RESUMO
A single crystalline layered semiconductor In1.2Ga0.8S3 phase was grown, and by intercalating p-aminopyridine (NH2-C5H4N or p-AP) molecules into this crystal, a new intercalation compound, In1.2Ga0.8S3·0.5(NH2-C5H4N), was synthesized. Further, by substituting p-AP molecules with p-ethylenediamine (NH2-CH2-CH2-NH2 or p-EDA) in this intercalation compound, another new intercalated compound-In1.2Ga0.8S3·0.5(NH2-CH2-CH2-NH2) was synthesized. It was found that the single crystallinity of the initial In1.2Ga0.8S3 samples was retained after their intercalation despite a strong deterioration in quality. The thermal peculiarities of both the intercalation and deintercalation of the title crystal were determined. Furthermore, the unit cell parameters of the intercalation compounds were determined from X-ray diffraction data (XRD). It was found that increasing the c parameter corresponded to the dimension of the intercalated molecule. In addition to the intercalation phases' experimental characterization, the lattice dynamical properties and the electronic and bonding features of the stoichiometric GaInS3 were calculated using the Density Functional Theory within the Generalized Gradient Approximations (DFT-GGA). Nine Raman-active modes were observed and identified for this compound. The electronic gap was found to be an indirect one and the topological analysis of the electron density revealed that the interlayer bonding is rather weak, thus enabling the intercalation of organic molecules.
RESUMO
Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here, we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data and interpreting model findings from a decision-maker's perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO EMR, as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesising and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.