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Institution

United States Department of Energy

GovernmentWashington D.C., District of Columbia, United States
About: United States Department of Energy is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Coal & Catalysis. The organization has 13656 authors who have published 14177 publications receiving 556962 citations. The organization is also known as: DOE & Department of Energy.
Topics: Coal, Catalysis, Combustion, Oxide, Hydrogen


Papers
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Journal ArticleDOI
TL;DR: In this paper, the TGA of magnetite (Fe3O4) oxidation was conducted at temperatures ranging from 750 to 900 °C over 10 oxidation cycles and the rate of oxidation was determined by the oxygen weight gain.
Abstract: Thermogravimetric analysis (TGA) of magnetite (Fe3O4) oxidation was conducted at temperatures ranging from 750 to 900 °C over 10 oxidation cycles. Oxidation experiments were carried out in a continuous stream of air for period of 30 min. The oxidized magnetite (Fe3O4), which resulted in formation of hematite (Fe2O3), was then reduced by using continuous stream of CO (5% and 10%) with N2 balance. The rate of oxidation was determined by the oxygen weight gain. Analysis of the data indicated that the oxidation behavior followed a two-stage process. The initial oxidation, which was very fast, took place in 2 min and was described using nucleation and growth processes with a low activation energy of about 4.21 ± 0.45 kJ/mol. As the reaction developed within the surface, oxygen transport through the product layer become the rate-controlling step with activation energy of 53.58 ± 3.56 kJ/mol.

132 citations

Journal ArticleDOI
TL;DR: The proposed PP method is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest while utilizing a projection index to explore projections of interestingness.
Abstract: The authors present a projection pursuit (PP) approach to target detection. Unlike most of developed target detection algorithms that require statistical models such as linear mixture, the proposed PP is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest. It utilizes a projection index to explore projections of interestingness. For target detection applications in hyperspectral imagery, an interesting structure of an image scene is the one caused by man-made targets in a large unknown background. Such targets can be viewed as anomalies in an image scene due to the fact that their size is relatively small compared to their background surroundings. As a result, detecting small targets in an unknown image scene is reduced to finding the outliers of background distributions. It is known that "skewness," is defined by normalized third moment of the sample distribution, measures the asymmetry of the distribution and "kurtosis" is defined by normalized fourth moment of the sample distribution measures the flatness of the distribution. They both are susceptible to outliers. So, using skewness and kurtosis as a base to design a projection index may be effective for target detection. In order to find an optimal projection index, an evolutionary algorithm is also developed to avoid trapping local optima. The hyperspectral image experiments show that the proposed PP method provides an effective means for target detection.

132 citations

Journal ArticleDOI
TL;DR: In this article, a new anti-reflection coating structure based on the use of Herpin equivalent layers is presented, which requires no additional optical material development and characterization because no new optical materials are necessary.

132 citations

Journal ArticleDOI
TL;DR: The results of the India National Gas Hydrate Program Expedition 02 (NGHP-02) have confirmed the presence of extensive sand-rich depositional systems throughout the deepwater portions of the Krishna-Godavari and Mahanadi Basins as discussed by the authors.

132 citations

Journal ArticleDOI
TL;DR: An atomistic cluster alignment method is developed to identify and characterize the local atomic structural order in liquids and glasses as mentioned in this paper, which can detect the presence of any type of local order in the system and quantify the structural similarity between a given set of templates and the aligned clusters in a systematic and unbiased manner.
Abstract: An atomistic cluster alignment method is developed to identify and characterize the local atomic structural order in liquids and glasses. With the ``order mining'' idea for structurally disordered systems, the method can detect the presence of any type of local order in the system and can quantify the structural similarity between a given set of templates and the aligned clusters in a systematic and unbiased manner. Moreover, population analysis can also be carried out for various types of clusters in the system. The advantages of the method in comparison with other previously developed analysis methods are illustrated by performing the structural analysis for four prototype systems (i.e., pure Al, pure Zr, ${\text{Zr}}_{35}{\text{Cu}}_{65}$, and ${\text{Zr}}_{36}{\text{Ni}}_{64}$). The results show that the cluster alignment method can identify various types of short-range orders (SROs) in these systems correctly while some of these SROs are difficult to capture by most of the currently available analysis methods (e.g., Voronoi tessellation method). Such a full three-dimensional atomistic analysis method is generic and can be applied to describe the magnitude and nature of noncrystalline ordering in many disordered systems.

132 citations


Authors

Showing all 13660 results

NameH-indexPapersCitations
Martin White1962038232387
Paul G. Richardson1831533155912
Jie Zhang1784857221720
Krzysztof Matyjaszewski1691431128585
Yang Gao1682047146301
David Eisenberg156697112460
Marvin Johnson1491827119520
Carlos Escobar148118495346
Joshua A. Frieman144609109562
Paul Jackson141137293464
Greg Landsberg1411709109814
J. Conway1401692105213
Pushpalatha C Bhat1391587105044
Julian Borrill139387102906
Cecilia Elena Gerber1381727106984
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20233
202223
2021633
2020601
2019654
2018598