Multifunctional asymmetric bi-ligand iron chelating agents towards low-cost, high performance, and stable zinc-iron redox flow battery

Tippayamalee, Parimol; Pattanathummasid, Chanachai; Chanajaree, Rungroj; Pienpinijtham, Prompong; Kheawhom, Soorathep; Cheacharoen, Rongrong

doi:10.1016/j.est.2024.111295

Journal of Energy Storage

2024

DOI: 10.1016/j.est.2024.111295

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Multifunctional asymmetric bi-ligand iron chelating agents towards low-cost, high performance, and stable zinc-iron redox flow battery

Parimol Tippayamalee,

Chanachai Pattanathummasid,

Rungroj Chanajaree

et al.

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Cited by 6 publications

References 48 publications

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Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries

Sukmas,

Qin,

Chanajaree

2024

Advcd Theory and Sims

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Organic solvents offer a promising avenue for enhancing metal‐ion battery performance, for instance, in suppressing dendritic formation. To expedite the discovery of optimal electrolyte formulations, this study integrates density functional theory calculations with machine learning to accurately predict binding energies between metal ions and organic solvents. Leveraging a vast dataset of over 300 organic molecules, an extra trees regressor model is developed and demonstrated to exhibit exceptional predictive capabilities. The model's performance is underscored by its high values on both validation and test sets. Key descriptors contributing to the model's accuracy include the number of valence electrons in the metal ion, the atomic number of the metal ion, and features associated with the van der Waals surface. By applying the trained model to a dataset of up to 20 000 unseen organic molecules, potential high‐performance electrolyte additives are identified. Notably, and emerge as promising candidates for Zn‐ion and Mg‐ion batteries, respectively, outperforming conventional additives. To gain deeper insights into the microscopic behaviour of these identified molecules, molecular dynamics simulations are conducted. This research establishes a robust in silico framework for accelerating the design of advanced metal‐ion batteries through the rational selection of organic solvents.

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Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries

Sukmas,

Qin,

Chanajaree

2024

Advcd Theory and Sims

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Strategically Modified Ligand Incorporating Mixed Phosphonate and Carboxylate Groups to Enhance Performance in All‐Iron Redox Flow Batteries

Nambafu,

Hollas,

Rice

et al. 2024

Advanced Energy Materials

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Iron redox flow batteries (Fe‐RFBs) hold significant promise for achieving cost‐effectiveness and utilizing abundant materials for stationary energy storage applications. Here, a design of a novel Fe complex utilizing a nitrogenous phosphonate/carboxylate mixed ligand, N,N‐Bis(phosphonomethyl)glycine (BMPG), is presented to achieve high performance Fe anolyte. Compared to its all‐phosphonate form, nitrilotri(methylphosphonic acid) (NTMPA), the new complex Fe(BPMG)2 demonstrates a negatively shifted redox potential, resulting in ≈0.07 V (≈10%) increase in battery output voltage. Full battery testing paired with ferrocyanide catholyte demonstrates stable cycling (capacity degradation <0.0001%/cycle) over 730 consecutive charge/discharge cycles with Coulombic Efficiency of 100% at a current density of 20 mA cm−2 under near neutral pH (≈8). Of particular interest, density functional theory (DFT) studies and operando Raman measurements provide strong evidence supporting a molecular structure in BPMG, which reveals the mixed phosphonate/carboxylate groups in BPMG maintain the octahedral coordination of the Fe ion center with phosphonates exclusively, while leaving the carboxylate unbound for both Fe(II) and Fe(III) complexes. This structural similarity between BPMG‐based Fe(II) and Fe(III) complexes effectively mitigates the slow redox reaction kinetics observed in Fe(NTMPA)2 anolyte, where significant ligand reorientation occurs between Fe(II) and Fe(III) complexes.

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Redox flow batteries: Asymmetric design analysis and research methods

Liu,

Zhang,

Huang

et al. 2024

Journal of Energy Storage

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Multifunctional asymmetric bi-ligand iron chelating agents towards low-cost, high performance, and stable zinc-iron redox flow battery

Cited by 6 publications

References 48 publications

Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries

Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries

Strategically Modified Ligand Incorporating Mixed Phosphonate and Carboxylate Groups to Enhance Performance in All‐Iron Redox Flow Batteries

Redox flow batteries: Asymmetric design analysis and research methods

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