Potential prediction in aqueous organic redox-targeting flow batteries: DFT calculation and experimental validation

材料科学 氧化还原 水溶液 流量(数学) 纳米技术 化学工程 化学物理 计算化学 机械 物理化学 冶金 化学 物理 工程类
作者
Sida Rong,Jin Ma,Hang Zhang,Juezhi Yu,Tidong Wang,Yichong Cai,Zheng Han,Ya Ji
出处
期刊:Energy Storage Materials [Elsevier BV]
卷期号:69: 103389-103389 被引量:10
标识
DOI:10.1016/j.ensm.2024.103389
摘要

Aqueous organic redox flow batteries (AORFBs) face challenges of low energy density, which can be addressed by the strategy of redox-targeting (RT) reaction integrating solid materials (SMs) with redox mediators (RMs). However, the potential matching between SM and RM is demanding and complex. In this work, we establish a precise density functional theory (DFT) protocol to predict redox potential in RT-AORFB with anthraquinone-2,7-disulfonic acid (AQDS) derivatives as RM and poly(N-anthraquinoyl pyrrole) (PAQPy) as SM. Theoretical redox potentials are calculated from the Gibbs free energy (GFE) of various molecular models. The results suggest a precise potential match for 1,8-dihydroxyanthraquinone-2,7-disulfonic acid (1,8-DHAQDS) and PAQPy (-1.08 V and -1.09 V vs. SHE). Additionally, hydrogen bonding is involved to make simulation results more realistic, demonstrating a positive potential shift with increased GFE difference for both AQDS derivatives and PAQPy. To further elucidate the influencing mechanism of hydrogen bonding, electrostatic potential (ESP) and HOMO-LUMO gap are integrated together with GFE. The results indicate the introduction of hydrogen bonding results in extended distance for electron tunneling and a larger HOMO-LUMO gap, leading to higher GFE difference and a positive potential shift. Remarkably, results of experimental validations agree well with theoretical potential calculation. Based on predictions results, the RT-AORFB is successfully constructed with the well-matched 1,8-DHAQDS and PAQPy, exhibiting a 3.86-fold capacity enhancement compared to blank AORFB with 1,8-DHAQDS. The integrated DFT approach with GFE, ESP and HOMO-LUMO gap in this work emerges as a promising method for accurately predicting redox potentials in RT systems.
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