We investigated a simple but effective method to precisely control the desired number of graphene layers on the Ni x Cu 1Àx alloy substrates by thermal chemical vapor deposition. Our method could be utilized to precisely control the number of graphene layers without altering growth conditions such as growth temperature and the cooling rate.Graphene, a two-dimensional nanostructure of sp 2 -bonded carbon atoms, has attracted worldwide interest owing to its novel and unique properties such as its high charge mobility, quantum Hall effect, high optical transparency, exibility, and electrical and thermal conductivities. 1-5 However, the realization of graphene-based optoelectronic, electronic, and chemical devices requires the development of a reproducible, large-scale method to produce single-or few-layer graphene lms with high crystalline quality. In particular, it is well recognized that one of the most promising application areas of graphene is transparent electrodes for solar cells or touchscreen displays. For these applications, precise control of graphene layer growth is essential to achieve uniform electrical and optical properties. Accordingly, intensive efforts have been devoted to developing an economic and reproducible method allowing the synthesis of graphene with the appropriate quality. Since graphene mechanically exfoliated from graphite akes was introduced by Geim et al. in 2004, many graphene synthesis methods have been demonstrated such as chemical exfoliation from bulk graphite powders, chemical reduction from graphene oxides, and chemical vapor deposition. 3,6-11 Among these, chemical
A new localized and computationally efficient approach is presented for shift/space-variant image restoration. Unlike conventional approaches, it models shift-variant blurring in a completely local form based on the recently proposed Rao Transform (RT). RT facilitates almost exact inversion of the blurring process locally and permits very fine-grain parallel implementation. The new approach naturally exploits the spatial locality of blurring kernels and smoothness of underlying focused images. It formulates the deblurring problem in terms of local parameters that are less correlated than raw image data. It is a fundamental advance that is general and not limited to any specific form of the blurring kernel such as a Gaussian. It has significant theoretical and computational advantages in comparison with conventional approaches such as those based on Singular Value Decomposition of blurring kernel matrices. Experimental results are presented for both synthetic and real image data. This approach is also relevant to solving integral equations.
This paper introduces a new rescue robot consisting of dual-manipulator and variable configuration mobile platform for multi-purpose such as casualty extraction and hazardous goods transport. A specific rescue motion strategy using a whole-body is suggested to tackle characteristics of the robot configuration and balancing issue. In order to take into account safety and stability of the robot during the rescue motions, some restrictions are reflected into redundant domain of the robot with different priority. For stable motion control in various scenarios, a singularityrobust inverse kinematics is adopted and modified to induce smoother robot movement. The robustness of the control approach is checked numerically by comparing other method and experiments for the rescue motion strategy are carried out by using a small-scaled simulator in place of the rescue robot under development.
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