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Aggregation significantly influences the transport, transformation, and bioavailability of engineered nanomaterials. Two–dimensional MoS2 nanosheets are one of the most well-studied transition-metal dichalcogenide nanomaterials. Nonetheless, the aggregation behavior of this material under environmental conditions is not well understood. Here, we investigated the aggregation of single-layer MoS2 (SL-MoS2) nanosheets under a variety of conditions. Trends in the aggregation of SL-MoS2 are consistent with classical Derjaguin–Landau–Verwey–Overbeek (DLVO) colloidal theory, and the critical coagulation concentrations of cations follow the order of trivalent (Cr3+) < divalent (Ca2+, Mg2+, Cd2+) < monovalent cations (Na+, K+). Notably, Pb2+ and Ag+ destabilize MoS2 nanosheet suspensions much more strongly than do their divalent and monovalent counterparts. This effect is attributable to Lewis soft acid–base interactions of cations with MoS2. Visible light irradiation synergistically promotes the aggregation of SL-MoS2 nanosheets in the presence of cations, which was evident even in the presence of natural organic matter. The light-accelerated aggregation was ascribed to dipole–dipole interactions due to transient surface plasmon oscillation of electrons in the metallic 1T phase, which decrease the aggregation energy barrier. These results reveal the phase-dependent aggregation behaviors of engineered MoS2 nanosheets with important implications for environmental fate and risk. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.