Synthesis and characterization of Na03RhO206H2O - a semiconductor with a weak ferromagnetic component

We have prepared the oxyhydrate Na03RhO206H2O by extracting Na+ cations from NaRhO2 and intercalating water molecules using an aqueous solution of Na2S2O8. Synchrotron X-ray powder diffraction, thermogravimetric analysis (TGA), and energy-dispersive x-ray analysis (EDX) reveal that a non-stoichiometric Na03(H2O)06 network separates layers of edge-sharing RhO6 octahedra containing Rh3+(4d6, S=0) and Rh4+ (4d5, S=1/2). The resistivities of NaRhO2 and Na03RhO206H2O (T < 300) reveal insulating and semi-conducting behavior with activation gaps of 134 meV and 7.8 meV, respectively. Both Na03RhO206H2O and NaRhO2 show paramagnetism at room temperature, however, the sodium-deficient sample exhibits simultaneously a weak but experimentally reproducible ferromagnetic component. Both samples exhibit a temperature-independent Pauli paramagnetism, for NaRhO2 at T > 50 K and for Na03RhO206H2O at T > 25 K. The relative magnitudes of the temperature-independent magnetic susceptibilities, that of the oxide sample being half that of the oxyhydrate, is consistent with a higher density of thermally accessible electron states at the Fermi level in the hydrated sample. At low temperatures the magnetic moments rise sharply, providing evidence of localized and weakl -ordered electronic spins.Comment: 15 fages 5 figures Solid State Communications in prin

Neo-Statecraft Theory, Historical Institutionalism and Institutional Change

This article provides a critical examination of the contribution that statecraft theory, which has been subject to recent revision and development, makes to the literature on institutional change. It articulates an emergent neo-statecraft approach that offers an agent-led form of historical institutionalism. This overcomes the common criticism that historical institutionalists underplay the creative role of actors. The article also argues that the approach brings back into focus the imperatives of electoral politics as a source of institutional change and provides a macro theory of change which is also commonly missing from historical institutionalist work. It can therefore identify previously unnoticed sources of stability and change, especially in states with strong executives and top-down political cultures

Cyclotrimerisation of isocyanates catalysed by low-coordinate Mn(II) and Fe(II) m-terphenyl complexes

Two- and three-coordinate m-terphenyl complexes of manganese and iron are efficient catalysts for the selective cyclotrimerisation of primary aliphatic isocyanates affording isocyanurates in short reaction times and under mild conditions

Reliability of mechanical sprint profiles in state U16 female basketball athletes

BLUF: Mechanical sprint characteristics produce acceptable trial to trial reliability in state U16 female basketball athletes. INTRODUCTION: Sprint acceleration is a key component of basketball performance that occurs frequently during competition that is commonly measured over various distances using timing gates. Sprint profiling is a topical area of research that provides practitioners an insight into the underlying mechanical characteristics that contributed to the performance. These include theoretical maximal force, theoretical maximal velocity, maximum power, slope of the force velocity relationship, maximum ratio of force, decrease in ratio of force and max speed. This study aimed to investigate the reliability of mechanical sprint characteristics in state U16 female basketball athletes

Understanding the limitations of self-supervised learning for tabular anomaly detection

While self-supervised learning has improved anomaly detection in computer vision and natural language processing, it is unclear whether tabular data can benefit from it. This paper explores the limitations of self-supervision for tabular anomaly detection. We conduct several experiments spanning various pretext tasks on 26 benchmark datasets to understand why this is the case. Our results confirm representations derived from self-supervision do not improve tabular anomaly detection performance compared to using the raw representations of the data. We show this is due to neural networks introducing irrelevant features, which reduces the effectiveness of anomaly detectors. However, we demonstrate that using a subspace of the neural network’s representation can recover performance

A high-resolution map of human evolutionary constraint using 29 mammals.

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ∼4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ∼60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease

Machine learning activation energies of chemical reactions

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar

Biocatalytic dearomatisation of para-fluorobenzoic acid:Access to versatile homochiral building blocks with quaternary centres

The enzyme benzoate dioxygenase (BZDO) from Ralstonia eutropha B9 is able to dihydroxylate benzoic acids in a dearomative process that proceeds with a different regioselectivity than other known dioxygenase enzymes. Here we show that 4-fluorobenzoic acid is oxidised by BZDO to give an enantiopure diol that can be rapidly elaborated to highly oxygenated homochiral building blocks with quaternary centres. Notably, the diol produced in this biotransformation displays reactivity which is distinct from that of the more extensively studied non-fluorinated analogue

Comparing the Performances of Force Fields in Conformational Searching of Hydrogen-Bond-Donating Catalysts

Here, we compare the relative performances of different force fields for conformational searching of hydrogen-bond-donating catalyst-like molecules. We assess the force fields by their predictions of conformer energies, geometries, low-energy, nonredundant conformers, and the maximum numbers of possible conformers. Overall, MM3, MMFFs, and OPLS3e had consistently strong performances and are recommended for conformationally searching molecules structurally similar to those in this study