Search

The electrolyte chemistry is crucially important for promoting the practical application of lithium metal batteries (LMBs). Here, we demonstrate for the first time that 1,3‐dimethylimidazolium dimethyl phosphate (DIDP) and trimethylsilyl trifluoroacetate (TMSF) can undergo in situ transesterification in carbonate electrolyte to generate dimethyl trimethylsilyl phosphate (DTMSP) and 1,3‐dimethylimidazolium trifluoroacetate (DITFA) as multifunctional additives for LMBs. H2O and HF can be removed by the Si−O group in DTMSP to improve the moisture resistance of electrolyte and the stability of cathode. Furthermore, the dissolution of lithium nitrate (LiNO3) in carbonate electrolyte can be promoted by the trifluoroacetate anion (TFA−) in DITFA, thereby optimizing the solvation structure and transport kinetics of Li+. More importantly, both DTMSP and DITFA tend to preferential redox decomposition due to the low lowest unoccupied molecular orbital (LUMO) and high highest occupied molecular orbital (HOMO). Consequently, a thin and robust layer rich in P/N/Si on the cathode and an inorganic‐rich layer (e.g. Li3N/Li3P) on the anode can be constructed and superior electrochemical performances are achieved. This artificial transesterification strategy to introduce favorable additives paves an efficient and ingenious route to high‐performance electrolyte for LMBs. [ABSTRACT FROM AUTHOR]

Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities. We proposed a similarity reward to help teach an optimal action sequence to model the step-wise nature of the human processing process explicitly. In addition, We designed an action set capable of handling multiple types of noise to construct the action space, thereby achieving successful crossmodal denoising. Extensive experiments against state-of-the-art methods on publicly available RGB, infrared, and terahertz datasets demonstrate the superiority of our method in crossmodal image denoising.