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A procedure for ex-situ evaluation of chemical (alkaline) stability of anion exchange membranes (AEM). The IEC and ionic conductivity of an AEM is measured before and after being heated (typically at 80C in 1 M KOH) for ~1000 hours.
Washington University in St. Louis (WUSTL), in collaboration with The University of Texas at San Antonio (UTSA) and Giner Inc. developed highly selective anion exchange membranes (AEMs) and a novel electrode-decoupled redox flow battery (RFB) for grid scale energy storage as part of the ARPA-E IONICS program (with connections to the DAYS program in the later part of the project). RFBs exhibit the crucial characteristic of system-level decoupled scaling of energy and power which makes them cost effective for the multi-GWh scales envisioned for grid-scale energy storage solutions. This has led to extensive (and deserved) research interest and attention. This project aimed to enhance the design space available for redox-flow batteries (RFBs) by enabling the long-term separation of disparate cationic (elemental) actives using a highly selective membrane separator, while concurrently permitting the transport of anions to balance charge. The approach proposed was to design and develop a highly selective anion-exchange membrane (AEM), which would in turn permit the design and development of electrode-decoupled RFBs. Pairs of (different element) cationic species with redox reactions exhibiting a large difference in their standard electrode potentials were identified to develop high voltage, high power RFBs while disrupting the existing paradigm of using a single element which can ionize to more than two soluble oxidation states (e.g.: Vanadium).
Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.
Anion-exchange membrane water electrolyzers (AEMWEs) can accelerate the deployment of more efficient and affordable hydrogen production solutions. Here, electrode structure is shown to affect water back-diffusion and oxygen transport, which, in return, governs overpotential behaviors in AEMWEs. Measurements indicate that electrode with copious catalytic sites produces water close to the AEM, creating a higher water gradient and driving water back-diffusion, which improves membrane hydration and mass transport. In situ measurement reveals a high pH gradient near the anode surface, which affects anode kinetics. Operando measurement shows reduced oxygen accumulation when decoupling oxygen production and transport on anode. Catalyst ink rheology and stability are tuned with additives to realize scalable fabrication of electrodes with enhanced transport features, allowing AEMWE to operate at 2 A cm−2 for over 1,000+ h at a 2.3 μV h−1 degradation rate. Analysis during and post-durability provides insights into degradation mechanisms. This work demonstrates an electrode design strategy for efficient and durable AEMWEs.
A procedure for measuring the ion exchange capacity (IEC) of an anion exchange membrane (AEM). This is done via a titration method (automated or manual).
A procedure for measuring the hydroxide conductivity of an anion exchange membrane (AEM). A four-electrode In-plane conductivity is measured as a function of relative humidity (RH).
The present disclosure is directed to triblock copolymer based anion exchange membranes (AEMs) and methods for making same. The membranes are useful as separators in electrochemical devices, such as fuel cells, electrolyzers, water desalination systems, and redox flow batteries.
Alkaline low temperature electrolysis (LTE) systems enjoy several potential advantages over acid-based LTE systems including facile oxygen evolution reaction (OER) kinetics and electrodes that can use little to no platinum group metals (PGM). The polymer membranes and membrane electrode assembly (MEA) structures going into alkaline electrochemical systems have seen significant advances in recent years. The objective of this project is to combine state-of-the-art alkaline polymer electrolyzer components into one optimized membrane electrode assembly (MEA) system to achieve DOE low temperature electrolysis (LTE) goals. The benefit of individual component advances cannot be fully appreciated until all aspects and components of the MEA are working at an equally high level of performance. New electrode fabrication methods were developed where solvent cast catalyst/ionomer solutions were used in place of insoluble ionomers to significantly improve adhesion. The membranes were improved in terms of durability and mechanical properties. The OER and HER catalysts were improved and made more durable.
The development of efficient, durable, and low-PGM electrocatalysts for the hydrogen evolution reaction (HER) in alkaline media is critical for next-generation electrolysis technologies. We report a facile two-step synthesis of highly dispersed PtNi and PtNi-nitride nanoclusters (NCs) (~2.3 nm) with ultralow Pt content (0.5 at.%) anchored on N-doped Vulcan carbon. Structural and compositional characterization via XAS, XPS, HAADF-STEM, HRTEM, and EDS mapping established key structure–activity relationships across varying Pt/Ni ratios and pyrolysis temperatures. The Pt0.5Ni0.5/C-750 catalyst, an ensemble of PtNi M-N-C type single-atom (SA) moieties with neighboring PtNi nanoclusters (NC), exhibited superior HER performance in alkaline media, achieving overpotentials of 30, 115, and 210 mV at 10, 100, and 500 mA cm−2, respectively. Despite at a lower Pt content, this novel SA–NC ensemble outperformed commercial Pt/C by ~36%. A standardized literature comparison with contemporary Pt- and Ru-doped analogues reveals the as-prepared Pt0.5Ni0.5/C-750 to sit at the apex of Tafel-limited kinetics and low overpotential at 100 mA cm−2. Tafel-limited Tafel slopes in both alkaline and acidic regimes confirm favorable proton recombination kinetics. Mass activities at 200 mV reached 13.8 and 18.84 A mgPt−1 in alkaline and acidic media, respectively. However, excessive nitridation (e.g., at 650 °C) adversely altered Pt electronic structure and HER kinetics. While Ni enhanced alkaline HER, acidic HER favored Ni-free analogues. Pt0.5Ni0.5/C-750 also demonstrated robust temperature responsiveness and 300-h operational stability at high current densities (0.5–1.0 A cm−2) in MEA tests. This work presents a scalable strategy for designing thermally responsive, durable, and compositionally tunable NC catalysts with neighboring SA moieties for alkaline electrolysis.
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The discharge of excessive phosphorous into water bodies can lead to serious eutrophication threatening aquatic ecosystem. Membrane capacitive deionization (MCDI) is an effective platform for deionizing aqueous streams; however, conventional MCDI is unable to selectively remove targeted ions from a liquid mixture. Here, in this work, we fabricated manganese oxide composite anion exchange membranes (AEMs) for MCDI to enhance phosphate removal selectivity from sodium chloride-sodium dihydrogen phosphate (10:1 M ratio) aqueous mixtures. We systematically investigated several critical factors, such as constant current or voltage operation, applied voltage amount, process stream pH, and manganese oxide (Mn 2 O 3 ) content in the AEM, on phosphate removal efficiency and phosphate selectivity. A trade-off was observed between phosphate removal and selectivity when increasing the cell voltage. Under the best conditions, a MCDI unit with a 20 wt% Mn 2 O 3 composite AEM and a bipolar membrane facilitated high phosphate removal efficiency of ≥ 31.8 % and a phosphate over chloride selectivity of 1.1 while showing stability for at least 30 cycles. To help understand how Mn 2 O 3 composite AEM boosts phosphate selectivity, static electronic structure calculations were performed, and they revelated that hydrogen phosphate absorption on Mn 2 O 3 composite AEM was 314 kcal/mol more exothermic than that on pristine AEM while chloride adsorption on Mn 2 O 3 composite AEM was 2.2 kcal/mol less exothermic than that on a pristine AEM. Overall, this work presents an effective strategy for selectively removing phosphate from model wastewater solutions and the mechanistic understanding that governs ion selectivity in composite ion-exchange membranes used in MCDI.
In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) – such as metamaterials, photonic crystals, and plasmonics – to achieve some desired scattering properties (e.g., transmission or reflection spectrum). DIMs are deep neural networks (i.e., deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. Furthermore, we publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.
Most commercial anion exchange membranes (AEMs) deploy quaternary ammonium moieties. Alternative cation moieties have been explored in AEMs for fuel cells, but there are no studies focused examining alternative tethered cations in AEMs for ionic separations—such as organic acid anion transport via electrodialysis. H-cell and conductivity experiments demonstrate that tethered benzyl 1-methyl imidazolium groups in polysulfone AEMs enhance lactate conductivity by 49% and improved lactate anion flux by 24x when compared to a quaternary benzyl ammonium polysulfone AEM. An electrodialysis demonstration with the imidazolium-type AEM showed a 2x improvement in lactate anion flux and 20% improvement in permselectivity when benchmarked against the quaternary ammonium AEM. Molecular dynamics and 2D NOESY NMR revealed closer binding of lactate anions to the imidazolium cations when compared to the quaternary ammonium cation. It is posited that this closer binding is responsible to greater flux values observed with imidazolium-type AEM.
Anion-exchange membranes (AEMs) are a possible replacement for perfluorosulfonic-acid membranes in energy-conversion devices, primarily due to the hydroxide mobile ion allowing the devices to operate in alkaline conditions with less expensive electrocatalysts. However, the transport properties of AEMs remain understudied, especially electro-osmosis. In this work, an electrochemical technique, where the open-circuit voltage is measured between two ends of a membrane maintained at different relative humidities, is used to determine the water transport number of various ionomers, including Versogen and Sustainion AEMs and Nafion cation-exchange membrane (CEM), as a function of water content and temperature. In addition, the CEMs and AEMs are examined in differing single-ion forms, specifically proton and sodium (CEM) and hydroxide and carbonate (AEM). Carbonate-form AEMs have the highest transport number (∼11), followed by sodium-form CEMs (∼8), hydroxide-form AEMs (∼6), and proton-form CEMs (∼3). Finally, a multicomponent transport model based on the Stefan-Maxwell-Onsager framework of binary interactions is used to develop a link between water transport number and water-transport properties, extracting a range for the unmeasured membrane water permeability of Versogen as a function of water content.
Anion exchange membranes (AEMs) have emerged as a promising technology for water electrolysis in hydrogen production since they offer significant cost reduction in choices of electrocatalysts and bipolar plates. However, AEMs satisfying multiple requirements of high ionic conductivity, good chemical stability, robust mechanical properties, scalable synthesis, and low manufacturing costs are rare. Herein, we introduce quaternary ammonium functionalized cyclic olefin copolymers (COCs) as a new class of chemically stable and low-cost AEM materials. To further enhance the mechanical robustness, we prepared reinforced composite AEMs by impregnating the ionically functionalized COC into a mechanically robust matrix. The resulting reinforced composite membrane exhibits a high hydroxide conductivity of 127 mS cm −1 and excellent mechanical strength. In water electrolyzers, the MEA demonstrated outstanding performance, achieving a current density of 2.24 A cm −2 at 1.8 V, attributable to high conductivity, enhanced mechanical properties, and good alkaline stability of the composite membrane. These results indicate that the COC-based AEMs demonstrate good potential for application in AEM electrolyzers.