One can nowadays readily generate monodisperse colloidal nanocrystals, but the underlying mechanism of nucleation and growth is still a matter of intense debate. Here, we combine X-ray pair distribution function (PDF) analysis, small angle X-ray scattering (SAXS), nuclear magnetic resonance (NMR), and transmission electron microscopy (TEM) to investigate the nucleation and growth of zirconia nanocrystals from zirconium chloride and zirconium isopropoxide at 340 °C, in the presence of surfactant. We find that initially, many amorphous particles are formed. Over time, the total particle concentration decreases while the amorphous particles recrystallize into ZrO2 nanocrystals. After a sudden increase, the concentration of nanocrystals stays constant over the course of the reaction. Both findings stand in contrast to reports of continuous nucleation in other surfactant-assisted nanocrystal syntheses. The non-classical nucleation is likely related to the precursor decomposition rate that is an order of magnitude higher than the observed crystallization rate. Comparing different zirconium precursors, we observe higher smaller particles for reactions with ZrBr4 or with Zr(OtBu)4, which we could correlate with a higher precursor decomposition rate.

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COVID‐19 vaccines have a limited supply, and there is a huge gap between supply and demand, leading to disproportionate administration. One of the main conditions on which balanced and optimal vaccine distribution depends are the health conditions of the vaccine recipients. Vaccine administration of front‐line workers, the elderly, and those with diseases should be prioritized. To solve this problem, we proposed a novel architecture called CovidXAI, which is trained with a self‐collected dataset with 24 parameters influencing the risk group of the vaccine recipient. Then, Random Forest and XGBoost classifiers have been used to train the model—having training accuracies of 0.85 and 0.87 respectively, to predict the risk factor, classified as low, medium, and high risk. The optimal vaccine distribution can be done using the derived from the predicted risk class. A web application is developed as a user interface, and Explainable AI (XAI) has been used to demonstrate the varying dependence of the various factors used in the dataset, on the output by CovidXAI.

One can nowadays readily generate monodisperse colloidal nanocrystals, but the underlying mechanism of nucleation and growth is still a matter of intense debate. Here, we combine X-ray pair distribution function (PDF) analysis, small angle X-ray scattering (SAXS), nuclear magnetic resonance (NMR), and transmission electron microscopy (TEM) to investigate the nucleation and growth of zirconia nanocrystals from zirconium chloride and zirconium isopropoxide at 340 °C, in the presence of surfactant. We find that initially, many amorphous particles are formed. Over time, the total particle concentration decreases while the amorphous particles recrystallize into ZrO2 nanocrystals. After a sudden increase, the concentration of nanocrystals stays constant over the course of the reaction. Both findings stand in contrast to reports of continuous nucleation in other surfactant-assisted nanocrystal syntheses. The non-classical nucleation is likely related to the precursor decomposition rate that is an order of magnitude higher than the observed crystallization rate. Comparing different zirconium precursors, we observe higher smaller particles for reactions with ZrBr4 or with Zr(OtBu)4, which we could correlate with a higher precursor decomposition rate.