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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Phase Field Modeling of Corrosion for Design of Next-Generation Magnesium-Aluminum Vehicle Joints

In this study, WPI and its partners sought to understand galvanic corrosion in Mg-Al friction stir weld (FSW) joints. Unlike fusion welds, FSW is done in the solid state. This limits (but does not eliminate) the formation of brittle Al-Mg intermetallics, resulting in better joint strength and corrosion resistance. This study aimed to understand corrosion behavior of Mg-Al FSW joints by using a Cahn-Hilliard phase field electrochemistry model based on the work of Pongsaksawad et al. Its context challenge problem was an ultra-light door designed by Magna with 6061-Al sheet outer and ZEK100-Mg sheet inner panels, joined by an adhesive and FSW. Unlike most FSW joints, which weld through the softer material into the harder one, the Al sheet’s hem joint around Mg required the FSW tool to through the harder Al into the softer Mg, which was itself a challenge. Despite this challenge, PNNL succeeded in consistently making very strong FSW lap joints, by using a triflute tool and power control. Characterization showed mostly separate Al and Mg regions in the weld, usually with a hook protruding from Al into Mg, which likely contributed to high joint strength. Nanohardness mapping showed higher hardness in thin Mg swirls into the Al nugget, likely due to the intermetallics. The study used the industry standard SAE J2334 Cyclic Corrosion Test (CCT) to simulate corrosion conditions over the life of a vehicle, as well as linear polarization testing and ASTM G71 pitting corrosion testing. Corrosion reactions were: H 2 O + ½O 2 + 2e - → 2OH - at the Al cathode, and either Mg → Mg 2+ + 2e - or Mg + 2OH - → Mg(OH) 2 + 2e - at the Mg anode, with electron transfer through the joint. Mass loss increased with Al section length, supporting the hypothesis that galvanic corrosion in Al-Mg joints is limited by the cathodic reaction. Both pitting corrosion (first anode reaction) and hydroxide film formation (second reaction) were observed. The J2334 CCT test method was slightly modified, adding a chromium solution cleaning step after each week of corrosion testing, in order to remove Mg oxides and hydroxides and accurately measure mass loss over time. That said, FSW joints treated in this way did not did not exhibit significant reduction in lap shear strength of the joints vs. newly welded samples which had not undergone corrosion. Corrosion appeared to consist mainly of pitting in Mg in a way which did not directly affect the bond between the materials where the joints failed. A Cahn-Hilliard phase field model coupled with electrical potential described the electronically mediated galvanic corrosion electrochemical reactions described above in the four-component Al-Mg-H 2 O-(H 2 O+½O 2 ) system. This 2-D model successfully predicted Mg(OH)₂ formation at the anode in some circumstances, and Mg pitting corrosion in others, and showed the correct electric field directions in both cases. The model predicted Mg pit corrosion depth within a factor of two of measured pits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Optimizing Nonaqueous Sodium–Polysulfide Redox-Flow Batteries: The Role of Solvation Effects with Glyme Solvents

Nonaqueous redox-flow batteries (NARFBs) that use economical alkali metals and the corresponding metal polysulfides are highly attractive for grid-scale energy storage. Although sodium–sulfur systems have been recognized as promising candidates and have been the focus of many studies due to their high earth abundance and energy density, an understanding of the role of the solvation chemistry of commonly used glyme solvents is missing. Herein, we report a systematic investigation into the solvation effects of glyme-based Na-S electrolytes through comprehensive physiochemical experiments and Density Functional Theory (DFT) simulations. Our findings revealed, on one hand, that an optimal coordination strength between glymes and Na + could maintain a relatively smooth Na + diffusion. On the other hand, glyme solvents with extended chain lengths shift the reduction potential of S 8 2– negatively to elevate the formation barrier of undesirable short-chain polysulfides (S n 2– , n ≤ 4) that have high membrane permeability. This solvation phenomenon not only mitigates capacity fading but also extends the operational longevity of the Na-S NARFBs. In conclusion, the results underscore the critical roles of balanced solvent–cation interactions and controlled redox potentials in improving the stability and efficiency of Na-S NARFB systems, marking a significant advancement in the development of sustainable energy storage solutions.

25 ENERGY STORAGE↗
DOE OSTI2021

Ultrafast charging of energy-dense lithium-ion batteries for urban air mobility

Urban air mobility (UAM) demands batteries with high energy density, long cycle life, and fast rechargeability. In this paper, we demonstrate an energy-dense lithium-ion battery (LiB) with ultralong cycle life under ultrafast charging. By using the asymmetric temperature modulation (ATM) method, i.e., charging at an elevated temperature and discharging around the ambient temperature, it is experimentally shown that the 209 Wh/kg LiB is charged to 88% state of charge (SOC) in ~5 min under UAM cycling while retaining 97.7% capacity after 1,000 cycles. Moreover, an experimentally validated electrochemical-thermal (ECT) model is developed to elucidate the fast charging process and the degradation mode of UAM batteries, quantitatively capturing lithium plating during fast charging. We find that the LiBs for UAM applications are most prone to lithium plating due to their higher initial SOC required as the reserve for safety; nevertheless, the ATM method is effective in minimizing or preventing lithium plating in the high SOC range of 30-90%. In addition to slowing down capacity fade, the ATM method also raises the usable capacity by 10%, which boosts the battery energy density and ensures the battery to perform full UAM cycles even at the end of life.

25 ENERGY STORAGE↗
DOE OSTI2023

Canister Centerline Cooling Experiments for DPF5-336 Reference Material Made with ERV3b Salt Simulant

This report provides experimental details and results on the elemental distributions (compositions), crystalline/amorphous phase distribution, and microstructures for DPF5-336 reference materials made with ERV3b salt that were subjected to the canister centerline cooling (CCC) heat treatment process. To fabricate these materials, ammonium dihydrogen phosphate was mixed with ERV3b salt simulant and a dechlorination procedure was run in the generation-2 dechlorinator at the Pacific Northwest National Laboratory up to 600°C in an alumina crucible. After the dechlorination process, the product was removed from the crucible, lightly crushed, loaded into a new alumina crucible, Fe 2 O 3 was added, and this was vitrified at 1100°C for 1 hour and quenched. The quenched material was mostly amorphous but showed some small diffraction peaks attributed to Li 3 Fe 2 (PO 4 ) 3 . This quenched material was ground to a fine particle size in a milling chamber, and this was subjected to the CCC cooling profile in duplicate within alumina and fused quartz crucibles. The CCC experiment is designed to simulate the slowest cooling profile (the vertical centerline) of a 0.61-m diameter canister where the slower cooling profile often results in the production of a variety of different crystalline phases upon formation of which result in a residual glass of unknown composition and properties. The information gleaned from these types of experiments will lead to improved composition-property predictions for materials with similar compositions. The results of the alumina and fused quartz experiments show different microstructures but similar diffraction patterns providing evidence of similar crystalline phases being present in each sample. Both samples had monazite [i.e., (Ce,Nd)PO 4 ] and mixtures of phases containing P, Fe, and alkali metal elements. This report completes the milestone M4FT-23PN030104042 with details provided in Appendix B.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Charting C–C coupling pathways in electrochemical CO 2 reduction on Cu(111) using embedded correlated wavefunction theory

The electrochemical CO 2 reduction reaction (CO 2 RR) powered by excess zero-carbon-emission electricity to produce especially multicarbon (C 2+ ) products could contribute to a carbon-neutral to carbon-negative economy. Foundational to the rational design of efficient, selective CO 2 RR electrocatalysts is mechanistic analysis of the best metal catalyst thus far identified, namely, copper (Cu), via quantum mechanical computations to complement experiments. Here, we apply embedded correlated wavefunction (ECW) theory, which regionally corrects the electron exchange-correlation error in density functional theory (DFT) approximations, to examine multiple C–C coupling steps involving adsorbed CO (*CO) and its hydrogenated derivatives on the most ubiquitous facet, Cu(111). We predict that two adsorbed hydrogenated CO species, either *COH or *CHO, are necessary precursors for C–C bond formation. The three kinetically feasible pathways involving these species yield all three possible products: *COH–CHO, *COH–*COH, and *OCH–*OCH. The most kinetically favorable path forms *COH–CHO. In contrast, standard DFT approximations arrive at qualitatively different conclusions, namely, that only *CO and *COH will prevail on the surface and their C–C coupling paths produce only *COH–*COH and *CO–*CO, with a preference for the first product. This work demonstrates the importance of applying qualitatively and quantitatively accurate quantum mechanical method to simulate electrochemistry in order ultimately to shed light on ways to enhance selectivity toward C 2+ product formation via CO 2 RR electrocatalysts.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Photoelectrochemical Nitrogen Reduction to Ammonia on Cupric and Cuprous Oxide Photocathodes

Photoelectrochemical N 2 reduction enables the production of NH 3 under ambient conditions using water as the hydrogen source. Furthermore, by utilizing solar energy, photoelectrochemical N 2 reduction can significantly reduce the energy input required for N 2 reduction. In this study, photoelectrochemical N 2 production was investigated using CuO and Cu 2 O photocathodes that are known to be poorly catalytic for water reduction, the major reaction competing with N 2 reduction. When tested under simulated solar illumination with isotopically labeled 15 N 2 in 0.1 M KOH solution, the CuO and Cu 2 O photocathodes produced 15 NH 3 with a Faradaic efficiency of 17% and 20% at 0.6 V and 0.4 V vs. RHE, respectively. These potentials are significantly more positive than the thermodynamic reduction potential of N 2 , which demonstrates how the use of photoexcited electrons in the CuO and Cu 2 O photocathodes can reduce the energy input required for NH 3 production. Here, the use of photoexcited electrons in these photocathodes for N 2 reduction, water reduction, and photocorrosion was carefully examined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Constant-potential molecular dynamics simulations of molten salt double layers for FLiBe and FLiNaK

We report the results of constant-potential molecular dynamics simulations of the double layer interface between molten 2LiF–BeF 2 (FLiBe) and 23LiF–6NaF–21KF (FLiNaK) fluoride mixtures and idealized solid electrodes. Employing methods similar to those used in studies of chloride double layers, we compute the structure and differential capacitance of molten fluoride electric double layers as a function of applied voltage. The role of molten salt structure is probed through comparisons between FLiBe and FLiNaK, which serve as models for strong and weak associate-forming salts, respectively. In FLiBe, screening involves changes in Be–F–Be angles and alignment of the oligomers parallel to the electrode, while in FLiNaK, the electric field is screened mainly by rearrangement of individual ions, predominantly the polarizable potassium cation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Living Filter Designs for In-Line Recovery and Sorting of Critical materials

This project explored “bio-mining” of critical materials from electronic waste (E-waste) streams by developing living filters arrays capable of recovering these materials, focusing on the platinum group metals (PGMs) and rare earth elements (REEs). While highly toxic, E-waste is considered a valuable “urban mine” as it contains critical materials such as PGMs and REEs with orders of magnitude higher purity than the richest ores. The aim of the project was to: (1) develop mechanically robust, silk-based biomaterial filtration membranes that can capture REEs from dilute aqueous waste; (2) design and build 3D bioprinted living filters containing encapsulated electrochemically active bacteria (EAB) capable of bio-reducing PGMs and recovering them from waste streams; and (3) constructing combined living filter arrays using both components to efficiently capture REE and PGM from the same waste stream. The developed silk-based filtration membranes were self-assembled using silk-nanofibrils (SNFs) derived from silkworm (Bombyx mori) cocoons in conjunction with recombinant silk-elastin-like proteins (SELPs), which contained lanthanide-binding peptide tags (LBTs) with a high affinity and specificity towards REEs. These 100% biodegradable protein-based membranes were capable of recovering up to 85% of model REE ions (Tb 3+ ) filtered through the membrane, with ~50% recovery achieved in the presence of high concentrations (100X) of common interfering metals (Ca 2+ , Cu 2+ , Fe 3+ , Zn 2+ ). REE captured by the membranes were easily recovered by applying a low pH desorption buffer. The membranes also demonstrated substantial reusability, with only a 30% loss in binding capacity after 4 cycles of REE binding and recovery. To recover the PGMs, we created a bottom-up assembling strategy to construct a living hydrogel composed of a seamlessly integrated living catalyst, Shewanella loihica PV-4 (PV-4), for metal reduction, and their structural and functional linkers, bio-reduced graphene oxide (B-rGO). This hydrogel demonstrated a close to 90% recovery of model metal ions, Pd, from a simulated e-waste leaching stream with minimum-to-no biomass production. It’s also worth noting that the Pd recovery is initiated immediately after the introduction of living hydrogel, compared to conventional biocarriers that required start-up times within hours to days. Overall, these living hydrogels demonstrated superior bioactivity, structural integrity, and agility over existing biocarriers, which offers extensive opportunities to advance the biological metal recovery with unparallel efficiency, reduced energy/material consumption, and minimal environmental impact.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fabrication, Modeling, and Testing of a Prototype for Particle Thermal Energy Storage Containment

Increasing penetration of variable renewable energy resources requires the deployment of energy storage at a range of durations. Long-duration energy storage (LDES) technologies will fulfill the need to firm variable renewable energy resource output throughout the year. Conventional electrochemical batteries (e.g., lithium-ion) are uneconomical in this role due to high energy capacity costs. Thermal energy storage (TES) is one promising technology for LDES applications because of its siting flexibility and ease of scaling. Particle-based TES systems use low-cost solid particles that have higher temperature limits than the molten salts used in traditional concentrated solar power systems. A key component in particle-based TES systems is the containment silo for the high-temperature (> 1100 degrees C) particles. This study combined experimental testing and computational modeling methods to design and characterize the performance of a particle containment silo for LDES applications. A containment silo prototype was built at a laboratory scale and used to validate a congruent transient finite element analysis (FEA) model. The validation compared the actual and predicted temperature profile through the prototype over six days as the particles cooled from their initial temperature. The performance of a commercial scale (> 5 GWhth) was then characterized using the validated model. The transient FEA model was subject to several charge-discharge cycles to mimic a possible operating schedule. The commercial-scale model predicted a storage efficiency in excess of 95% after five days of storage with a design storage temperature of 1200 degrees C. Insulation material and concrete temperature limits were considered as well. The validation of the methodology means the FEA model can simulate a range of scenarios for future applications. This work supports the development of a promising LDES technology with implications for grid-scale electrical energy storage, but also for thermal energy storage for industrial process heating applications.

ENERGY STORAGE↗
DOE OSTI2021

Designing Modules to Prevent Reverse Bias Degradation in Perovskite Solar Cells when Partial Shading Occurs

When a solar cell in a panel is shaded, the illuminated cells can place a large reverse bias on the shaded cell to attempt to force current through it. Although the panel can continue to produce power, the reverse bias can cause significant problems for the shaded cell. In the case of perovskite solar modules, Joule heating and irreversible electrochemical reactions will degrade the cell. Some photovoltaic technologies use bypass diodes to solve this problem. To prevent both a perovskite cell and an all-perovskite tandem cell from falling into reverse bias breakdown while shaded, no more than two cells per bypass diode are allowed, which is likely to be prohibitively expensive for many applications. Herein, how many solar cells can be protected by one bypass diode in single-junction and multijunction perovskite modules is explored. It is easier to incorporate bypass diodes into modules with singulated cells than with monolithic panels. It is shown that perovskite–silicon tandems can be protected with fewer bypass diodes than with single-junction perovskite modules. Furthermore, it is suggested that if bypass diodes cannot be feasibly incorporated, then the panels should be deployed in utility-scale power plants.

14 SOLAR ENERGY↗
DOE OSTI2025

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2023

Crystalline versus Glassy Nature of Iron Phosphate Waste Forms Subjected to Different Slow Cooling Curves

This report provides experimental details and associated results on the elemental distributions (compositions), crystalline/amorphous phase distributions, and microstructures for iron phosphate reference materials (DPF5-336) made with different cooling curves and starting from different temperatures (i.e., 1050°C and 1200°C). The primary goal of this work was to evaluate the properties of the DPF5- 336 reference material after it was melted and cooled at different rates to simulate the cooling profile of different types of canisters. Crystal fractions across the SCC#2, SCC#3, and SCC#5 samples were all similar ranging in crystal content of 35.66–41.14 mass% with amorphous fractions being the balance (64.34–58.86 mass%, respectively). This shows that the heat treatment profile did not seems to greatly affect either the total crystal content or the phase distributions. The quenched sample was almost completely amorphous (99.55 mass%) with very small peaks identified as Li 3 Fe 2 (PO 4 ) 3 whereas the SCC-treated materials showed peaks for what appear to be ten separate phases present in various concentrations. Another goal of these experiments was to see how the phase distribution affected chemical durability of these waste forms, but that information is being collected at Argonne National Laboratory and will be published in a separate report

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Tandem Electrocatalytic CO 2 Reduction with Efficient Intermediate Conversion over Pyramid-Textured Cu–Ag Catalysts

If combined with renewably generated electricity, electrochemical CO 2 reduction (E-CO 2 R) could be used as a sustainable source of chemicals and fuels. Tandem catalysis approaches are attractive for providing the product selectivity, which would be required for commercial applications. Here, we demonstrate a two-step tandem electrocatalytic E-CO 2 R with efficient conversion of the intermediate species. The catalyst scaffold is Si(100), which is etched to form a textured surface consisting of micron-sized pyramid structures with the {111} facets. Two metals are used in the electrocatalytic cascade: Ag is employed to perform a two-electron reduction of CO 2 to the intermediate CO, and Cu performs conversion to more reduced products. Using high-angle physical vapor deposition, we form separated, micron-scale areas of the two electrocatalysts on opposite sides of the pyramids, with their relative surface coverages being tunable with the deposition angle. Compared to the textured scaffolds with blanket Ag and Cu used as controls, bimetallic pyramid tandem catalysts have higher current densities and much lower faradic efficiencies (FE) for CO. These effects are due to efficient conversion of the CO formed on Ag to more reduced products on Cu. Methane is the main product to be enhanced by the cascade pathway: a bimetallic catalyst with approximately equal coverages of Ag and Cu produces methane with a FE of 62% at -1.1 V RHE , corresponding to a partial current density of 12.7 mA cm -2 . We estimate an intermediate conversion yield for the CO intermediate of 80-90%, which is close to the mass-transport limited value predicted by reaction-diffusion simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Technology Strategy Assessment: Findings from Storage Innovations 2030 Supercapacitors

Electrochemical capacitors, which are commercially called supercapacitors or ultracapacitors, are a family of energy storage devices with remarkably high specific power compared with other electrochemical storage devices. Supercapacitors do not require a solid dielectric layer between the two electrodes, instead they store energy by accumulating electric charge on porous electrodes filled with an electrolyte solution and separated by an insulating porous membrane. Supercapacitors offer large specific capacitance and high power output. They can be charged and discharged very quickly, offer excellent cycle life and long operational life, and operate over a broad temperature range. The major drawbacks of supercapacitors are low energy density and a high self-discharge rate. For example, a supercapacitor passively discharges from 100% to 50% in a month compared with only 5% for a lithium-ion battery [1]. The high capital cost and low energy density of supercapacitors make the unit cost of energy stored ($/kWh) more expensive than alternatives such as batteries. Their attributes make them attractive for uses in which frequent small charges/discharges are required (e.g., ensuring power quality or providing frequency regulation). Their attributes and cost make them less attractive for long-duration energy storage, which favors technologies with low self-discharge that cost less per unit of energy stored.

25 ENERGY STORAGE↗
DOE OSTI2023

Review of Carbon Support Coordination Environments for Single Metal Atom Electrocatalysts (SACS)

This topical review focuses on the distinct role of carbon support coordination environment of single-atom catalysts (SACs) for electrocatalysis. The article begins with an overview of atomic coordination configurations in SACs, including a discussion of the advanced characterization techniques and simulation used for understanding the active sites. A summary of key electrocatalysis applications is then provided. These processes are oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), nitrogen reduction reaction (NRR), and carbon dioxide reduction reaction (CO 2 RR). The review then shifts to modulation of the metal atom-carbon coordination environments, focusing on nitrogen and other non-metal coordination through modulation at the first coordination shell and modulation in the second and higher coordination shells. Representative case studies are provided, starting with the classic four-nitrogen-coordinated single metal atom (M$-$N 4 ) based SACs. Bimetallic coordination models including homo-paired and hetero-paired active sites are also discussed, being categorized as emerging approaches. The theme of the discussions is the correlation between synthesis methods for selective doping, the carbon structure–electron configuration changes associated with the doping, the analytical techniques used to ascertain these changes, and the resultant electrocatalysis performance. In conclusion, critical unanswered questions as well as promising underexplored research directions are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2022

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

Numerical simulation has become indispensable in advancing cost-effective process optimization and control of flow batteries. We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation conditions. However, limited by the simplifications of the 0D model, the PCDNN cannot capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout the charge–discharge cycle, including the tail region of the voltage discharge curve. The loss function for training the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. In conclusion, this allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.

25 ENERGY STORAGE↗