Connecting adjacent active layers with structural pillars for high-performance Li-organic batteries
Source abstract
Abstract
Organic electrode materials with the versatility of molecular engineering emerge as promising alternatives to construct high-performance batteries. However, a weak binding force between active layers leads to poor structural stability accompanied by a multi-electron redox, thus hindering the construction of practical devices based on organic materials. Herein, we report a structural engineering approach to improve the structural stability of organic molecules by pre-intercalating potassium ions (K + ) as pillars into the adjacent rhodizonate (C 6 O 6 2− ) layers. This enhanced binding, with increased coordination sites of K-O, effectively prevents the exfoliation of C 6 O 6 2− layers and provides stable diffusion channels for lithium ions (Li + ). The resulting batteries exhibit accelerated reaction kinetics and enhanced Li + diffusion, leading to a high energy density of 722 Wh kg −1 (based on active materials) and reversible capacity of 315 mAh g −1 at 1.0 C, with a capacity retention of 225 mAh g −1 after 500 cycles. In addition, by virtue of the flexible nature, a Li-K 2 C 6 O 6 battery has been made into flexible fibers for next-generation wearable systems, offering a new avenue for realizing practical devices based on organic single molecules. This work presents a general and efficient strategy to unlock theoretically high-performance organic electrode materials for advanced Li-organic batteries. • Pre-intercalating K + as pillars into adjacent layers enables stable configuration. • Enhanced structural stability and facilitated ionic diffusion are achieved in organic single molecules. • Novel fiber-shaped Li-K 2 C 6 O 6 batteries with high flexibility are constructed.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advancements in Battery Materials · Advanced Battery Materials and Technologies · Inorganic Chemistry and Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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