Johannes Kepler University of Linz

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTConjugated Polymer-Based Organic Solar CellsSerap Günes, Helmut Neugebauer, and Niyazi Serdar SariciftciView Author Information Linz Institute of Organic Solar Cells (LIOS), Physical Chemistry, Johannes Kepler University of Linz, Austria Cite this: Chem. Rev. 2007, 107, 4, 1324–1338Publication Date (Web):April 11, 2007Publication History Received17 September 2006Published online11 April 2007Published inissue 1 April 2007https://pubs.acs.org/doi/10.1021/cr050149zhttps://doi.org/10.1021/cr050149zresearch-articleACS PublicationsCopyright © 2007 American Chemical SocietyRequest reuse permissionsArticle Views66227Altmetric-Citations5692LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Carrier dynamics,Electrical conductivity,Electrodes,Mobility,Solar cells Get e-Alerts

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.

With data from 33 nations, we illustrate the differences between cultures that are tight (have many strong norms and a low tolerance of deviant behavior) versus loose (have weak social norms and a high tolerance of deviant behavior). Tightness-looseness is part of a complex, loosely integrated multilevel system that comprises distal ecological and historical threats (e.g., high population density, resource scarcity, a history of territorial conflict, and disease and environmental threats), broad versus narrow socialization in societal institutions (e.g., autocracy, media regulations), the strength of everyday recurring situations, and micro-level psychological affordances (e.g., prevention self-guides, high regulatory strength, need for structure). This research advances knowledge that can foster cross-cultural understanding in a world of increasing global interdependence and has implications for modeling cultural change.

Using various methods, the size of the shadow economy in 76 developing, transition, and OECD countries is estimated. Average size varies from 12 percent of GDP for OECD countries, to 23 percent for transition countries and 39 percent for developing countries. Increasing taxation and social security contributions combined with rising state regulations are driving forces for the increase of the shadow economy, especially in OECD countries. According to some findings, corruption has a positive impact on the size of the shadow economy, and a growing shadow economy has a negative effect on official GDP growth.

We study whether a depth two neural network can learn another depth two network using gradient descent. Assuming a linear output node, we show that the question of whether gradient descent converges to the target function is equivalent to the following question in electrodynamics: Given k fixed protons in R^d, and k electrons, each moving due to the attractive force from the protons and repulsive force from the remaining electrons, whether at equilibrium all the electrons will be matched up with the protons, up to a permutation. Under the standard electrical force, this follows from the classic Earnshaw's theorem. In our setting, the force is determined by the activation function and the input distribution. Building on this equivalence, we prove the existence of an activation function such that gradient descent learns at least one of the hidden nodes in the target network. Iterating, we show that gradient descent can be used to learn the entire network one node at a time.

We show that the power conversion efficiency of organic photovoltaic devices based on a conjugated polymer/methanofullerene blend is dramatically affected by molecular morphology. By structuring the blend to be a more intimate mixture that contains less phase segregation of methanofullerenes, and simultaneously increasing the degree of interactions between conjugated polymer chains, we have fabricated a device with a power conversion efficiency of 2.5% under AM1.5 illumination. This is a nearly threefold enhancement over previously reported values for such a device, and it approaches what is needed for the practical use of these devices for harvesting energy from sunlight.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

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.

Rigorous evidence identification is essential for systematic reviews and meta-analyses (evidence syntheses) because the sample selection of relevant studies determines a review's outcome, validity, and explanatory power. Yet, the search systems allowing access to this evidence provide varying levels of precision, recall, and reproducibility and also demand different levels of effort. To date, it remains unclear which search systems are most appropriate for evidence synthesis and why. Advice on which search engines and bibliographic databases to choose for systematic searches is limited and lacking systematic, empirical performance assessments. This study investigates and compares the systematic search qualities of 28 widely used academic search systems, including Google Scholar, PubMed, and Web of Science. A novel, query-based method tests how well users are able to interact and retrieve records with each system. The study is the first to show the extent to which search systems can effectively and efficiently perform (Boolean) searches with regards to precision, recall, and reproducibility. We found substantial differences in the performance of search systems, meaning that their usability in systematic searches varies. Indeed, only half of the search systems analyzed and only a few Open Access databases can be recommended for evidence syntheses without adding substantial caveats. Particularly, our findings demonstrate why Google Scholar is inappropriate as principal search system. We call for database owners to recognize the requirements of evidence synthesis and for academic journals to reassess quality requirements for systematic reviews. Our findings aim to support researchers in conducting better searches for better evidence synthesis.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Abstract Efficiencies of organic solar cells based on an interpenetrating network of a conjugated polymer and a fullerene as donor and acceptor materials still need to be improved for commercial use. We have developed a postproduction treatment that improves the performance of solar cells based on poly(3‐hexylthiophene) (P3HT) and [6,6]‐phenyl C 61 ‐butyric acid methyl ester (PCBM) by means of a tempering cycle at elevated temperatures in which an external voltage is simultaneously applied, resulting in a significant increase of the short‐circuit current. Using this postproduction treatment, an enhancement of the short‐circuit current density, I sc , to 8.5 mA cm –2 under illumination with white light at an illumination intensity of 800 W m –2 and an increase in external quantum efficiency (IPCE, incident photon to collected electron efficiency) to 70 % are demonstrated.

A European consensus conference on endometrial carcinoma was held in 2014 to produce multi-disciplinary evidence-based guidelines on selected questions. Given the large body of literature on the management of endometrial carcinoma published since 2014, the European Society of Gynaecological Oncology (ESGO), the European SocieTy for Radiotherapy and Oncology (ESTRO), and the European Society of Pathology (ESP) jointly decided to update these evidence-based guidelines and to cover new topics in order to improve the quality of care for women with endometrial carcinoma across Europe and worldwide.

Abstract. We introduce a new iterative regularization procedure for inverse problems based on the use of Bregman distances, with particular focus on problems arising in image processing. We are motivated by the problem of restoring noisy and blurry images via variational methods by using total variation regularization. We obtain rigorous convergence results and effective stopping criteria for the general procedure. The numerical results for denoising appear to give significant improvement over standard models, and preliminary results for deblurring/denoising are very encouraging.

A series of highly soluble fullerene derivatives with varying acceptor strengths (i.e., first reduction potentials) was synthesized and used as electron acceptors in plastic solar cells. These fullerene derivatives, methanofullerene [6,6]-phenyl C61-butyric acid methyl ester (PCBM), a new azafulleroid, and a ketolactam quasifullerene, show a variation of almost 200 mV in their first reduction potential. The open circuit voltage of the corresponding devices was found to correlate directly with the acceptor strength of the fullerenes, whereas it was rather insensitive to variations of the work function of the negative electrode. These observations are discussed within the concept of Fermi level pinning between fullerenes and metals via surface charges.

Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states. It is interfaced to many widely used electronic-structure codes thanks to its independence from the basis sets representing these Bloch states. In the past few years the development of Wannier90 has transitioned to a community-driven model; this has resulted in a number of new developments that have been recently released in Wannier90 v3.0. In this article we describe these new functionalities, that include the implementation of new features for wannierisation and disentanglement (symmetry-adapted Wannier functions, selectively-localised Wannier functions, selected columns of the density matrix) and the ability to calculate new properties (shift currents and Berry-curvature dipole, and a new interface to many-body perturbation theory); performance improvements, including parallelisation of the core code; enhancements in functionality (support for spinor-valued Wannier functions, more accurate methods to interpolate quantities in the Brillouin zone); improved usability (improved plotting routines, integration with high-throughput automation frameworks), as well as the implementation of modern software engineering practices (unit testing, continuous integration, and automatic source-code documentation). These new features, capabilities, and code development model aim to further sustain and expand the community uptake and range of applicability, that nowadays spans complex and accurate dielectric, electronic, magnetic, optical, topological and transport properties of materials.

Within the different organic photovoltaic devices the conjugated polymer/fullerene bulk heterojunction approach is one of the foci of today's research interest. These devices are highly dependent on the solid state nanoscale morphology of the two components (donor/acceptor) in the photoactive layer. The need for finely phase separated polymer–fullerene blends is expressed by the limited exciton diffusion length present in organic semiconductors. Typical distances that these photo-excitations can travel within a pristine material are around 10–20 nm. In an efficient bulk heterojunction the scale of phase separation is therefore closely related to the respective exciton diffusion lengths of the two materials involved. Once the excitons reach the donor/acceptor interface, the photoinduced charge transfer results in the charge separation. After the charges have been separated they require percolated pathways to the respective charge extracting electrodes in order to supply an external direct current. Thus also an effective charge transport relies on the development of a suitable nanomorphology i.e. bicontinuous interpenetrating phase structures within these blend films. The present feature article combines and summarizes the experimental findings on this nanomorphology–efficiency relationship.

In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures.

This analysis demonstrates the relevance and robustness of the theory of planned behavior in the prediction of business start–up intentions and subsequent behavior based on longitudinal survey data (2011 and 2012; n = 969) from the adult population in Austria and Finland. By doing so, the study addresses two weaknesses in current research: the limited scope of samples used in the majority of prior studies and the scarcity of investigations studying the translation of entrepreneurial intentions into behavior. The paper discusses conceptual and methodological issues related to studying the intention–behavior relationship and outlines avenues for future research.

Colloidal nanocrystals (NCs, i.e., crystalline nanoparticles) have become an important class of materials with great potential for applications ranging from medicine to electronic and optoelectronic devices. Todays strong research focus on NCs has been prompted by the tremendous progress in their synthesis. Impressively narrow size distributions of just a few percent, rational shape-engineering, compositional modulation, electronic doping, and tailored surface chemistries are now feasible for a broad range of inorganic compounds. The performance of inorganic NC-based photovoltaic and light-emitting devices has become competitive to other state-of-the-art materials. Semiconductor NCs hold unique promise for near- and mid-infrared technologies, where very few semiconductor materials are available. On a purely fundamental side, new insights into NC growth, chemical transformations, and self-organization can be gained from rapidly progressing in situ characterization and direct imaging techniques. New phenomena are constantly being discovered in the photophysics of NCs and in the electronic properties of NC solids. In this Nano Focus, we review the state of the art in research on colloidal NCs focusing on the most recent works published in the last 2 years.

A methodology has been developed for the study of molecular recognition at the level of single events and for the localization of sites on biosurfaces, in combining force microscopy with molecular recognition by specific ligands. For this goal, a sensor was designed by covalently linking an antibody (anti-human serum albumin, polyclonal) via a flexible spacer to the tip of a force microscope. This sensor permitted detection of single antibody-antigen recognition events by force signals of unique shape with an unbinding force of 244 +/- 22 pN. Analysis revealed that observed unbinding forces originate from the dissociation of individual Fab fragments from a human serum albumin molecule. The two Fab fragments of the antibody were found to bind independently and with equal probability. The flexible linkage provided the antibody with a 6-nm dynamical reach for binding, rendering binding probability high, 0.5 for encounter times of 60 ms. This permitted fast and reliable detection of antigenic sites during lateral scans with a positional accuracy of 1.5 nm. It is indicated that this methodology has promise for characterizing rate constants and kinetics of molecular recognition complexes and for molecular mapping of biosurfaces such as membranes.