Heralded photonic interaction between distant single ions

We establish heralded interaction between two remotely trapped single 40Ca+ ions through the exchange of single photons. In the sender ion, we release single photons with controlled temporal shape on the P_3/2 to D_5/2 transition and transmit them to the distant receiver ion. Individual absorption events in the receiver ion are detected by quantum jumps. For continuously generated photons, the absorption reduces significantly the lifetime of the long-lived D_5/2 state. For triggered single-photon transmission, we observe coincidence between the emission at the sender and quantum jump events at the receiver.Comment: 5 pages, 4 figures. v2: number on p. 3, bottom, correcte

alkaline earth cations influence catalytic activity in a photosystem II-like fashion

In reaction sequences for light driven water-splitting into H2 and O2, water- oxidation is a crucial reaction step. In vivo, the process is catalysed within a photoenzyme called photosystem II (PSII) by a μ-oxido CaMn4 cluster, the oxygen-evolving complex (OEC). The OEC is known to be virtually inactive if Ca2+ is removed from its structure. Activity can be restored not only by the addition of Ca2+ but also Sr2+ ions. We have recently introduced layered calcium manganese oxides of the birnessite mineral family as functional synthetic model compounds for the OEC. Here, we present the syntheses of layered manganese oxides where we varied the interlayer cations, preparing a series of K-, Ca-, Sr- and Mg-containing birnessites. Structural motifs within these materials were determined using X-ray absorption spectroscopy (XAS) showing that all materials have similar atomic structures despite their different elemental compositions. Water-oxidation experiments were carried out to elucidate structure-reactivity relations. These experiments demonstrated that the oxides — like the OEC — require the presence of calcium in their structures to reach maximum catalytic activity. As another similarity to the OEC, Sr2+ is the “second best choice” for the secondary cation. The results thus support mechanistic proposals which involve an important catalytic role for Ca2+ in biological water-oxidation. Additionally, they offer valuable hints for the development of synthetic, manganese-based water-oxidation catalysts for artificial photosynthesis

Phantoms of Innovation: Disciplined Simulation for Ex-ante Evaluation in Design Science Research

For years, there has been an emphasis on how to efficiently and effectively identify, evaluate, and implement innovative information systems in both design science research (DSR) and practice. Nonetheless, still today, these efforts continue to be hampered by the temporal gap between ideation and evaluation. Usually, innovative ideas are implemented at a late stage of maturity (e.g., prototypes) to test their viability in practice. This widespread approach results in waste of resources and time if the viability of an idea fails outside the lab environment. This paper discusses an ex-ante evaluation approach derived from “pretotyping” that allows innovative ideas to be tested in naturalistic settings even before they have been implemented. Thus, we call them “phantoms”. We show how this approach reduces temporal and relevance gaps, and we provide a preliminary assessment of its practicability by presenting and discussing three case studies conducted with real organizations and prospective users

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R

The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.Comment: 40 pages, 7 Figure

DoubleML: An Object-Oriented Implementation of Double Machine Learning in R

The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consists of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods

Syntheses, Electrode Preparations, Electrolytes and Two Fundamental Questions

The efficient catalysis of the four-electron oxidation of water to molecular oxygen is a central challenge for the development of devices for the production of solar fuels. This is equally true for artificial leaf-type structures and electrolyzer systems. Inspired by the oxygen evolving complex of Photosystem II, the biological catalyst for this reaction, scientists around the globe have investigated the possibility to use manganese oxides (“MnOx”) for this task. This perspective article will look at selected examples from the last about 10 years of research in this field. At first, three aspects are addressed in detail which have emerged as crucial for the development of efficient electrocatalysts for the anodic oxygen evolution reaction (OER): (1) the structure and composition of the “MnOx” is of central importance for catalytic performance and it seems that amorphous, MnIII/IV oxides with layered or tunnelled structures are especially good choices; (2) the type of support material (e.g. conducting oxides or nanostructured carbon) as well as the methods used to immobilize the MnOx catalysts on them greatly influence OER overpotentials, current densities and long-term stabilities of the electrodes and (3) when operating MnOx-based water-oxidizing anodes in electrolyzers, it has often been observed that the electrocatalytic performance is also largely dependent on the electrolyte’s composition and pH and that a number of equilibria accompany the catalytic process, resulting in “adaptive changes” of the MnOx material over time. Overall, it thus has become clear over the last years that efficient and stable water-oxidation electrolysis by manganese oxides can only be achieved if at least four parameters are optimized in combination: the oxide catalyst itself, the immobilization method, the catalyst support and last but not least the composition of the electrolyte. Furthermore, these parameters are not only important for the electrode optimization process alone but must also be considered if different electrode types are to be compared with each other or with literature values from literature. Because, as without their consideration it is almost impossible to draw the right scientific conclusions. On the other hand, it currently seems unlikely that even carefully optimized MnOx anodes will ever reach the superb OER rates observed for iridium, ruthenium or nickel-iron oxide anodes in acidic or alkaline solutions, respectively. So at the end of the article, two fundamental questions will be addressed: (1) are there technical applications where MnOx materials could actually be the first choice as OER electrocatalysts? and (2) do the results from the last decade of intensive research in this field help to solve a puzzle already formulated in 2008: “Why did nature choose manganese to make oxygen?”