材料科学
磷酸铁锂
锂(药物)
磷酸盐
再生(生物学)
无机化学
化学工程
有机化学
生物
细胞生物学
化学
电化学
电极
物理化学
内分泌学
工程类
作者
Jie Tang,Haotian Qu,Chongbo Sun,Xiao Xiao,Haocheng Ji,Junxiong Wang,Junfeng Li,Guanjun Ji,Xuan Zhang,Hui‐Ming Cheng,Guangmin Zhou
标识
DOI:10.1002/adma.202420238
摘要
Abstract Lithiation reactions driven by chemical potential offer a promising avenue for directly regenerating degraded lithium iron phosphate (LFP). However, the choice of solution system significantly influences the lithium supplementation where improper selection may result in poor lithium recovery or extremely slow kinetics. Herein, it is identified that the most critical factor affecting solution repair effectiveness is the redox potential of the anions in the solution, which determines whether spent LFP (SLFP) can undergo spontaneous lithiation under ambient conditions. Then, machine learning (ML) is used for prediction and screening of huge potential solution systems, and finally a general strategy is proposed: creating a low redox potential solution system that incorporates anions with either low redox potential or moderate redox potential at high concentrations. As a demonstration, the regenerated LFP by ascorbic acid and LiOH solution systems exhibits a high discharge capacity of 144 mAh g −1 at 1 C, retaining 96% of its capacity after 500 cycles at 5 C. This work establishes an important criteria for designing solution systems to restore degraded LFP, marking a significant advancement in the direct regeneration of cathode materials from spent lithium‐ion batteries (LIBs).
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