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.

Conjugated polymer-based organic solar cells

Abstract: The need to develop inexpensive renewable energy sources stimulates scientific research for efficient, low-cost photovoltaic devices.1 The organic, polymer-based photovoltaic elements have introduced at least the potential of obtaining cheap and easy methods to produce energy from light.2 The possibility of chemically manipulating the material properties of polymers (plastics) combined with a variety of easy and cheap processing techniques has made polymer-based materials present in almost every aspect of modern society.3 Organic semiconductors have several advantages: (a) lowcost synthesis, and (b) easy manufacture of thin film devices by vacuum evaporation/sublimation or solution cast or printing technologies. Furthermore, organic semiconductor thin films may show high absorption coefficients4 exceeding 105 cm-1, which makes them good chromophores for optoelectronic applications. The electronic band gap of organic semiconductors can be engineered by chemical synthesis for simple color changing of light emitting diodes (LEDs).5 Charge carrier mobilities as high as 10 cm2/V‚s6 made them competitive with amorphous silicon.7 This review is organized as follows. In the first part, we will give a general introduction to the materials, production techniques, working principles, critical parameters, and stability of the organic solar cells. In the second part, we will focus on conjugated polymer/fullerene bulk heterojunction solar cells, mainly on polyphenylenevinylene (PPV) derivatives/(1-(3-methoxycarbonyl) propyl-1-phenyl[6,6]C61) (PCBM) fullerene derivatives and poly(3-hexylthiophene) (P3HT)/PCBM systems. In the third part, we will discuss the alternative approaches such as polymer/polymer solar cells and organic/inorganic hybrid solar cells. In the fourth part, we will suggest possible routes for further improvements and finish with some conclusions. The different papers mentioned in the text have been chosen for didactical purposes and cannot reflect the chronology of the research field nor have a claim of completeness. The further interested reader is referred to the vast amount of quality papers published in this field during the past decade.

Plastic Solar Cells

Abstract: Recent developments in conjugated-polymer-based photovoltaic elements are reviewed. The photophysics of such photoactive devices is based on the photo-induced charge transfer from donor-type semiconducting conjugated polymers to acceptor-type conjugated polymers or acceptor molecules such as Buckminsterfullerene, C60. This photo-induced charge transfer is reversible, ultrafast (within 100 fs) with a quantum efficiency approaching unity, and the charge-separated state is metastable (up to milliseconds at 80 K). Being similar to the first steps in natural photosynthesis, this photo-induced electron transfer leads to a number of potentially interesting applications, which include sensitization of the photoconductivity and photovoltaic phenomena. Examples of photovoltaic architectures are presented and their potential in terrestrial solar energy conversion discussed. Recent progress in the realization of improved photovoltaic elements with 3 % power conversion efficiency is reported.

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Abstract: Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the `Frechet Inception Distance'' (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark.

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Abstract: We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR-10 results and yield the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10% classification error for a single crop, single model network.

Organic solar cells: An overview

Abstract: Organic solar cell research has developed during the past 30 years, but especially in the last decade it has attracted scientific and economic interest triggered by a rapid increase in power conversion efficiencies. This was achieved by the introduction of new materials, improved materials engineering, and more sophisticated device structures. Today, solar power conversion efficiencies in excess of 3% have been accomplished with several device concepts. Though efficiencies of these thin-film organicdevices have not yet reached those of their inorganic counterparts (η ≈ 10–20%); the perspective of cheap production (employing, e.g., roll-to-roll processes) drives the development of organic photovoltaic devices further in a dynamic way. The two competitive production techniques used today are either wet solution processing or dry thermal evaporation of the organic constituents. The field of organic solar cells profited well from the development of light-emitting diodes based on similar technologies, which have entered the market recently. We review here the current status of the field of organic solar cells and discuss different production technologies as well as study the important parameters to improve their performance.