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Redox-active molecules, or redoxmers, in nonaqueous redox flow batteries often suffer from membrane crossover and low electrochemical stability. Transforming inorganic polyionic redoxmers established for aqueous batteries into nonaqueous candidates is an attractive strategy to address these challenges. Here we demonstrate such tailoring for hexacyanoferrate (HCF) by pairing the anions with tetra-n-butylammonium cation (TBA+). TBA3HCF has good solubility in acetonitrile and >1 V lower redox potential vs the aqueous counterpart; thus, the familiar aqueous catholyte becomes a new nonaqueous anolyte. The lowering of redox potential correlates with replacement of water by acetonitrile in the solvation shell of HCF, which can be traced to H-bond formation between water and cyanide ligands. Symmetric flow cells indicate exceptional stability of HCF polyanions in nonaqueous electrolytes and Nafion membranes completely block HCF crossover in full cells. Ion pairing of metal complexes with organic counterions can be effective for developing promising redoxmers for nonaqueous flow batteries. 

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.