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.

From Melamine-Cyanuric Acid Supramolecular Aggregates to Carbon Nitride Hollow Spheres

Abstract: Graphitic carbon nitride (g-CN) is a promising heterogeneous metal-free catalyst for organic photosynthesis, solar energy conversion, and photodegradation of pollutants. Its catalytic performance is easily adjustable by modifying texture, optical, and electronic properties via nanocasting, doping, and copolymerization. However, simultaneous optimization has yet to be achieved. Here, a facile synthesis of mesoporous g-CN using molecular cooperative assembly between triazine molecules is reported. Flower-like, layered spherical aggregates of melamine cyanuric acid complex (MCA) are formed by precipitation from equimolecular mixtures in dimethyl sulfoxide (DMSO). Thermal polycondensation of MCA under nitrogen at 550 °C produces mesoporous hollow spheres comprised of tri-s-triazine based g-CN nanosheets (MCA-CN) with the composition of C3N4.14H1.98. The layered structure succeeded from MCA induces stronger optical absorption, widens the bandgap by 0.16 eV, and increases the lifetime of photoexcited charge carriers by twice compared to that of the bulk g-CN, while the chemical structure remains similar to that of the bulk g-CN. As a result of these simultaneous modifications, the photodegradation kinetics of rhodamine B on the catalyst surface can be improved by 10 times.

The emergence and prospects of deep-ultraviolet light-emitting diode technologies

Abstract: By alloying GaN with AlN the emission of AlGaN light-emitting diodes can be tuned to cover almost the entire ultraviolet spectral range (210–400 nm), making ultraviolet light-emitting diodes perfectly suited to applications across a wide number of fields, whether biological, environmental, industrial or medical. However, technical developments notwithstanding, deep-ultraviolet light-emitting diodes still exhibit relatively low external quantum efficiencies because of properties intrinsic to aluminium-rich group III nitride materials. Here, we review recent progress in the development of AlGaN-based deep-ultraviolet light-emitting devices. We also describe the key obstacles to enhancing their efficiency and how to improve their performance in terms of defect density, carrier-injection efficiency, light extraction efficiency and heat dissipation. This Review covers recent progress in AlGaN-based deep-ultraviolet light-emitting devices. The key technologies of how to improve their performance, carrier-injection efficiency, light extraction efficiency and heat dissipation are discussed.

The Hybrid BCI

Abstract: Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a "brain switch". For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system.

Automatic analysis of malware behavior using machine learning

Abstract: Malicious software - so called malware - poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffective, such that millions of hosts in the Internet are infected with malware in the form of computer viruses, Internet worms and Trojan horses. While obfuscation and polymorphism employed by malware largely impede detection at file level, the dynamic analysis of malware binaries during run-time provides an instrument for characterizing and defending against the threat of malicious software. In this article, we propose a framework for the automatic analysis of malware behavior using machine learning. The framework allows for automatically identifying novel classes of malware with similar behavior (clustering) and assigning unknown malware to these discovered classes (classification). Based on both, clustering and classification, we propose an incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis. The incremental analysis significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.