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Institution

Technical University of Denmark

EducationKongens Lyngby, Hovedstaden, Denmark
About: Technical University of Denmark is a education organization based out in Kongens Lyngby, Hovedstaden, Denmark. It is known for research contribution in the topics: Population & Wind power. The organization has 24126 authors who have published 66394 publications receiving 2443649 citations. The organization is also known as: Danmarks Tekniske Universitet & DTU.


Papers
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Journal ArticleDOI
06 Jul 2007-Science
TL;DR: The active site for hydrogen evolution, a reaction catalyzed by precious metals, on nanoparticulate molybdenum disulfide (MoS2) is determined by atomically resolving the surface of this catalyst before measuring electrochemical activity in solution.
Abstract: The identification of the active sites in heterogeneous catalysis requires a combination of surface sensitive methods and reactivity studies. We determined the active site for hydrogen evolution, a reaction catalyzed by precious metals, on nanoparticulate molybdenum disulfide (MoS2) by atomically resolving the surface of this catalyst before measuring electrochemical activity in solution. By preparing MoS2 nanoparticles of different sizes, we systematically varied the distribution of surface sites on MoS2 nanoparticles on Au(111), which we quantified with scanning tunneling microscopy. Electrocatalytic activity measurements for hydrogen evolution correlate linearly with the number of edge sites on the MoS2 catalyst.

4,930 citations

Journal ArticleDOI
TL;DR: The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix that provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data.
Abstract: Motivation: The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. Results: The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.

4,822 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a method to improve our understanding of the behavior of real solids by using calculations of the electronic structure of real molecules and solids in order to improve the search for new and better materials.
Abstract: We would like to improve our understanding of the behaviour of real solids. Why do atoms combine into molecules and solids in the way they do and what is the response of these aggregates of atoms to external fields, stress, temperature, etc. ? Answers to such questions would, for instance, be a useful guide in our, so far mostly empirical, search for new and better materials Stronger materials, materials which corrode less, better catalysts, better magnets, better semi- or superconductors, and, materials with properties unknown today. I expect that future advances in this field will be aided by insight obtained from calculations of the electronic structure.

4,733 citations

Journal ArticleDOI
TL;DR: In this paper, an ab initio method for calculating the electronic structure, electronic transport, and forces acting on the atoms, for atomic scale systems connected to semi-infinite electrodes and with an applied voltage bias.
Abstract: We describe an ab initio method for calculating the electronic structure, electronic transport, and forces acting on the atoms, for atomic scale systems connected to semi-infinite electrodes and with an applied voltage bias. Our method is based on the density-functional theory (DFT) as implemented in the well tested SIESTA approach (which uses nonlocal norm-conserving pseudopotentials to describe the effect of the core electrons, and linear combination of finite-range numerical atomic orbitals to describe the valence states). We fully deal with the atomistic structure of the whole system, treating both the contact and the electrodes on the same footing. The effect of the finite bias (including self-consistency and the solution of the electrostatic problem) is taken into account using nonequilibrium Green's functions. We relate the nonequilibrium Green's function expressions to the more transparent scheme involving the scattering states. As an illustration, the method is applied to three systems where we are able to compare our results to earlier ab initio DFT calculations or experiments, and we point out differences between this method and existing schemes. The systems considered are: (i) single atom carbon wires connected to aluminum electrodes with extended or finite cross section, (ii) single atom gold wires, and finally (iii) large carbon nanotube systems with point defects.

4,674 citations

Journal ArticleDOI
TL;DR: A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed and it is estimated that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%.

4,268 citations


Authors

Showing all 24555 results

NameH-indexPapersCitations
Peer Bork206697245427
Jens K. Nørskov184706146151
Jens Nielsen1491752104005
Bernhard O. Palsson14783185051
Jian Yang1421818111166
Kim Overvad139119686018
Bernard Henrissat139593100002
Torben Jørgensen13588386822
Joel N. Hirschhorn133431101061
John W. Hutchinson12941974747
Robert J. Cava125104271819
Robert A. Harrington12478968023
Hans Ulrik Nørgaard-Nielsen12429584595
M. Linden-Vørnle12023580049
Allan Hornstrup11832883519
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023252
2022714
20214,533
20204,533
20193,792
20183,665