Tsinghua University

About: Tsinghua University is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 129978 authors who have published 200506 publications receiving 4549561 citations. The organization is also known as: Tsinghua & THU.

Fabrication of Single-Crystalline Silicon Nanowires by Scratching a Silicon Surface with Catalytic Metal Particles†

Abstract: A novel strategy for preparing large-area, oriented silicon nanowire (SiNW) arrays on silicon substrates at near room temperature by localized chemical etching is presented. The strategy is based on metal-induced (either by Ag or Au) excessive local oxidation and dissolution of a silicon substrate in an aqueous fluoride solution. The density and size of the as-prepared SiNWs depend on the distribution of the patterned metal particles on the silicon surface. High-density metal particles facilitate the formation of silicon nanowires. Well-separated, straight nanoholes are dug along the Si block when metal particles are well dispersed with a large space between them. The etching technique is weakly dependent on the orientation and doping type of the silicon wafer. Therefore, SiNWs with desired axial crystallographic orientations and doping characteristics are readily obtained. Detailed scanning electron microscopy observations reveal the formation process of the silicon nanowires, and a reasonable mechanism is proposed on the basis of the electrochemistry of silicon and the experimental results.

Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box

Abstract: Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional distributions used for imputation; and plots for comparing the distributions of observed and imputed data. In addition, we use Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models. Our goal is to have a demonstration package that (a) avoids many of the practical problems that arise with existing multivariate imputation programs, and (b) demonstrates state-of-the-art diagnostics that can be applied more generally and can be incorporated into the software of others.

High-Rate, Ultralong Cycle-Life Lithium/Sulfur Batteries Enabled by Nitrogen-Doped Graphene

Abstract: Nitrogen-doped graphene (NG) is a promising conductive matrix material for fabricating high-performance Li/S batteries. Here we report a simple, low-cost, and scalable method to prepare an additive-free nanocomposite cathode in which sulfur nanoparticles are wrapped inside the NG sheets (S@NG). We show that the Li/S@NG can deliver high specific discharge capacities at high rates, that is, ∼1167 mAh g–1 at 0.2 C, ∼1058 mAh g–1 at 0.5 C, ∼971 mAh g–1 at 1 C, ∼802 mAh g–1 at 2 C, and ∼606 mAh g–1 at 5 C. The cells also demonstrate an ultralong cycle life exceeding 2000 cycles and an extremely low capacity-decay rate (0.028% per cycle), which is among the best performance demonstrated so far for Li/S cells. Furthermore, the S@NG cathode can be cycled with an excellent Coulombic efficiency of above 97% after 2000 cycles. With a high active S content (60%) in the total electrode weight, the S@NG cathode could provide a specific energy that is competitive to the state-of-the-art Li-ion cells even after 2000 cycl...