Three-Dimensional Porous Si@SiOx/Ag/CN Anode Derived from Deposition Silicon Waste toward High-Performance Li-Ion Batteries.

The application of photovoltaic (PV) solid waste to the field of lithium-ion batteries is deemed to be an effective solution for waste disposal, which can not only solve the problem of environmental pollution but also avoid the loss of secondary resources. Herein, based on the volatile deposited waste produced by electron beam refining polysilicon, a simple and environmentally friendly method was designed to synthesize P-Si@SiOx/Ag/CN as an anode material for lithium-ion batteries. Remarkably, the presence of silver and the formation of a carbon-nitrogen network can enhance the electrical conductivity of the composite and boost the transport efficiency of lithium ions. Furthermore, the porous Si@SiOx structure is generated by silver-assisted chemical etching (Ag-ACE), and the carbon-nitrogen grid architecture is formed after lyophilization with NaCl as a template, which can jointly provide sufficient buffer space for the volume change of silicon during lithiation/delithiation. Benefitting from these advantages, the P-Si@SiOx/Ag/CN anode exhibits outstanding cycling performance with 759 mA h g-1 over 300 cycles at 0.5 A g-1. Meanwhile, the lithium-ion batteries employing the P-Si@SiOx/Ag/CN anodes present a superior rate capability of 950 mA h g-1 at 2 A g-1 and retain a high reversible specific capacity of 956 mA h g-1 at 1 A g-1 after 50 cycles. This work opens up a new economic strategy for the fabrication of high-performance silicon anodes and affords a promising avenue for the recycling of PV silicon waste.

An inverse method to estimate emission rates based on nonlinear least-squares-based ensemble four-dimensional variational data assimilation with local air concentration measurements.

An inverse source estimation method is proposed to reconstruct emission rates using local air concentration sampling data. It involves the nonlinear least squares-based ensemble four-dimensional variational data assimilation (NLS-4DVar) algorithm and a transfer coefficient matrix (TCM) created using FLEXPART, a Lagrangian atmospheric dispersion model. The method was tested by twin experiments and experiments with actual Cs-137 concentrations measured around the Fukushima Daiichi Nuclear Power Plant (FDNPP). Emission rates can be reconstructed sequentially with the progression of a nuclear accident, which is important in the response to a nuclear emergency. With pseudo observations generated continuously, most of the emission rates were estimated accurately, except under conditions when the wind blew off land toward the sea and at extremely slow wind speeds near the FDNPP. Because of the long duration of accidents and variability in meteorological fields, monitoring networks composed of land stations only in a local area are unable to provide enough information to support an emergency response. The errors in the estimation compared to the real observations from the FDNPP nuclear accident stemmed from a shortage of observations, lack of data control, and an inadequate atmospheric dispersion model without improvement and appropriate meteorological data. The proposed method should be developed further to meet the requirements of a nuclear emergency response.