Na Storage Activity or Inertness of P-Configurations in N, P Dual-Doped Carbon Nanofibers: Bulk vs Surface.

Heteroatom doping is an effective method to improve the electrochemical properties of hard carbon anodes for sodium-ion batteries. However, the different roles of surface and bulk heteroatoms in Na storage have not been explored much. Herein, N, P dual-doped carbon nanofibers (NP-CNFs) with high doping contents and low surface area are designed to clarify the above issue. It is confirmed that P plays a more crucial role in Na storage compared with N. In addition, surface and bulk P not only possess different configurations but also show distinct Na storage activity. There are only oxidized POx groups on the surface, which are inactive for Na storage but promote the stability of electrochemistry interphase, while in the bulk phase, unoxidized P-C bonds also emerge except POx, which shows preeminently reversible Na storage activity, and the POx groups are activated simultaneously. Furthermore, P-doping changes the reactivity of N-configurations with Na both on the surface and in the bulk phase, exhibiting interesting synergism. As expected, the surface stability, bulk activity, and synergism enable NP-CNFs to achieve superior performance. It could deliver a prominent capacity of 105.6 mAh g-1 at 10 A g-1 after 3000 cycles in half cells and 164.3 mAh g-1 at 1 A g-1 after 200 cycles in full cells.

A Bayesian Spatiotemporal Nowcasting Model for Public Health Decision-Making and Surveillance.

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers.