Technical University of Berlin

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.

Exceptional size-dependent activity enhancement in the electroreduction of CO2 over Au nanoparticles.

Abstract: The electrocatalytic reduction of CO2 to industrial chemicals and fuels is a promising pathway to sustainable electrical energy storage and to an artificial carbon cycle, but it is currently hindered by the low energy efficiency and low activity displayed by traditional electrode materials. We report here the size-dependent catalytic activity of micelle-synthesized Au nanoparticles (NPs) in the size range of ∼1–8 nm for the electroreduction of CO2 to CO in 0.1 M KHCO3. A drastic increase in current density was observed with decreasing NP size, along with a decrease in Faradaic selectivity toward CO. Density functional theory calculations showed that these trends are related to the increase in the number of low-coordinated sites on small NPs, which favor the evolution of H2 over CO2 reduction to CO. We show here that the H2/CO product ratio can be specifically tailored for different industrial processes by tuning the size of the catalyst particles.

Precipitation seasonality and variability over the Tibetan plateau as resolved by the High Asia reanalysis

Abstract: Because of the scarcity of meteorological observations, the precipitation climate on the Tibetan Plateau and surrounding regions (TP) has been insufficiently documented so far. In this study, the characteristics and basic features of precipitation on the TP during an 11-yr period (2001–11) are described on monthly-to-annual time scales. For this purpose, a new high-resolution atmospheric dataset is analyzed, the High Asia Reanalysis (HAR), generated by dynamical downscaling of global analysis data using the Weather Research and Forecasting (WRF) model. The HAR precipitation data at 30- and 10-km resolutions are compared with both rain gauge observations and satellite-based precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM). It is found that the HAR reproduces previously reported spatial patterns and seasonality of precipitation and that the high-resolution data add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation. It is demonstrat...

Covariate Shift by Kernel Mean Matching

Abstract: This chapter contains sections titled: Introduction, Sample Reweighting, Distribution Matching, Risk Estimates, The Connection to Single Class Support Vector Machines, Experiments, Conclusion, Appendix: Proofs

Mesoporous nitrogen-doped carbon for the electrocatalytic synthesis of hydrogen peroxide.

Abstract: Mesoporous nitrogen-doped carbon derived from the ionic liquid N-butyl-3-methylpyridinium dicyanamide is a highly active, cheap, and selective metal-free catalyst for the electrochemical synthesis of hydrogen peroxide that has the potential for use in a safe, sustainable, and cheap flow-reactor-based method for H(2)O(2) production.

SchNet - a deep learning architecture for molecules and materials

Abstract: Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics.