Fabrication of Antireflective Sub-Wavelength Structures on Silicon Nitride Using Nano Cluster Mask for Solar Cell Application

We have developed a simple and scalable approach for fabricating sub-wavelength structures (SWS) on silicon nitride by means of self-assembled nickel nanoparticle masks and inductively coupled plasma (ICP) ion etching. Silicon nitride SWS surfaces with diameter of 160–200 nm and a height of 140–150 nm were obtained. A low reflectivity below 1% was observed over wavelength from 590 to 680 nm. Using the measured reflectivity data in PC1D, the solar cell characteristics has been compared for single layer anti-reflection (SLAR) coatings and SWS and a 0.8% improvement in efficiency has been seen

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response