Cosmic-Lab: Optical companions to binary Millisecond Pulsars

Millisecond Pulsars (MSPs) are fast rotating, highly magnetized neutron stars. According to the "canonical recycling scenario", MSPs form in binary systems containing a neutron star which is spun up through mass accretion from the evolving companion. Therefore, the final stage consists of a binary made of a MSP and the core of the deeply peeled companion. In the last years, however an increasing number of systems deviating from these expectations has been discovered, thus strongly indicating that our understanding of MSPs is far to be complete. The identification of the optical companions to binary MSPs is crucial to constrain the formation and evolution of these objects. In dense environments such as Globular Clusters (GCs), it also allows us to get insights on the cluster internal dynamics. By using deep photometric data, acquired both from space and ground-based telescopes, we identified 5 new companions to MSPs. Three of them being located in GCs and two in the Galactic Field. The three new identifications in GCs increased by 50% the number of such objects known before this Thesis. They all are non-degenerate stars, at odds with the expectations of the "canonical recycling scenario". These results therefore suggest either that transitory phases should also be taken into account, or that dynamical processes, as exchange interactions, play a crucial role in the evolution of MSPs. We also performed a spectroscopic follow-up of the companion to PSRJ1740-5340A in the GC NGC 6397, confirming that it is a deeply peeled star descending from a ~0.8Msun progenitor. This nicely confirms the theoretical expectations about the formation and evolution of MSPs.

Glycerol oxidehydration to acrylic acid on complex mixed-metal oxides

The project of this Ph.D. thesis is based on a co-supervised collaboration between Università di Bologna, ALMA MATER STUDIORUM (Italy) and Instituto de Tecnología Química, Universitat Politècnica de València ITQ-UPV (Spain). This Ph.D. thesis is about the synthesis, characterization and catalytic testing of complex mixed-oxide catalysts mainly related to the family of Hexagonal Tungsten Bronzes (HTBs). These materials have been little explored as catalysts, although they have a great potential as multifunctional materials. Their peculiar acid properties can be coupled to other functionalities (e.g. redox sites) by isomorphous substitution of tungsten atoms with other transition metals such as vanadium, niobium and molybdenum. In this PhD thesis, it was demonstrated how it is possible to prepare substituted-HTBs by hydrothermal synthesis; these mixed-oxide were fully characterize by a number of physicochemical techniques such as XPS, HR-TEM, XAS etc. They were also used as catalysts for the one-pot glycerol oxidehydration to acrylic acid; this reaction might represent a viable chemical route to solve the important issue related to the co-production of glycerin along the biodiesel production chain. Acrylic acid yields as high as 51% were obtained and important structure-reactivity correlations were proved to govern the catalytic performance; only fine tuning of acid and redox properties as well as the in-framework presence of vanadium are fundamental to achieve noteworthy yields into the acid monomer. The overall results reported herein might represent an important contribution for future applications of HTBs in catalysis as well as a general guideline for a multifaceted approach for their physicochemical characterization.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.