Nanoparticle Exsolution from Nanoporous Perovskites for Highly Active and Stable Catalysts.

Nanoporosity is clearly beneficial for the performance of heterogeneous catalysts. Although exsolution is a modern method to design innovative catalysts, thus far it is predominantly studied for sintered matrices. A quantitative description of the exsolution of Ni nanoparticles from nanoporous perovskite oxides and their effective application in the biogas dry reforming is here presented. The exsolution process is studied between 500 and 900 °C in nanoporous and sintered La0.52 Sr0.28 Ti0.94 Ni0.06 O3±Î´ . Using temperature-programmed reduction (TPR) and X-ray absorption spectroscopy (XAS), it is shown that the faster and larger oxygen release in the nanoporous material is responsible for twice as high Ni reduction than in the sintered system. For the nanoporous material, the nanoparticle formation mechanism, studied by in situ TEM and small-angle X-ray scattering (SAXS), follows the classical nucleation theory, while on sintered systems also small endogenous nanoparticles form despite the low Ni concentration. Biogas dry reforming tests demonstrate that nanoporous exsolved catalysts are up to 18 times more active than sintered ones with 90% of CO2 conversion at 800 °C. Time-on-stream tests exhibit superior long-term stability (only 3% activity loss in 8 h) and full regenerability (over three cycles) of the nanoporous exsolved materials in comparison to a commercial Ni/Al2 O3 catalyst.

Layer separation mapping and consolidation evaluation of a fifteenth century panel painting using terahertz time-domain imaging.

Over time, artworks often sustain paint layer separation and air gaps within their internal structure due to storage conditions and past restoration efforts. Because of this, paint layer consolidation interventions are an essential activity for art conservators. However, it is difficult to determine the exact location and the extent of layer separation on a piece of art in a non-invasive way, and even more difficult to evaluate the success of a consolidation intervention. In this work, a fifteenth-century wood panel painting was analyzed using terahertz time-domain imaging before and after it was consolidated. Using the terahertz data, it was possible to determine the areas on the artwork in need of consolidation and aid the intervention. The analysis of the after data allowed for the control and determination of the success of the consolidation effort in a non-destructive manner.

Bimetallic Exsolved Heterostructures of Controlled Composition with Tunable Catalytic Properties.

In this paper, we show how the composition of bimetallic Fe-Ni exsolution can be controlled by the nature and concentration of oxygen vacancies in the parental matrix and how this is used to modify the performance of CO2-assisted ethane conversion. Mesoporous A-site-deficient La0.4Sr0.6-αTi0.6Fe0.35Ni0.05O3±Î´ (0 ≤ α ≤ 0.2) perovskites with substantial specific surface area (>40 m2/g) enabled fast exsolution kinetics (T < 500 °C, t < 1 h) of bimetallic Fe-Ni nanoparticles of increasing size (3-10 nm). Through the application of a multitechnique approach we found that the A-site deficiency determined the concentration of oxygen vacancies associated with iron, which controlled the Fe reduction. Instead of homogeneous bimetallic nanoparticles, the increasing Fe fraction from 37 to 57% led to the emergence of bimodal Fe/Ni3Fe systems. Catalytic tests showed superior stability of our catalysts with respect to commercial Ni/Al2O3. Ethane reforming was found to be the favored pathway, but an increase in selectivity toward ethane dehydrogenation occurred for the systems with a low metallic Fe fraction. The chance to control the reduction and growth processes of bimetallic exsolution offers interesting prospects for the design of advanced catalysts based on bimodal nanoparticle heterostructures.

New Jersey COVID-19 municipal dataset.

Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.