Iowa State University

About: Iowa State University is a education organization based out in Ames, Iowa, United States. It is known for research contribution in the topics: Population & Gene. The organization has 50151 authors who have published 107716 publications receiving 3355909 citations. The organization is also known as: Iowa State University of Science and Technology & Iowa State College.

All-metallic three-dimensional photonic crystals with a large infrared bandgap

Abstract: Three-dimensional (3D) metallic crystals are promising photonic bandgap structures: they can possess a large bandgap, new electromagnetic phenomena can be explored, and high-temperature (above 1,000 degrees C) applications may be possible. However, investigation of their photonic bandgap properties is challenging, especially in the infrared and visible spectrum, as metals are dispersive and absorbing in these regions. Studies of metallic photonic crystals have therefore mainly concentrated on microwave and millimetre wavelengths. Difficulties in fabricating 3D metallic crystals present another challenge, although emerging techniques such as self-assembly may help to resolve these problems. Here we report measurements and simulations of a 3D tungsten crystal that has a large photonic bandgap at infrared wavelengths (from about 8 to 20 microm). A very strong attenuation exists in the bandgap, approximately 30 dB per unit cell at 12 microm. These structures also possess other interesting optical properties; a sharp absorption peak is present at the photonic band edge, and a surprisingly large transmission is observed in the allowed band, below 6 microm. We propose that these 3D metallic photonic crystals can be used to integrate various photonic transport phenomena, allowing applications in thermophotovoltaics and blackbody emission.

Varying Coefficient Models

Abstract: In this and the following chapter we consider ways to combine sets of data for the purpose of estimation that may violate a basic assumption of the linear regression model. Specifically, we will study cases when it cannot be assumed that the structural parameters are identical for all observations in a sample of data. Frequently these problems occur when a data set consists of series of observations over time on cross-sectional units. Many of the topics discussed in this chapter can or do apply directly to the pooling of time series and cross-sectional data, but the primary discussion of that topic is contained in Chapter 15. In this chapter we focus on models with parameters that vary in some systematic and/or random way across partitions of the sample data, even from observation to observation.