École Polytechnique Fédérale de Lausanne

About: École Polytechnique Fédérale de Lausanne is a facility organization based out in Lausanne, Switzerland. It is known for research contribution in the topics: Population & Catalysis. The organization has 44041 authors who have published 98296 publications receiving 4372092 citations. The organization is also known as: EPFL & ETHL.

Hole‐Transport Materials for Perovskite Solar Cells

Abstract: The pressure to move towards renewable energy has inspired researchers to look for ideas in photovoltaics that may lead to a major breakthrough. Recently the use of perovskites as a light harvester has lead to stunning progress. The power conversion efficiency of perovskite solar cells is now approaching parity (>22 %) with that of the established technology which took decades to reach this level of performance. The use of a hole transport material (HTM) remains indispensable in perovskite solar cells. Perovskites can conduct holes, but they are present at low levels, and for efficient charge extraction a HTM layer is a prerequisite. Herein we provide an overview of the diverse types of HTM available, from organic to inorganic, in the hope of encouraging further research and the optimization of these materials.

Selectivity of potentiometric ion sensors.

Abstract: Selectivities of solvent polymeric membrane ion-selective electrodes (ISEs) are quantitatively related to equilibria at the interface between the sample and the electrode membrane. However, only correctly determined selectivity coefficients allow accurate predictions of ISE responses to real-world samples. Moreover, they are also required for the optimization of ionophore structures and membrane compositions. Best suited for such purposes are potentiometric selectivity coefficients as defined already in the 1960s. This paper briefly reviews the basic relationships and focuses on possible biases in the determination of selectivity coefficients. The traditional methods to determine selectivity coefficients (separate solution method, fixed interference method) are still the same as those originally proposed by IUPAC in 1976. However, several precautions are needed to obtain meaningful data. For example, errors arise when the response to a weakly interfering ion is also influenced by the primary ion leaching from the membrane. Wrong selectivity coefficients may be also obtained when the interfering agent is highly preferred and the electrode shows counterion interference. Recent advances show how such pitfalls can be avoided. A detailed recipe to determine correct potentiometric selectivity coefficients unaffected by such biases is presented.

Synthesis of functional polymers by post-polymerization modification.

Abstract: Post-polymerization modification is based on the direct polymerization or copolymerization of monomers bearing chemoselective handles that are inert towards the polymerization conditions but can be quantitatively converted in a subsequent step into a broad range of other functional groups. The success of this method is based on the excellent conversions achievable under mild conditions, the excellent functional-group tolerance, and the orthogonality of the post-polymerization modification reactions. This Review surveys different classes of reactive polymer precursors bearing chemoselective handles and discusses issues related to the preparation of these reactive polymers by direct polymerization of appropriately functionalized monomers as well as the post-polymerization modification of these precursors into functional polymers.

Geometric deep learning: going beyond Euclidean data

Abstract: Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field.