interface types, and the Au surface energy Minimization that promotes a 2D–3D Stranski–Krastanov-like transition of the shell. For a low Au/Fe volume ratio, the wetting is the dominant driving force and leads to the same polyhedral shape for the core and the shell, with an octagonal section. For a large Au/Fe ratio, the surface and interface energy Minimizations can act independently to form an almost cube-shaped Fe core surrounded by six Au pyramids. The experimental nanoparticle shapes are well reproduced by the model, for both low and large Au/Fe volume ratios.
Zhang David - One of the best experts on this subject based on the ideXlab platform.
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A Survey of Sparse Representation: Algorithms and Applications
IEEE Access, 2014Co-Authors: Zhang Zheng, Li Xuelong, Yong Xu, Xuelong Li, Yang Jian, Xu Yong, Jian Yang, Zheng Zhang, Zhang DavidAbstract:Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm Minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: sparse representation with $l_0$-norm Minimization; sparse representation with $l_p$-norm ($0 < p < 1$) Minimization; sparse representation with $l_1$-norm Minimization; sparse representation with $l_{2,1}$-norm Minimization; and sparse representation with $l_2$-norm Minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: greedy strategy approximation; constrained optimization; proximity algorithm-based optimization; and homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. In particular, an experimentally comparative study of these sparse representation algorithms was presented.
Marie-josé Casanove - One of the best experts on this subject based on the ideXlab platform.
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Equilibrium shape of core(Fe)–shell(Au) nanoparticles as a function of the metals volume ratio
Journal of Applied Physics, 2020Co-Authors: Anne Ponchet, Segolene Combettes, Patrizio Benzo, Nathalie Tarrat, Marie-josé Casanove, Magali BenoitAbstract:The equilibrium shape of nanoparticles is investigated to elucidate the various core–shell morphologies observed in a bimetallic system associating two immiscible metals, iron and gold, that crystallize in the bcc and fcc lattices, respectively. Fe–Au core–shell nanoparticles present a crystalline Fe core embedded in a polycrystalline Au shell, with core and shell morphologies both depending on the Au/Fe volume ratio. A model is proposed to calculate the energy of these nanoparticles as a function of the Fe volume, Au/Fe volume ratio, and the core and shell shape, using the density functional theory-computed energy densities of the metal surfaces and of the two possible Au/Fe interfaces. Three driving forces leading to equilibrium shapes were identified: the strong adhesion of Au on Fe, the Minimization of the Au/Fe interface energy that promotes one of the two possible interface types, and the Au surface energy Minimization that promotes a 2D–3D Stranski–Krastanov-like transition of the shell. For a low Au/Fe volume ratio, the wetting is the dominant driving force and leads to the same polyhedral shape for the core and the shell, with an octagonal section. For a large Au/Fe ratio, the surface and interface energy Minimizations can act independently to form an almost cube-shaped Fe core surrounded by six Au pyramids. The experimental nanoparticle shapes are well reproduced by the model, for both low and large Au/Fe volume ratios.
