Phosphonate-based iron complex for a cost-effective and long cycling aqueous iron redox flow battery.

A promising metal-organic complex, iron (Fe)-NTMPA2, consisting of Fe(III) chloride and nitrilotri-(methylphosphonic acid) (NTMPA), is designed for use in aqueous iron redox flow batteries. A full-cell testing, where a concentrated Fe-NTMPA2 anolyte (0.67 M) is paired with a Fe-CN catholyte, demonstrates exceptional cycling stability over 1000 charge/discharge cycles, and noteworthy performances, including 96% capacity utilization, a minimal capacity fade rate of 0.0013% per cycle (1.3% over 1,000 cycles), high Coulombic efficiency and energy efficiency near 100% and 87%, respectively, all achieved under a current density of 20 mA·cm-². Furthermore, density functional theory unveils two potential coordination structures for Fe-NTMPA2 complexes, improving the understanding between the ligand coordination environment and electron transfer kinetics. When paired with a high redox potential Fe-Dcbpy/CN catholyte, 2,2'-bipyridine-4,4'-dicarboxylic (Dcbpy) acid and cyanide (CN) ligands, Fe-NTMPA2 demonstrates a notably elevated cell voltage of 1 V, enabling a practical energy density of up to 9 Wh/L.

Coupled Experimental-Theoretical Characterization of a Carbon Electrode in Vanadium Redox Flow Batteries using X-ray Absorption Spectroscopy.

Vanadium redox flow batteries (VRFBs) have emerged as promising solutions for stationary grid energy storage due to their high efficiency, scalability, safety, near room-temperature operation conditions, and the ability to independently size power and energy capacities. The performance of VRFBs heavily relies on the redox couple reactions of V2+/V3+ and VO2+/VO2+ on carbon electrodes. Therefore, a thorough understanding of the surface functionality of carbon electrodes and their propensity for degradation during electrochemical cycles is crucial for designing VRFBs with extended lifespans. In this study, we present a coupled experimental-theoretical approach based on carbon K edge X-ray absorption spectroscopy (XAS) to characterize carbon electrodes prepared under different conditions and identify relevant functional groups that contribute to unique spectroscopic features. Atomic models were created to represent functional groups, such as hydroxyl, carboxyl, methyl, and aldehyde, bonded to carbon atoms in either sp2 or sp3 environments. The interactions between functionalized carbon and various solvated vanadium complexes were modeled using density functional theory. A library of carbon K-edge XAS spectra was generated for distinct carbon atoms in different functional groups, both before and after interacting with solvated vanadium complexes. We demonstrate how these simulated spectra can be used to deconvolve ex situ experimental spectra measured from carbon electrodes and to track changes in the electrode composition following immersion in different electrolytes or extended cycling within a functional VRFB. By doing so, we identify the active species present on the carbon electrodes, which play a crucial role in determining their electrochemical performance.

SOMAS: a platform for data-driven material discovery in redox flow battery development.

Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database "Solubility of Organic Molecules in Aqueous Solution" (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system strings, molecular graphs, and three-dimensional atomic coordinates using four different neural network architectures-fully connected neural networks, recurrent neural networks, graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about the molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.

Graphical Gaussian process regression model for aqueous solvation free energy prediction of organic molecules in redox flow batteries.

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using the Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model for electrostatic interaction, the nonpolar interaction contribution of the solvent and the conformational entropy of the solute in the solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of the conformational entropy of the solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal mol-1 for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

Reversible ketone hydrogenation and dehydrogenation for aqueous organic redox flow batteries.

Aqueous redox flow batteries with organic active materials offer an environmentally benign, tunable, and safe route to large-scale energy storage. Development has been limited to a small palette of organics that are aqueous soluble and tend to display the necessary redox reversibility within the water stability window. We show how molecular engineering of fluorenone enables the alcohol electro-oxidation needed for reversible ketone hydrogenation and dehydrogenation at room temperature without the use of a catalyst. Flow batteries based on these fluorenone derivative anolytes operate efficiently and exhibit stable long-term cycling at ambient and mildly increased temperatures in a nondemanding environment. These results expand the palette to include reversible ketone to alcohol conversion but also suggest the potential for identifying other atypical organic redox couple candidates.

Synthesis and characterization of Pd complexes of a carbazolyl/bis(imine) NNN pincer ligand.

A serendipitously discovered construction of a carbazole nucleus by lithiation of N-methylated bis(4-methyl-2,6-dibromophenyl)amine is described. It was used to synthesize an NNN pincer-type ligand that combines a central carbazole site (N-methylated in the precursor ligand) with two flanking aldimine donors bearing mesityl substituents. The installation of this ligand on Pd was accomplished via an N-Me cleaving reaction with (COD)PdCl(2) producing MeCl and (NNN)PdCl (where NNN is an anionic carbazolyl/bis(imine) pincer ligand). Several (NNN)PdX complexes were characterized spectroscopically. (NNN)PdOTf ((-)OTf = triflate or (-)O(3)SCF(3)) readily reacted with stoichiometric amounts of water in benzene or dichloromethane to give a cationic water adduct [(NNN)Pd(OH(2))]OTf. An X-ray diffraction study on a single crystal of (NNN)PdCl revealed an almost perfectly square planar environment about Pd and an almost perfectly planar carbazole/bis(imine) conjugated system. Cyclic voltammetry of (NNN)PdCl showed quasi-reversible oxidation at E(1/2) = 0.72 V vs Fc/Fc(+) which is most likely ligand-based.