Influence of the substrate bias voltage on the crystallographic structure and surface composition of Ti6A14V thin films deposited by rf magnetron sputtering

In this work, the influence of the substrate bias on the crystalline structure and surface composition of Ti6Al4V thin films prepared by rf magnetron sputtering were studied. Samples were grown onto two different types of substrates: AISI 420 steel and common glass using a Ti6Al4V (99.9 %) target. Substrate bias was varied from -100V to -200 V. Samples were characterized by X-ray diffraction (XRD), Energy Dispersive X-ray Analysis (EDX), Scanning Electron Microscopy (SEM), and X-Ray Photoelectron Spectroscopy (XPS). It was observed that the increase of the substrate voltage improved the crystallinity of the deposited films. The stoichiometry of the deposited thin films was studied by EDX and found to be slightly different from that of the target material. Finally, the passive film spontaneously formed on the deposited films upon exposure to the laboratory atmosphere was studied by XPS. The composition of the passive film is rather complex since it contains several forms of oxidized titanium and vanadium as well as Al2O3.Peer reviewe

Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.</p

Synthesis and characterization of La_0.8Sr_1.2Co_0.5M_0.5O_4-? (M=Fe, Mn)

The M4+-containing K2NiF4-type phases La0.8Sr1.2Co0.5Fe0.5O4 and La0.8Sr1.2Co0.5Mn0.5O4 have been synthesized by a sol-gel procedure and characterized by X-ray powder diffraction, thermal analysis, neutron powder diffraction and Mössbauer spectroscopy. Oxide ion vacancies are created in these materials via reduction of M4+ to M3+ and of Co3+ to Co2+. The vacancies are confined to the equatorial planes of the K2NiF4-type structure. A partial reduction of Mn3+ to Mn2+ also occurs to achieve the oxygen stoichiometry in La0.8Sr1.2Co0.5Mn0.5O3.6. La0.8Sr1.2Co0.5Fe0.5O3.65 contains Co2+ and Fe3+ ions which interact antiferromagnetically and result in noncollinear magnetic order consistent with the tetragonal symmetry. Competing ferromagnetic and antiferromagnetic interactions in La0.8Sr1.2Co0.5Fe0.5O4, La0.8Sr1.2Co0.5Mn0.5O4 and La0.8Sr1.2Co0.5Mn0.5O3.6 induce spin glass properties in these phases

Investigation into the effect of Si doping on the performance of SrFeO3-δ SOFC electrode materials

In this paper we report the successful incorporation of silicon into SrFeO3-δ perovskite materials for potential applications as electrode materials for solid oxide fuel cells. It is observed that Si doping leads to a change from a tetragonal cell (with partial ordering of oxygen vacancies) to a cubic one (with the oxygen vacancies disordered). Annealing experiments in 5% H2/95% N2 (up to 800 °C) also showed the stabilization of the cubic form for the Si-doped samples under reducing conditions, suggesting that they may be suitable for both cathode and anode applications. In contrast to the cubic cell of the reduced Si doped system, reduction of undoped SrFeO3-δ leads to the formation of a brownmillerite structure with ordered oxide ion vacancies. SrFe 0.90Si0.10O3-δ and SrFe 0.85Si0.15O3-δ were analysed by neutron powder diffraction, and the data confirmed the cubic cell, with no long range oxygen vacancy ordering. Mössbauer spectroscopy data were also recorded for SrFe0.90Si0.10O3-δ, and indicated the presence of only Fe3+ and Fe5+ (i.e. disproportionation of Fe4+ to Fe3+ and Fe5+) for such doped samples. Conductivity measurements showed an improvement in the conductivity on Si doping. Composite electrodes with 50% Ce0.9Gd0.1O 1.95 were therefore examined on dense Ce0.9Gd 0.1O1.95 pellets in two different atmospheres: air and 5% H2/95% N2. In both atmospheres an improvement in the area specific resistance (ASR) values is observed for the Si-doped samples. Thus the results show that silicon can be incorporated into SrFeO3-δ- based materials and can have a beneficial effect on the performance, making them potentially suitable for use as cathode and anode materials in symmetrical SOFCs. © 2013 The Royal Society of Chemistry.Peer Reviewe

Femtosecond pulsed laser deposition of nanostructured CdS films

In this work, we report an investigation of the properties of nanostructured deposits obtained from femtosecond pulsed laser deposition of CdS sintered targets. Specifically, we address the effects of laser irradiation wavelength, laser fluence, and substrate temperature (from 25 to 450 °C). The composition of the deposits was characterized using X-ray photoelectron spectroscopy (XPS), their crystallinity by X-ray diffraction (XRD), and the surface morphology was studied by environmental scanning electron microscopy (ESEM) and atomic force microscopy (AFM). It has been found that the smallest nanoparticles, with an average diameter of 25 nm and a narrow size distribution, together with particulates in the range of 80-120 nm, are obtained at the shortest laser wavelength of 266 nm on room-temperature substrates. Deposits do not contain microscopic droplets in any of the explored conditions. © 2010 American Chemical Society.Peer Reviewe

Decoupling of defect and short-range order contributions to resistivity recovery measurements in binary alloys

© 2014 American Physical Society. We report a new and improved approach that uses low-temperature resistivity recovery measurements to study the defect kinetics in metallic binary alloys. This method is able to decouple the effect related to the irradiation defect contribution to the resistivity from that of the short-range order, which is enhanced by the free migration of defects. This approach can provide reliable experimental data which are more suitable for comparisons with current computational models. Furthermore, the difference in this method with respect to the classical one is that our method gives information concerning the role of vacancies and interstitials on short-range order. The method is applied to a model alloy Fe-5%Cr, of interest for fusion applications, where short-range order effects have been previously found to play a role.Peer Reviewe

On the Importance of Electroweak Corrections for Majorana Dark Matter Indirect Detection

Recent analyses have shown that the inclusion of electroweak corrections can alter significantly the energy spectra of Standard Model particles originated from dark matter annihilations. We investigate the important situation where the radiation of electroweak gauge bosons has a substantial influence: a Majorana dark matter particle annihilating into two light fermions. This process is in p-wave and hence suppressed by the small value of the relative velocity of the annihilating particles. The inclusion of electroweak radiation eludes this suppression and opens up a potentially sizeable s-wave contribution to the annihilation cross section. We study this effect in detail and explore its impact on the fluxes of stable particles resulting from the dark matter annihilations, which are relevant for dark matter indirect searches. We also discuss the effective field theory approach, pointing out that the opening of the s-wave is missed at the level of dimension-six operators and only encoded by higher orders.Comment: 25 pages, 6 figures. Minor corrections to match version published in JCA

Conformations of Linear DNA

We examine the conformations of a model for under- and overwound DNA. The molecule is represented as a cylindrically symmetric elastic string subjected to a stretching force and to constraints corresponding to a specification of the link number. We derive a fundamental relation between the Euler angles that describe the curve and the topological linking number. Analytical expressions for the spatial configurations of the molecule in the infinite- length limit were obtained. A unique configuraion minimizes the energy for a given set of physical conditions. An elastic model incorporating thermal fluctuations provides excellent agreement with experimental results on the plectonemic transition.Comment: 5 pages, RevTeX; 6 postscript figure

Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients:A Causal Approach

Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment. However, in this setting we are limited to observational data with quality issues, missing values, small sample size and high dimensionality: we cannot reliably learn such models from limited observational data with these sources of bias. Instead, we choose to learn a causal Bayesian network to mitigate the issues above and to leverage the prior knowledge on endometrial cancer available from clinicians and physicians. We introduce a causal discovery algorithm for causal Bayesian networks based on bootstrap resampling, as opposed to the single imputation used in related works. Moreover, we include a context variable to evaluate whether selection bias results in learning spurious associations. Finally, we discuss the strengths and limitations of our findings in light of the presence of missing data that may be missing-not-at-random, which is common in real-world clinical settings.</p

Causal Discovery with Missing Data in a Multicentric Clinical Study

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.</p