Intrinsic spectral selectivity in ultra-high temperature ceramics for solar applications
Different concepts for solar receiver systems have been developed, and each collector architecture (linear parabolic systems, solar dishes, solar tower plants, etc) demands peculiar system solutions [1]. It is a general rule that the efficiency of solar thermal systems rapidly increases with increasing working temperature. In solar tower plants, a critical parameter for temperature increasing is the receiver, where the whole heliostat field concentrates the collected sunlight. The family of Ultra High Temperature Ceramics (UHTCs) can be interesting for this application because of its unique combination of properties. In fact UHTCs are characterized by some of the highest melting points of any known material (>3900°C for the monocarbides of Ta and Hf), high hardness, good wear resistance, good chemical stability and mechanical strength at high temperatures and high thermal conductivities. Up to now, UHTCs are employed mainly in the aerospace industry for hypersonic vehicles, rocket motor nozzles or atmospheric entry probes capable of the most extreme entry conditions. The ultra-high melting point of UHTCs, together with the unique combination of good thermal conductivity and chemical stability appear intriguing for employing them in high temperature solar furnaces [2].
Processing, mechanical properties and oxidation behavior of TaC and HfC composites containing 15 vol%TaSi2 or MoSi2
Fully dense HfC and TaC-based composites containing 15 vol% TaSi 2 or MoSi 2 were produced by hot pressing at 1750–1900 °C. TaSi 2 enhanced the sinterability of the composites and nearly fully dense materials were obtained at lower temperatures than in the case of MoSi 2 -containing ones. The TaC-based composites performed better than HfC composites at room temperature, showing values of mechanical strength up to 900 MPa and a fracture toughness of 4.7 MPa·m 1/2 . However, preliminary oxidation tests carried out in air at 1600 °C revealed that HfC-based composites have a superior high temperature stability compared to TaC-based materials.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.