In this work, H NMR relaxometry and diffusometry as well as viscometry experiments were carried out as a means to study the molecular dynamics of magnetic and nonmagnetic ionic liquid-based systems. In order to evaluate the effect of a cosolvent on the superparamagnetic properties observed for Aliquat-iron-based magnetic ionic liquids, mixtures comprising different concentrations, 1% and 10% (v/v), of DMSO-d6 were prepared and studied. The results for both magnetic and nonmagnetic systems were consistently analyzed an suggest that, when at low concentrations, DMSO-d6 promotes more structured ionic arrangements, thus enhancing these superparamagnetic properties. Furthermore, the analysis of temperature and water concentration effects allowed to conclude that neither one of these variables significantly affected the superparamagnetic properties of the studied magnetic ionic liquids.
Compartmental epidemiological models are, by far, the most popular in the study of dynamics related with infectious diseases. It is, therefore, not surprising that they are frequently used to study the current COVID-19 pandemic. Taking advantage of the real-time availability of COVID-19 related data, we perform a compartmental model fitting analysis of the portuguese case, using an online open-access platform with the integrated capability of solving systems of differential equations. This analysis enabled the data-driven validation of the used model and was the basis for robust projections of different future scenarios, namely, increasing the detected infected population, reopening schools at different moments, allowing Easter celebrations to take place and population vaccination. The method presented in this work can easily be used to perform the non-trivial task of simultaneously fitting differential equation solutions to different epidemiological data sets, regardless of the model or country that might be considered in the analysis.
Accurate models are fundamental to understand the dynamics of the COVID-19 pandemic and to evaluate different mitigation strategies. Here, we present a multi-compartmental model that fits the epidemiological data for eleven countries, despite the reduced number of fitting parameters. This model consistently explains the data for the daily infected, recovered, and dead over the first six months of the pandemic. The good quality of the fits makes it possible to explore different scenarios and evaluate the impact of both individual and collective behaviors and government- level decisions to mitigate the epidemic. We identify robust alternatives to lockdown, such as self- protection measures, and massive testing. Furthermore, communication and risk perception are fundamental to modulate the success of different strategies. The fitting/simulation tool is publicly available for use and test of other models, allowing for comparisons between different underlying assumptions, mitigation measures, and policy recommendations.
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