Three dimensional mapping of Fe dopants in ceria nanocrystals using direct spectroscopic electron tomography

Electron tomography is a powerful technique for the 3D characterization of the morphology of nanostructures. Nevertheless, resolving the chemical composition of complex nanostructures in 3D remains challenging and the number of studies in which electron energy loss spectroscopy (EELS) is combined with tomography is limited. During the last decade, dedicated reconstruction algorithms have been developed for HAADF-STEM tomography using prior knowledge about the investigated sample. Here, we will use the prior knowledge that the experimental spectrum of each reconstructed voxel is a linear combination of a well-known set of references spectra in a so-called direct spectroscopic tomography technique. Based on a simulation experiment, it is shown that this technique provides superior results in comparison to conventional reconstruction methods for spectroscopic data, especially for spectrum images containing a relatively low signal to noise ratio. Next, this technique is used to investigate the spatial distribution of Fe dopants in Fe:Ceria nanoparticles in 3D. It is shown that the presence of the Fe2+ dopants is correlated with a reduction of the Ce atoms from Ce4+ towards Ce3+. In addition, it is demonstrated that most of the Fe dopants are located near the voids inside the nanoparticle

Measuring Lattice Strain in Three Dimensions through Electron Microscopy

The three-dimensional (3D) atomic structure of nanomaterials, including strain, is crucial to understand their properties. Here, we investigate lattice strain in Au nanodecahedra using electron tomography. Although different electron tomography techniques enabled 3D characterizations of nanostructures at the atomic level, a reliable determination of lattice strain is not straightforward. We therefore propose a novel model-based approach from which atomic coordinates are

Severe Pediatric COVID-19 and Multisystem Inflammatory Syndrome in Children from Wild-type to Population Immunity: A Prospective Multicenter Cohort Study with Real-time Reporting

Background: SARS-CoV-2 variant evolution and increasing immunity altered the impact of pediatric SARS-CoV-2 infection. Public health decision-making relies on accurate and timely reporting of clinical data. Methods: This international hospital-based multicenter, prospective cohort study with real-time reporting was active from March 2020 to December 2022. We evaluated longitudinal incident rates and risk factors for disease severity. Results: We included 564 hospitalized children with acute COVID-19 (n = 375) or multisystem inflammatory syndrome in children (n = 189) from the Netherlands, Curaçao and Surinam. In COVID-19, 134/375 patients (36%) needed supplemental oxygen therapy and 35 (9.3%) required intensive care treatment. Age above 12 years and preexisting pulmonary conditions were predictors for severe COVID-19. During omicron, hospitalized children had milder disease. During population immunity, the incidence rate of pediatric COVID-19 infection declined for older children but was stable for children below 1 year. The incidence rate of multisystem inflammatory syndrome in children was highest during the delta wave and has decreased rapidly since omicron emerged. Real-time reporting of our data impacted national pediatric SARS-CoV-2 vaccination- and booster-policies. Conclusions: Our data supports the notion that similar to adults, prior immunity protects against severe sequelae of SARS-CoV-2 infections in children. Real-time reporting of accurate and high-quality data is feasible and impacts clinical and public health decision-making. The reporting framework of our consortium is readily accessible for future SARS-CoV-2 waves and other emerging infections