The many lives of magnetized neutron stars

The magnetic field strength at birth is arguably one of the most important properties to determine the evolutionary path of a neutron star. Objects with very high fields, collectively known as magnetars, are characterized by high X-ray quiescent luminosities, occurrence of outbursts, and, for some of them, sporadic giant flares. While the magnetic field strength is believed to drive their collective behaviour, however, the diversity of their properties, and, especially, the observation of magnetar-like bursts from “low-field” pulsars, has been a theoretical puzzle. In this review, we discuss results of long-term simulations following the coupled evolution of the X-ray luminosity and the timing properties for a large, homogeneous sample of X-ray emitting isolated neutron stars, accounting for a range of initial magnetic field strengths, envelope compositions, and neutron star masses. In addition, by following the evolution of magnetic stresses within the neutron star crust, we can also relate the observed magnetar phenomenology to the physical properties of neutron stars, and in particular to their age and magnetic field strength and topology. The dichotomy of “high-B” field pulsars versus magnetars is naturally explained, and occasional outbursts from old, low B-field neutron stars are predicted. We conclude by speculating on the fate of old magnetars, and by presenting observational diagnostics of the neutron star crustal field topology.

Ligand tethering by ion-exchange for the immobilization of homogeneous catalysts

A Rh phosphine complex, derived from the Wilkinson’s catalyst, has been immobilized by ion-exchange on the ammonium form of a Al-MCM-41 sample. Ammonium ions have been exchanged by cholamine ions, which act as an amine ligand, and then the Wilkinson’s catalyst has been immobilized by substitution of a phosphine ligand by the anchored amine. This is a novel immobilization procedure, as a ligand, instead of the whole complex, is tethered to the support by ion exchange. The obtained hybrid catalyst has been characterized by Elemental Analysis, DRIFTS and XPS. The quantitative exchange of ammonium by cholamine and coordination of Rh to amines has been observed. Most of the anchored Rh is considered to be coordinated to the ligand tethered to the support and a small proportion seems to be interacting with the protonated ligand or with the support surface. The catalyst has been tested in the hydrogenation of cyclohexene and in the hydroformylation of 1-octene. In the first case the catalyst is active and reusable, while a strong Rh leaching takes place in the second one.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.