Inspired by rich physics and functionalities of graphenes, scientists have taken an intensive interest in two-dimensional (2D) crystals of h-BN (analogue of graphite, so-called "white" graphite). Recent calculations have predicted the exciting potentials of BN nanoribbons in spintronics due to tunable magnetic and electrical properties; however no experimental evidence has been provided since fabrication of such ribbons remains a challenge. Here, we show that few- and single-layered BN nanoribbons, mostly terminated with zigzag edges, can be produced under unwrapping multiwalled BN nanotubes through plasma etching. The interesting stepwise unwrapping and intermediate states were observed and analyzed. Opposed to insulating primal tubes, the nanoribbons become semiconducting due to doping-like conducting edge states and vacancy defects, as revealed by structural analyses and ab initio simulations. This study paves the way for BN nanoribbon production and usage as functional semiconductors with a wide range of applications in optoelectronics and spintronics.
The Chinese financial aid system intends to increase the affordability of postsecondary education and provide access to college for disadvantaged students. However, the research base for access to aid in China is extremely thin. Using data from a large cross-sectional survey in Beijing, this study found that attending selective institutions with high-ability peers was positively correlated with the amount of aid awarded and the probability of receiving aid. Female students, students with college-educated fathers, and students from poorer households were expected to receive more aid. Junior and senior students along with more able individuals in science-related majors obtained significantly more financial assistance.
This paper proposes a Bayesian generalized linear multilevel model with a pth-order autoregressive error process to analyze unbalanced binary time-series cross-sectional (TSCS) data. The model specification is motivated by the generic TSCS data structure and is intended to handle the associated inefficiency and endogeneity problems. It accommodates heterogeneity across units and between time periods in the form of random intercepts and random-effect coefficients. At the same time, its pth-order autoregressive error process, employed either by itself or in concert with other dynamic methods, adequately corrects serial correlation and improves statistical inference and forecasting. With a stationarity restriction on the error process, the model can also be used as a residual-based cointegration test on discrete TSCS data. This is especially valuable because cointegration testing on discrete TSCS data is methodologically challenging and rarely conducted in practice. To handle the estimation difficulties, I developed an efficient Markov chain Monte Carlo (MCMC) algorithm by orthogonalizing the error term with the Cholesky decomposition and adding an auxiliary variable. The parameter expansion method, that is, partial group move–multigrid Monte Carlo updating (PGM-MGMC), is employed to further improve MCMC mixing and speed up convergence. The paper also provides a computational scheme to approximate the Bayes’s factor for the purposes of serial correlation diagnostics, lag order determination, and variable selection. Simulated and empirical examples are used to assess the model and techniques.