Edge chirality determination of graphene by Raman spectroscopy

Raman imaging on the edges of single layer micromechanical cleavage graphene (MCG) was carried out. The intensity of disorder-induced Raman feature (D band at ~1350 cm-1) was found to be correlated to the edge chirality: it is stronger at the armchair edge and weaker at the zigzag edge. This shows that Raman spectroscopy is a reliable and practical method to identify the chirality of graphene edge and to help in determination of the crystal orientation. The determination of graphene chirality is critically important for fundamental study as well as for applications.Comment: 14 pages, 3 figures, 1 tabl

Interaction between graphene and SiO2 surface

With first-principles DFT calculations, the interaction between graphene and SiO2 surface has been analyzed by constructing the different configurations based on {\alpha}-quartz and cristobalite structures. The single layer graphene can stay stably on SiO2 surface is explained based on the general consideration of configuration structures of SiO2 surface. It is also found that the oxygen defect in SiO2 surface can shift the Fermi level of graphene down which opens out the mechanism of hole-doping effect of graphene absorbed on SiO2 surface observed in experiments.Comment: 17 pages, 7 figure

Warm-Starting Fixed-Point Based Control Synthesis

In this work we propose a patching algorithm to incrementally modify controllers, synthesized to satisfy a temporal logic formula, when some of the control actions become unavailable. The main idea of the proposed algorithm is to “warm-start” the synthesis process with an existing fixed-point based controller that has a larger action set. By exploiting the structure of the fixed-point based controllers, our algorithm avoids repeated computations while synthesizing a controller with restricted action set. Moreover, we show that the algorithm is sound and complete, that is, it provides the same guarantees as synthesizing a controller from scratch with the new action set. An example on synthesizing controllers for a simplified walking robot model under ground constraints is used to illustrate the approach. In this application, the ground constraints determine the action set and they might not be known a priori. Therefore it is of interest to quickly modify a controller synthesized for an unconstrained surface, when new constraints are encountered. Our simulations indicate that the proposed approach provides at least 5-times speed-up compared to synthesizing a controller from scratch.DARPAPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146730/1/warm-starting-for-deepblue.pd