Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Computer science & Thin film. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

Anthracene Based Conjugated Polymers: Correlation between π−π-Stacking Ability, Photophysical Properties, Charge Carrier Mobility, and Photovoltaic Performance

Abstract: This article reports on the synthesis, characterization and properties of a series of anthracene−containing poly(p-phenylene-ethynylene)-alt-poly(p-phenylene-vinylene)s (PPE−PPV) copolymers with general constitutional unit (Ph−C≡C−Anthr−C≡C−Ph−CH═CH−Ph−CH═CH−)n denoted AnE-PV. Solely linear (AnE-PVaa, -ad, -ae) and solely branched (AnE-PVbb) as well as mixed linear and branched (AnE-PVab, -ac, -ba, -cc) alkoxy side chains were grafted to the backbone in order to tune the π−π-stacking ability of the materials. It has been possible to establish a correlation between π−π-stacking ability, absorptive behavior, charge carrier mobility, solar cell active layer nanoscale morphology and resulting photovoltaic performance. Solar cells energy conversion efficiencies between 0.34% and 3.14% were achieved. The best performance was achieved from AnE-PVab showing both stacking ability and highest π−π-stacking distance of 0.386 nm as compared to 0.380 nm for the others. Poorer performance resulted from polymers with no ...

Toxicity Prediction using Deep Learning

Abstract: Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines — and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA and FDA launched the Tox21 Data Challenge within the “Toxicology in the 21st Century” (Tox21) initiative. The goal of this challenge was to assess the performance of computational methods in predicting the toxicity of chemical compounds. State of the art toxicity prediction methods build upon specifically-designed chemical descriptors developed over decades. Though Deep Learning is new to the field and was never applied to toxicity prediction before, it clearly outperformed all other participating methods. In this application paper we show that deep nets automatically learn features resembling well-established toxicophores. In total, our Deep Learning approach won both of the panel-challenges (nuclear receptors and stress response) as well as the overall Grand Challenge, and thereby sets a new standard in tox prediction.