Institution
Australian Nuclear Science and Technology Organisation
Government•Sydney, New South Wales, Australia•
About: Australian Nuclear Science and Technology Organisation is a government organization based out in Sydney, New South Wales, Australia. It is known for research contribution in the topics: Neutron diffraction & Neutron scattering. The organization has 2189 authors who have published 6430 publications receiving 165214 citations. The organization is also known as: ANSTO.
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University of Jyväskylä1, University of California, Los Angeles2, California Polytechnic State University3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, Brigham Young University12, University of Texas at Austin13, Universidade Federal de Minas Gerais14, Google15
TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Abstract: SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.
12,774 citations
University of Jyväskylä1, University of California, Los Angeles2, California Polytechnic State University3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, University of Texas at Austin12, Brigham Young University13, Universidade Federal de Minas Gerais14, Google15
TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
6,244 citations
TL;DR: The Southern Hemisphere SHCal04 radiocarbon calibration curve has been updated with the addition of new data sets extending measurements to 2145 cal BP and including the ANSTO Younger Dryas Huon pine data set as mentioned in this paper.
Abstract: The Southern Hemisphere SHCal04 radiocarbon calibration curve has been updated with the addition of new data sets extending measurements to 2145 cal BP and including the ANSTO Younger Dryas Huon pine data set. Outside the range of measured data, the curve is based upon the Northern Hemisphere data sets as presented in IntCal13, with an interhemispheric offset averaging 43 ± 23 yr modeled by an autoregressive process to represent the short-term correlations in the offset. DOI: 10.2458/azu_js_rc.55.16783
1,596 citations
TL;DR: In this paper, the metal-organic framework Fe2(dobdc) was demonstrated to exhibit excellent performance characteristics for separation of ethylene/ethane and propylene/propane mixtures at 318 kelvin.
Abstract: The energy costs associated with large-scale industrial separation of light hydrocarbons by cryogenic distillation could potentially be lowered through development of selective solid adsorbents that operate at higher temperatures. Here, the metal-organic framework Fe2(dobdc) (dobdc4- : 2,5-dioxido-1,4-benzenedicarboxylate) is demonstrated to exhibit excellent performance characteristics for separation of ethylene/ethane and propylene/propane mixtures at 318 kelvin. Breakthrough data obtained for these mixtures provide experimental validation of simulations, which in turn predict high selectivities and capacities of this material for the fractionation of methane/ethane/ethylene/acetylene mixtures, removal of acetylene impurities from ethylene, and membrane-based olefin/paraffin separations. Neutron powder diffraction data confirm a side-on coordination of acetylene, ethylene, and propylene at the iron(II) centers, while also providing solid-state structural characterization of the much weaker interactions of ethane and propane with the metal.
1,470 citations
TL;DR: The direct chemical synthesis of carbon nanosheets in gram-scale quantities in a bottom-up approach based on the common laboratory reagents ethanol and sodium is reported, yielding a fused array of graphene sheets that are dispersed by mild sonication.
Abstract: Carbon nanostructures have emerged as likely candidates for a wide range of applications, driving research into novel synthetic techniques to produce nanotubes, graphene and other carbon-based materials. Single sheets of pristine graphene have been isolated from bulk graphite in small amounts by micromechanical cleavage1, and larger amounts of chemically modified graphene sheets have been produced by a number of approaches2,3,4,5,6,7. Both of these techniques make use of highly oriented pyrolitic graphite as a starting material and involve labour-intensive preparations. Here, we report the direct chemical synthesis of carbon nanosheets in gram-scale quantities in a bottom-up approach based on the common laboratory reagents ethanol and sodium, which are reacted to give an intermediate solid that is then pyrolized, yielding a fused array of graphene sheets that are dispersed by mild sonication. The ability to produce bulk graphene samples from non-graphitic precursors with a scalable, low-cost approach should take us a step closer to real-world applications of graphene. Most techniques for producing graphene use graphite as a starting material and are labour-intensive. The direct chemical synthesis of carbon nanosheets in gram-scale quantities from the common laboratory reagents ethanol and sodium has now been demonstrated. The ability to produce bulk graphene samples from non-graphitic precursors with a scalable, low-cost approach should take us a step closer to real-world applications of graphene.
1,031 citations
Authors
Showing all 2201 results
Name | H-index | Papers | Citations |
---|---|---|---|
Kevin Varvell | 138 | 1325 | 93740 |
Gordon G. Wallace | 114 | 1267 | 69095 |
Gao Qing Lu | 108 | 546 | 53914 |
Zaiping Guo | 95 | 516 | 32390 |
Michael V. Swain | 91 | 739 | 31167 |
J. Justin Gooding | 81 | 610 | 27971 |
Andy Baker | 80 | 400 | 24533 |
Bruce G. Robinson | 79 | 405 | 30145 |
Zhi Ping Xu | 77 | 538 | 25653 |
David Clark | 73 | 652 | 24857 |
MacDonald J. Christie | 70 | 227 | 18207 |
David M. J. S. Bowman | 70 | 396 | 21976 |
Xungai Wang | 68 | 675 | 19654 |
Andrew J. Pitman | 65 | 260 | 19308 |
Arnan Mitchell | 64 | 597 | 14059 |