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: Thin film & Quantum dot. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

Colloidal HgTe nanocrystals with widely tunable narrow band gap energies : From telecommunications to molecular vibrations

Abstract: A convenient, aqueous-based synthesis of stable HgTe nanocrystals with widely size-tunable room temperature emission between wavelengths of 1.2 to 3.7 μm is demonstrated. By the choice of the thiols, applied as stabilizers, we optimized the growth dynamics, the luminescence quantum yields (up to 40%), and a ligand-exchange procedure, required to transfer the nanocrystals from water to nonpolar organic solvents. The latter is greatly improved and facilitated by the use of mercaptoethylamine as initial stabilizer. The possibility to tune the HgTe nanocrystal sizes from 3 to 12 nm and to control their surface functionalities (hydrophobic and hydrophilic) makes them very promising for the development of infrared optical devices, emitting in the wavelength region between the telecommunications and the molecular vibrations.

Flexible Conjugated Polymer-Based Plastic Solar Cells: From Basics to Applications

Abstract: Triggered by the outstanding worldwide growth of the photovoltaic market as well as by the need of alternative energy sources in future, organic solar cells are the object of vivid interest from both industrial and academic sides. Based on the semiconducting properties of organic conjugated macromolecules, these devices possess the potential to be processed by common printing techniques. Besides being easily upscalable on rigid as well as on flexible substrates, they open the route of roll-to-roll production of low cost renewable energy sources. Today, up to 5% power conversion efficiencies are reported in this kind of plastic solar cells.

Model-Based Clustering of Multiple Time Series

Abstract: We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest estimating the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast estimation into the Bayesian framework and use Markov chain Monte Carlo simulation methods. We discuss model identification and base model selection on marginal likelihoods. A simulation study documents the efficiency gains in estimation and forecasting that are realized when appropriately grouping the time series of a panel. Two economic applications illustrate the usefulness of the method in analyzing also extensions to Markov switching within clusters and heterogeneity within clusters, respectively.