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We review thermal properties of graphene and few-layer graphene, and discuss applications of these materials in thermal management of advanced electronics. The intrinsic thermal conductivity of graphene - among the highest of known materials - is dominated by phonons near the room temperature. The examples of thermal management applications include the few-layer graphene heat spreaders integrated near the heat generating areas of the high-power density transistors. It has been demonstrated that few-layer graphene heat spreaders can lower the hot-spot temperature during device operation resulting in improved performance and reliability of the devices., Comment: 31 pages, 8 figures

Surface anti-reflectance and passivation properties of the Al2O3 films deposited on crystalline Si substrates by atomic layer deposition are investigated. Textured Si with 100 nm Al2O3 shows a very low average reflectance of ~2.8 %. Both p-type and n-type Si wafers are well passivated by Al2O3 films. The maximal minority carrier lifetimes are improved from ~10 {\mu}s before Al2O3 passivation to above 3 ms for both p-type and n-type Si after Al2O3 passivation layer deposition and annealing at an appropriate temperature. Our results indicate the dual functions of anti-reflectance and surface passivation in c-Si solar cell applications.

Self-heating is a severe problem for high-power GaN electronic and optoelectronic devices. Various thermal management solutions, e.g. flip-chip bonding or composite substrates have been attempted. However, temperature rise still limits applications of the nitride-based technology. Here we demonstrate that thermal management of GaN transistors can be substantially improved via introduction of the alternative heat-escaping channels implemented with few-layer graphene - an excellent heat conductor. We have transferred few-layer graphene to AlGaN/GaN heterostructure field-effect transistors on SiC substrates to form the "graphene-graphite quilts" - lateral heat spreaders, which remove heat from the channel regions. Using the micro-Raman spectroscopy for in-situ monitoring we have shown that temperature can be lowered by as much as ~ 20oC in such devices operating at ~13-W/mm power density. The simulations suggest that the efficiency of the "graphene quilts" can be made even higher in GaN devices on thermally resistive sapphire substrates and in the designs with the closely located heat sinks. Our results open a novel application niche for few-layer graphene in high-power electronics., Comment: 13 pages; 4 display items

Using the requisition papers of Chinese Nature Science Basic Research in management and information department, we construct the weighted network of research areas({\bf WRAN}) represented by the subject codes. In WRAN, two research areas are considered connected if they have been filled in at least one requisition paper. The edge weight is defined as the number of requisition papers which have filled in the same pairs of codes. The node strength is defined as the number of requisition papers which have filled in this code, including the papers which have filled in it only. Here we study a variety of nonlocal statistics for these networks, such as typical distances between research areas through the network, and measures of centrality such as betweenness. These statistics characteristics can illuminate the global development trend of Chinese scientific study, it is also helpful to adjust the code system to reflect the real status more accurately. Finally, we present a plausible model for the formation and structure of networks with the observed properties., Comment: 8 pages, 14 figures

Using Memory Tabu Search(MTS) algorithm, we investigate the relationship between structural characters and synchronizability of scale-free networks by maximizing and minimizing the ratio $Q$ of the eigenvalues of the coupling matrix by edge-intercrossing procedures. The numerical results indicate that clustering coefficient $C$, maximal betweenness $B_{max}$ are two most important factors to scale-free network synchronizability, and assortative coefficient $r$ and average distance $D$ are the secondary ones. Moreover, the average degree $$ affects the relationship between above structural characters and synchronizability of scale-free networks, and the minimal $Q$ decreases when $$ increases., Comment: 4 pages, 3 figures

In this paper, we propose a self-learning mutual selection model to characterize weighted evolving networks. By introducing the self-learning probability $p$ and the general mutual selection mechanism, which is controlled by the parameter $m$, the model can reproduce scale-free distributions of degree, weight and strength, as found in many real systems. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$, depending on the parameters $m$ and $p$. The model can unify the characterization of both assortative and disassortative weighted networks. Also, we find that self-learning may contribute to the assortative mixing of social networks., Comment: 5 pages, 5 figures

In this paper, a simply rule that generates scale-free networks with very large clustering coefficient and very small average distance is presented. These networks are called {\bf Multistage Random Growing Networks}(MRGN) as the adding process of a new node to the network is composed of two stages. The analytic results of power-law exponent $\gamma=3$ and clustering coefficient $C=0.81$ are obtained, which agree with the simulation results approximately. In addition, the average distance of the networks increases logarithmical with the number of the network vertices is proved analytically. Since many real-life networks are both scale-free and small-world networks, MRGN may perform well in mimicking reality., Comment: 3 figures, 4 pages

In this paper, a directed network model for world-wide web is presented. The out-degree of the added nodes are supposed to be scale-free and its mean value is $m$. This model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, the in-degree distribution obeys the power-law form with the exponent $\gamma=2+1/m$, depending on the average out-degree. This finding is supported by the empirical data, which has not been emphasized by the previous studies on directed networks., Comment: 5 pages, 5 eps figures

It has been found that the networks with scale-free distribution are very resilient to random failures. The purpose of this work is to determine the network design guideline which maximize the network robustness to random failures with the average number of links per node of the network is constant. The optimal value of the distribution exponent and the minimum connectivity to different network size are given in this paper. Finally, the optimization strategy how to improve the evolving network robustness is given., Comment: 6 pages, 1 figure

The scale-fee networks, having connectivity distribution $P(k)\sim k^{-\alpha}$ (where $k$ is the site connectivity), is very resilient to random failures but fragile to intentional attack. The purpose of this paper is to find the network design guideline which can make the robustness of the network to both random failures and intentional attack maximum while keeping the average connectivity $$ per node constant. We find that when $=3$ the robustness of the scale-free networks reach its maximum value if the minimal connectivity $m=1$, but when $$ is larger than four, the networks will become more robust to random failures and targeted attacks as the minimal connectivity $m$ gets larger.