Estudio de Métodos de Física Computacional y su Aplicación en Nanotecnología

En el presente trabajo se estudia los métodos de física computacional y su aplicación en nanotecnología, específicamente se empleó estas herramientas para explorar la génesis de los Pirogalol[4]arenos y la interacción del hidrógeno molecular (H2) con rccc Metil-Pyg[4]arenos funcionalizado con cationes Li+, Na+ y Mg2+, esta investigación se realizo mediante cálculos cuanto-mecánicos aplicando la DFT (i.e., Density Functional Theory) a nivel de teoría B3LYP y B97D y dos conjuntos de funciones base 6-311G(d,p) y 6-311++G(d,p). En la primera fase de la investigación se evaluó los factores cinéticos del auto-ensamblaje del R-Pyg[4]Ar (R = metil y fenil) en modelos simplificados (i.e., dímeros), estudiando el enlace de rotación mediante un giro de 360° y cada 20° se obtuvo las energías de cada isómero conformacional. En la segunda fase se analizó, la estabilidad de los cationes dentro de la cavidad del Metil-Pyg[4]Ar, y también la posición óptima del H2 en el complejo funcionalizado. Obtenida la geometría de equilibrio, la energía de amarre (EA) fue calculada para cada complejo. Los resultados muestran: en la primera fase la convergencia con los datos experimentales y en la segunda que el Mg-Metil-Pyg[4]Ar es una potencial nanoestructura para el almacenamiento del H2. También se observó que el funcional B97D es el mejor nivel de teoría para refinar los cálculos de la DFT convencional

Tunable plasmons in regular planar arrays of graphene nanoribbons with armchair and zigzag-shaped edges

Recent experimental evidence for and the theoretical confirmation of tunable edge plasmons and surface plasmons in graphene nanoribbons have opened up new opportunities to scrutinize the main geometric and conformation factors, which can be used to modulate these collective modes in the infrared-to-terahertz frequency band. Here, we show how the extrinsic plasmon structure of regular planar arrays of graphene nanoribbons, with perfectly symmetric edges, is influenced by the width, chirality and unit-cell length of each ribbon, as well as the in-plane vacuum distance between two contiguous ribbons. Our predictions, based on time-dependent density functional theory, in the random phase approximation, are expected to be of immediate help for measurements of plasmonic features in nanoscale architectures of nanoribbon devicesC.V.G. acknowledges the financial support of the “Secretaria Nacional de Educación Superior, Ciencia, Tecnología e Innovación” (SENESCYT-ECUADOR

Analysis of covid-19 outbreak in Ecuador using the logistic model

At the end of 2019, the COVID-19 disease emerged in the city of Wuhan, China, and caused an outbreak of unusual viral pneumonia. Being highly transmissible, this novel coronavirus disease has spread fast all over the world. COVID-19 continues to challenge most developed countries in the search for an effective strategy to either prevent infection or to avoid the spreading of the disease. While several developed countries have managed to contain COVID-19, several countries in Latin America continue to report an increase in the daily number of infected people. Ecuador, particularly, became the epicenter of the COVID-19 outbreak in the region during March and April 2020. In this context, the present study shows a simple mathematical approach to understand the effect of the COVID-19 outbreak in Ecuador (and some Latin American countries such as Brazil, Peru, and Colombia). The proposed method is based on the exponential model, discrete logistic equation, and differential logistic model using one-year data from March 1, 2020, to February 28, 2021. This study presents the estimated growth rate coefficient (λ), the total number of cases (N), and the midpoint of maximum infection (t_0) as well as the variability of the λ coefficient as a function of total cases and time. The exponential model shows a high value of λ=0.185 which decreases to λ=0.014 and λ=0.056 according to the discrete and differential logistic models, respectively. An accurate value of the total number of cases of infected people was found by analyzing the number of daily cases as a function of the total of cases whose value (N~409 K) agrees with the data reported at the end of May 2021, validating the proposed approach. How to use the current mathematical approach for long-term prediction is also discussed here. Most importantly, the proposed method has two important characteristics: (i) the mathematical model is as simple as possible compared to other time-consuming approaches, and (ii) it can be used to study the effect of COVID-19 and predicts its consequences in other countries, allowing revenue new decisions against the COVID-19 diseas