Engineering topics

Hollas, Aaron M.

Publications and source records attributed to Hollas, Aaron M..

4 records

DOE OSTI2024

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

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.

25 ENERGY STORAGE↗

DOE OSTI2024

Adoption Readiness Level Assessment of Redox Flow Batteries

Adoption readiness levels (ARLs) were developed by the Department of Energy’s Office of Technology Transitions (OTT) to holistically capture barriers to market adoption for a technology. The framework consists of 17 risk dimensions falling into 4 broad categories: Value Proposition, Market Acceptance, Resource Maturity, and License to Operate. OTT’s Commercial Adoption Readiness Assessment Tool (CARAT) can be used to evaluate a technology’s ARL. This work applies CARAT to redox flow batteries to evaluate the level of risk for this technology class across the 17 dimensions. Redox flow batteries were found to bear 1-2 high risk dimensions, 10-11 medium risk dimensions, 5 low risk dimensions, and scored an overall low readiness on the CARAT scoring scale (ranges reflect variation with flow battery chemistry). Herein, we describe the factors and evaluation across the dimensions leading to this score for redox flow batteries.

25 ENERGY STORAGE↗

DOE OSTI2024

Redox Activity Modulation in Extended Fluorenone-Based Flow Battery Electrolytes with π-π Stacking Effect

Redox flow battery shows promise for grid-scale energy storage. Aqueous organic redox flow batteries are particularly popular due to their potentially low material cost and safe water-based electrolyte. Commonly, redox active molecules used in this field feature aromatic rings, and increasing π-aromatic conjugation has been a popular strategy to achieve high energy density, high power density, and reduced crossover in new material design. However, this approach can inadvertently hinder redox activity depending on redox mechanism. This study reveals the underlying π-π stacking effect in extended aromatic redox active compounds, where aromatic radical intermediates are involved in the redox process. We report a molecular design strategy to mitigate the negative effect of π-π stacking by altering solvation dynamics and introducing molecular steric hindrance.

25 ENERGY STORAGE↗

DOE OSTI2024

Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗