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Pedro L. de Andres

Publications and source records attributed to Pedro L. de Andres.

4 recordsLinked to original sources

The Significant Role of Hydrogen in the Formation of Silicon Carbide in Evolved Stars

Cosmic dust is mainly formed in the atmospheres of evolved stars. In carbon rich stars, amorphous carbon along with silicon carbide are the main constituents of dust grains yet the mechanisms involved in the formation of these grains are still poorly understood. Several molecular precursors have been proposed to form silicon carbide grains. Here, we have simulated in the laboratory the formation of silicon carbide dust starting from atomic C, atomic Si and H$_2$ and we have clearly identified SiC$_2$ as a key molecular precursor of nanodust analogues. We show that the interaction of molecular hydrogen with atomic carbon initiates the formation of hydrocarbons, which then react with atomic silicon to produce gas-phase SiC$_2$. In our experiments, the silicon carbide nanodust analogues are partially hydrogenated. Chemical routes for the formation of SiC$_2$ and organosilicon species are discussed on the basis of thermochemical calculations and chemical kinetics modelling. Our findings reveal the central role of molecular hydrogen in the formation of SiC$_2$ and contribute to a deeper understanding of silicon carbide dust formation processes in evolved stars, from atoms to molecules, clusters, and ultimately dust grains.

astro-ph.GA↗

Monitoring and Forecasting COVID-19: Statistical Heuristic Regression, Susceptible-Infected-Removed model and, Spatial Stochastics

The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. Dynamics of such public-health threats can often be efficiently analysed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derived the three free parameters for both models in several cases and tested them against the amount of data needed to bring accuracy in predictions. The SHR model is approximately +/- 2% accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a useful tool to forecast the evolution of the disease. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.

q-bio.PE↗

Formation of Hydrocarbons from Hydrogenated Graphene in Circumstellar Clouds

We describe a mechanism that explains the formation of hydrocarbons and hydrocarbyls from hydrogenated graphene/graphite; hard C-C bonds are weakened and broken by the synergistic effect of chemisorbed hydrogen and high temperature vibrations. Total energies, optimized structures, and transition states are obtained from Density Functional Theory simulations. These values have been used to determine the Boltzman probability for a thermal fluctuation to overcome the kinetic barriers, yielding the time scale for an event to occur. This mechanism can be used to rationalize the possible routes for the creation of small hydrocarbons and hydrocarbyls from etched graphene/graphite in stellar regions.

cond-mat.mes-hall↗

Patterson Function from Low-Energy Electron Diffraction Measured Intensities and Structural Discrimination

Surface Patterson Functions have been derived by direct inversion of experimental Low-Energy Electron Diffraction I-V spectra measured at multiple incident angles. The direct inversion is computationally simple and can be used to discriminate between different structural models. 1x1 YSi_2 epitaxial layers grown on Si(111) have been used to illustrate the analysis. We introduce a suitable R-factor for the Patterson Function to make the structural discrimination as objective as possible. From six competing models needed to complete the geometrical search, four could easily be discarded, achieving a very significant and useful reduction in the parameter space to be explored by standard dynamical LEED methods. The amount and quality of data needed for this analysis is discussed.

cond-mat.mtrl-sci↗