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: Laser & Catalysis. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

Turkevich in New Robes: Key Questions Answered for the Most Common Gold Nanoparticle Synthesis

Abstract: This contribution provides a comprehensive mechanistic picture of the gold nanoparticle synthesis by citrate reduction of HAuCl4, known as Turkevich method, by addressing five key questions. The synthesis leads to monodisperse final particles as a result of a seed-mediated growth mechanism. In the initial phase of the synthesis, seed particles are formed onto which the residual gold is distributed during the course of reaction. It is shown that this mechanism is a fortunate coincidence created by a favorable interplay of several chemical and physicochemical processes which initiate but also terminate the formation of seed particles and prevent the formation of further particles at later stages of reaction. Since no further particles are formed after seed particle formation, the number of seeds defines the final total particle number and therefore the final size. The gained understanding allows illustrating the influence of reaction conditions on the growth process and thus the final size distribution.

Comparative study of optical phonons in the rhombohedrally distorted perovskites LaAlO 3 and LaMnO 3

Abstract: The Raman and infrared phonons of isostructural rhombohedral ${\mathrm{LaMnO}}_{3}$ and ${\mathrm{LaAlO}}_{3}$ are studied at room temperature. The experimental spectra are compared with the prediction of lattice-dynamical calculations and the lines observed are assigned to definite atomic vibrations. It is shown that the Raman mode of ${A}_{1g}$ symmetry in ${\mathrm{LaAlO}}_{3}$ and ${\mathrm{LaMnO}}_{3}$ (at $123 {\mathrm{cm}}^{\ensuremath{-}1}$ and $236 {\mathrm{cm}}^{\ensuremath{-}1},$ respectively) involves atomic motions that cause the rhombohedral distortion, i.e., it is a ``soft'' mode, and its position could be used as a measure of the degree of the distortion. It is also argued that the broad Raman bands in the high-frequency range of ${\mathrm{LaMnO}}_{3}$ are not proper modes of the rhombohedral $R3\ifmmode\bar\else\textasciimacron\fi{}c$ structure, but are rather induced by the dynamic Jahn-Teller effect.

Deep-neural-network solution of the electronic Schrödinger equation.

Abstract: The electronic Schrodinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrodinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H10, and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient. High-accuracy quantum chemistry methods struggle with a combinatorial explosion of Slater determinants in larger molecular systems, but now a method has been developed that learns electronic wavefunctions with deep neural networks and reaches high accuracy with only a few determinants. The method is applicable to realistic chemical processes such as the automerization of cyclobutadiene.

Static Analysis of Executables for Collaborative Malware Detection on Android

Abstract: Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.