LCC researcher, Associate Professor Ya Ji 's Group recently published a paper on “Potential prediction in aqueous organic redox-targeting flow batteries: DFT calculation and experimental validation” in Energy Storage Materials

Abstract:

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.

Original Article:

https://doi.org/10.1016/j.ensm.2024.103389

Sida Rong, PhD student of LCC

Dr. Ya Ji, Associate Professor of LCC

Webpage: https://lcc.sjtu.edu.cn/En/Data/View/2483