Lithium recovery from spent LiFePO 4 batteries is significant to prevent resource depletion and environmental pollution. In this study, the employment of "water in salt" electrolyte in LiFePO 4 battery enlightened us to develop a novel method for the selective recovery of lithium from spent LiFePO 4 batteries through oxidizing LiFePO 4 to FePO 4 with sodium persulfate (Na 2 S 2 O 8 ). Effect of several variables on the Li leaching efficiency was investigated. Additionally, combined thermodynamic analysis and characterization of XRD, XPS were employed to investigate the leaching mechanism. More than 99% of Li can be selectively leached in 20 min at ambient temperature with only 0.05 times excess of Na 2 S 2 O 8 . The high leaching efficiency can be ascribed to the stability and without destruction for the solid structure during the oxidation leaching. A closed-loop process was then proposed for recycling entire spent LiFePO 4 batteries, and finally high purity Li 2 CO 3 (99 wt %) was successfully prepared. The process is economically feasible and environmentally friendly and has great potential for the industrial-scale recycling of spent LiFePO 4 batteries.
Reflectivity images of the earth are calculated by migrating discrete grids of seismic traces. Typically, such traces are spatially undersampled on a recording grid with limited aperture width and so give rise to migration noise sometimes referred to as the acquisition footprint. For poststack migration images, we show how to partly deconvolve the acquisition footprint by applying a deblurring filter to the migration section, where the filter is the approximate inverse to the migration Green’s function. Results with synthetic and field data show that post‐stack migration deconvolution can noticeably improve the spatial resolution of migration images, decrease the strength of migration artifacts, and improve the quality of the migration image. We conclude that migration deconvolution can be a viable alternative to some of the other postmigration processing procedures based on statistics and ad hoc parameter choices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.