Ultrafast holographic nanopatterning of biocatalytically formed silica

Diatoms are of interest to the materials research community because of their ability to create highly complex and intricate silica structures under physiological conditions: what these single-cell organisms accomplish so elegantly in nature requires extreme laboratory conditions to duplicate-this is true for even the simplest of structures. Following the identification of polycationic peptides from the diatom Cylindrotheca fusiformis, simple silica nanospheres can now be synthesized in vitro from silanes at nearly neutral pH and at ambient temperatures and pressures. Here we describe a method for creating a hybrid organic/inorganic ordered nanostructure of silica spheres through the incorporation of a polycationic peptide (derived from the C. fusiformis silaffin-1 protein) into a polymer hologram created by two-photon-induced photopolymerization. When these peptide nanopatterned holographic structures are exposed to a silicic acid, an ordered array of silica nanospheres is deposited onto the clear polymer substrate. These structures exhibit a nearly fifty-fold increase in diffraction efficiency over a comparable polymer hologram without silica. This approach, combining the ease of processability of an organic polymer with the improved mechanical and optical properties of an inorganic material, could be of practical use for the fabrication of photonic devices.     

A new Markov regime-switching count time series approach for forecasting initial public offering volumes and detecting issue cycles

This study proposes a novel Markov regime-switching negative binomial generalized autoregressive conditional heteroskedasticity model for analyzing count data time series. We develop a likelihood-based method for parameter estimation and give the one-step-ahead forecasting algorithms for the mean, variance, and quantiles. An empirical analysis of both the U.S. initial public offering (IPO) and Chinese A-share IPO markets indicates that our method is very efficient in forecasting monthly IPO volumes and detecting hot/cold issue markets. The first-day IPO return is positively correlated with the IPO volume in a hot issue market but negatively correlated with the IPO volume in a cold issue market, in both the U.S. and Chinese IPO markets. However, the average first-day return in the previous hot issue market has a significant positive impact on the current IPO volume for only the U.S. IPO market. Our approach helps to more accurately model and understand the behavior of hot/cold IPO issue markets.