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Institution

Technical University of Berlin

EducationBerlin, Germany
About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Quantum dot & Laser. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.


Papers
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Journal ArticleDOI
01 Mar 2009-Energy
TL;DR: In this article, the authors discuss four different approaches developed by the authors for calculating the endogenous part of exergy destruction as well as the approach based on the structural theory, and conclude that all approaches lead to comparable and acceptable results.

321 citations

Journal ArticleDOI
TL;DR: A systematic structural elucidation of the near surface active species of the two remarkably active nickel phosphides Ni12P5 and Ni2P on the basis of extensive analytical, microscopic, and spectroscopic investigations is reported in this paper.
Abstract: A systematic structural elucidation of the near-surface active species of the two remarkably active nickel phosphides Ni12P5 and Ni2P on the basis of extensive analytical, microscopic, and spectroscopic investigations is reported. The latter can serve as complementary efficient electrocatalysts in the hydrogen (HER) versus oxygen evolution reaction (OER) in alkaline media. In the OER Ni12P5 shows enhanced performance over Ni2P due to the higher concentration of nickel in this phase, which enables the formation of an amorphous NiOOH/Ni(OH)2 shell on a modified multiphase with a disordered phosphide/phosphite core. The situation is completely reversed in the HER, where Ni2P displayed a significant improvement in electrocatalytic activity over Ni12P5 owing to a larger concentration of phosphide/phosphate species in the shell. Moreover, the efficiently combined use of the two nickel phosphide phases deposited on nickel foam in overall electrocatalytic water splitting is demonstrated by a strikingly low cell v...

321 citations

Journal ArticleDOI
TL;DR: A gene-brain-behavior pathway that can be altered as a consequence of colony-level selection for quantities of stored food is demonstrated and a variable quantitative trait locus that appears to influence sucrose response thresholds is identified.
Abstract: Honey bee foragers were tested for their proboscis extension response (PER) to water and varying solutions of sucrose. Returning pollen and nectar foragers were collected at the entrance of a colony and were assayed in the laboratory. Pollen foragers had a significantly higher probability of responding to water and to lower concentrations of sucrose. Bees derived from artificially selected high- and low-pollen-hoarding strains were also tested using the proboscis extension assay. Returning foragers were captured and tested for PERs to 30% sucrose. Results demonstrated a genotypic effect on PERs of returning foragers. The PERs of departing high- and low-strain foragers were consistent with those of returning foragers. The PERs were related to nectar and water reward perception of foragers. High strain bees were more likely to return with loads of water and lower concentrations of sucrose than foragers from the low pollen strain. Low-strain bees were more likely to return empty. We identified a previously mapped genomic region that contains a variable quantitative trait locus that appears to influence sucrose response thresholds. These studies demonstrate a gene-brain-behavior pathway that can be altered as a consequence of colony-level selection for quantities of stored food.

321 citations

Journal ArticleDOI
TL;DR: The unique morphology, small nanoparticles stacked upon on another, is proposed to promote C-C coupling reaction selectivity from CO2RR by suppressing HER.
Abstract: In this study, we demonstrate that the initial morphology of nanoparticles can be transformed into small fragmented nanoparticles, which were densely contacted to each other, during electrochemical CO2 reduction reaction (CO2RR). Cu-based nanoparticles were directly grown on a carbon support by using cysteamine immobilization agent, and the synthesized nanoparticle catalyst showed increasing activity during initial CO2RR, doubling Faradaic efficiency of C2H4 production from 27% to 57.3%. The increased C2H4 production activity was related to the morphological transformation over reaction time. Twenty nm cubic Cu2O crystalline particles gradually experienced in situ electrochemical fragmentation into 2-4 nm small particles under the negative potential, and the fragmentation was found to be initiated from the surface of the nanocrystal. Compared to Cu@CuO nanoparticle/C or bulk Cu foil, the fragmented Cu-based NP/C catalyst achieved enhanced C2+ production selectivity, accounting 87% of the total CO2RR products, and suppressed H2 production. In-situ X-ray absorption near edge structure studies showed metallic Cu0 state was observed under CO2RR, but the fragmented nanoparticles were more readily reoxidized at open circuit potential inside of the electrolyte, allowing labile Cu states. The unique morphology, small nanoparticles stacked upon on another, is proposed to promote C-C coupling reaction selectivity from CO2RR by suppressing HER.

321 citations

Book ChapterDOI
06 Sep 2016
TL;DR: This paper proposed an approach to extend layer-wise relevance propagation to neural networks with local renormalization layers, which is a very common product-type nonlinearity in convolutional neural networks.
Abstract: Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e.g. an image, down to relevance scores for the single input dimensions of the sample such as subpixels of an image. While this approach can be applied directly to generalized linear mappings, product type non-linearities are not covered. This paper proposes an approach to extend layer-wise relevance propagation to neural networks with local renormalization layers, which is a very common product-type non-linearity in convolutional neural networks. We evaluate the proposed method for local renormalization layers on the CIFAR-10, Imagenet and MIT Places datasets.

321 citations


Authors

Showing all 27602 results

NameH-indexPapersCitations
Markus Antonietti1761068127235
Jian Li133286387131
Klaus-Robert Müller12976479391
Michael Wagner12435154251
Shi Xue Dou122202874031
Xinchen Wang12034965072
Michael S. Feld11955251968
Jian Liu117209073156
Ary A. Hoffmann11390755354
Stefan Grimme113680105087
David M. Karl11246148702
Lester Packer11275163116
Andreas Heinz108107845002
Horst Weller10545144273
G. Hughes10395746632
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023191
2022650
20213,307
20203,387
20193,105
20182,910