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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Alkaline Zinc Passivation Mechanism is Controlled by Hydroxide Concentration

We studied Zn passivation and oxide growth in Zincate (Zn(OH) 4 2− ) in 4 and 8 M KOH solutions using an electrochemical quartz crystal microbalance (EQCM), building on our initial work at 1 M KOH where passivation was kinetically controlled. A porous passivating oxide spontaneously forms on Zn electrodes when KOH is above 4 M and saturated with zincate. However, passivation does not occur when bulk zincate concentration is decreased, resulting in continual Zn dissolution. EQCM data suggests that the passivation mechanism is strongly affected by pOH. Mass transport and kinetic processes in the 4 M KOH electrolytes couple and govern Zn passivation. At 8 M, KOH concentration shifts passivation to mass transport control. We explain this by the increased solubility of Zn(OH) 3 − with increasing pOH. The variation in the mechanism of passivation implications for how passivation is handled in Zn-alkaline batteries. The importance of controlling the mass transport increases with increased pOH, suggesting that electrode design, additives, and potential flow should be optimized. At lower pOHs, kinetics and mass transport must be balanced to manage passivation effectively. Additionally, the changing nature of the native oxide layer on the surface has implications for the evenness of deposition and dissolution on the Zn electrode.

Wittman, Reed M. [Oak Ridge National Laboratory (O↗
DOE OSTI2025

Controlling magnetism and transport at perovskite cobaltite interfaces via strain-tuned oxygen vacancy ordering

Complex oxides such as perovskite cobaltites exhibit rich phenomena at interfaces due to the complex interplay between their structural, defect, electronic, and magnetic degrees of freedom. Here, we study this interplay here in the ferromagnetic metallic cobaltite La 1−𝑥 ⁢Sr 𝑥⁢ CoO 3−𝛿 , using specific substrates to vary both the heteroepitaxial strain (compressive vs tensile) and growth orientation ((001) vs (110)). Transmission electron microscopy, electron energy-loss spectroscopy, high-resolution X-ray diffraction, magnetometry, polarized neutron reflectometry, and electronic magnetotransport measurements are applied. Lattice mismatch and growth orientation are found to enable the precise control of interfacial oxygen vacancy ordering in La 1−𝑥⁢ Sr 𝑥 ⁢CoO 3−𝛿 , thus dictating strain relaxation and oxygen vacancy depth profiles, in turn controlling thickness-dependent magnetic and electronic properties. In particular, compressive strain and (110) orientations minimize deleterious magnetic/electronic dead layer effects, leading to the optimization of interfacial magnetism and transport. Strain and orientation tuning of oxygen vacancy ordering are thus established as powerful means to control physical properties at cobaltite-based interfaces, relevant to several fields.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Promising performance of sulfide catholytes compared to halide alternatives in NMC811 cathodes for sheet-type sulfide solid-state batteries

Sulfide-based solid-state batteries (SSBs) show promise in achieving energy densities over 350 Wh/kg, yet challenges persist with their incorporation of high-voltage, nickel-rich layered oxide cathodes, such as LiNi₀.₈Mn₀.₁Co₀.₁O₂ (NMC811), due to the poor oxidation stability of sulfide solid-state electrolytes (SSEs) like Li₆PS₅Cl (LPSCl). Although halide SSEs such as Li₃InCl₆ (LIC) and Li₃YCl₆ (LYC) have previously shown promise in stabilizing high-voltage NMC cathodes, our research reveals that sulfide SSE catholytes, particularly when combined with surface-coated NMC cathodes, deliver superior performance. Here, this investigation assesses the cycling stability of various catholytes—LPSCl, LIC, Li₁₀GeP₂S₁₂ (LGPS), combined LIC-LPSCl, and LYC-LPSCl—in SSBs with LiNbO₃-coated NMC811 cathodes against sheet-type LPSCl separators. Findings indicate that while LGPS-based cathodes maintain higher capacity retention, they yield lower deliverable capacity, and LIC cathodes experience significant electrochemical degradation. Importantly, our results underscore that sulfide SSE catholytes, in conjunction with LiNbO₃-coated cathodes, optimize the cathode-electrolyte interphase (CEI), enhancing both kinetics and mass transport. These insights provide a strategic direction for optimizing catholyte composition in the development of sheet-type sulfide-based SSBs.

Catholyte↗
DOE OSTI2020

Optimization of barrier height in InGaN quantum wells for rapid interwell carrier transport and low nonradiative recombination

Rapid interwell carrier transport is a key process for a uniform carrier distribution and reduced Auger recombination in multiple quantum well (MQW) light emitting devices. In this work, the interwell transport has been studied by time-resolved photoluminescence in In 0.12 Ga 0.88 N MQWs with In x Ga 1–x N (x = 0 - 0.06) and Al 0.065 Ga 0.935 N barriers. Only for the InGaN barriers the transport is efficient. Furthermore, introduction of In into the barriers is accompanied by an increase of the nonradiative recombination at QW interfaces. Still, even with the increased Shockley–Read–Hall recombination, structures with InGaN barriers might be advantageous for high power devices because of the reduced Auger recombination.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗
DOE OSTI2023

Tailoring the Mass Transport to Achieve High-Performing Unitized Regenerative Fuel Cells Considering Practical Operating Conditions

Unitized regenerative fuel cells (URFCs) convert electrical energy to chemical bonds in hydrogen during charge and convert chemical energy to output electricity during discharge, offering a promising solution to long-term energy storage. Recent studies indicate that the round-trip-voltaic efficiency (RTE) and longevity of URFCs are limited by complex mass transport during charging and discharging. Here, we first investigate how different porous transport layer (PTL) structures can impact URFC performance. The preferred PTL has a low tortuosity and high porosity, leading to a high RTE above 50% at 1 A cm −2 using Nafion 212. Moreover, thicker membranes, such as Solvay 90, are required to ensure mechanical stability and minimize H 2 crossover when operating under high differential pressure. Although this assembly inevitably leads to a higher ohmic loss, the RTE can be improved by further tailoring the electrode structures to facilitate mass transport by using supported catalyst, which still achieves over 50% RTEs at 1 A cm −2 . Optimization of porous structure to mitigate mass transport resistance with appropriate materials down selection considering practical application requirements can be a key design principle for achieving high-performing URFCs.

25 ENERGY STORAGE↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗
DOE OSTI2022

DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy

DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter-I p steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high-Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. For the non-nuclear phase of ITER, two actuators are identified to lower the L–H threshold power in hydrogen plasmas. With this physics understanding and suite of capabilities, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q = 10 in ITER at ~8 MA was coupled to a detached divertor, and a near super H-mode optimized-pedestal scenario with co-I p beam injection was coupled to a radiative divertor. The hybrid core scenario was achieved directly, without the need for anomalous current diffusion, using off-axis current drive actuators. Also, a controller to assess proximity to stability limits and regulate β N in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally, innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Diii-D Research Advancing The Physics Basis For Optimizing The Tokamak Approach To Fusion Energy

DIII-D physics research addresses critical challenges for operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter-Ip steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs. ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high-Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. With this physics understanding and suite of actuators, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q=10 in ITER at 9 MA was coupled to a detached divertor, and a near Super H-mode optimized-pedestal scenario with co-Ip beam injection, was coupled to a radiative divertor. The Hybrid core scenario was achieved directly, without the need for internal flux pumping, using off-axis current drive actuators, and a controller to avoid mode locking and disruptions in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation.

Fenstermacher, M.↗
DOE OSTI2022

Carbon-Binder Weight Loading Optimization for Improved Lithium-Ion Battery Rate Capability

Battery performance is strongly correlated with electrode microstructure and weight loading of the electrode components. Among them are the carbon-black and binder additives that enhance effective conductivity and provide mechanical integrity. However, these both reduce effective ionic transport in the electrolyte phase and reduce energy density. Therefore, an optimal additive loading is required to maximize performance, especially for fast charging where ionic transport is essential. Such optimization analysis is however challenging due to the nanoscale imaging limitations that prevent characterizing this additive phase and thus quantifying its impact on performance. Herein, an additive-phase generation algorithm has been developed to remedy this limitation and identify percolation threshold used to define a minimal additive loading. Improved ionic transport coefficients from reducing additive loading has been then quantified through homogenization calculation, macroscale model fitting, and experimental symmetric cell measurement, with good agreement between the methods. Rate capability test demonstrates capacity improvement at fast charge at the beginning of life, from 37% to 55%, respectively for high and low additive loading during 6C CC charging, in agreement with macroscale model, and attributed to a combination of lower cathode impedance, reduced electrode tortuosity and cathode thickness.

25 ENERGY STORAGE↗
DOE OSTI2024

Carbon-Binder Optimization for Lithium-Ion Battery Extreme Fast Charge

Battery performance is strongly correlated with electrode microstructure and weight loading of the electrode components. Among them are the carbon-black and binder additives that enhance effective conductivity and provide mechanical integrity. However, these both reduce effective ionic transport in the electrolyte phase and reduce energy density. Therefore, an optimal additive loading is required to maximize performance, especially for fast charging where ionic transport is essential. Such optimization analysis is however challenging due to the nanoscale imaging limitations that prevent characterizing this additive phase and thus quantifying its impact on performance. Herein, an additive-phase generation algorithm has been developed to remedy this limitation and identify percolation threshold used to define a minimal additive loading. Improved ionic transport coefficients from reducing additive loading has been then quantified through homogenization calculation, macroscale model fitting, and experimental symmetric cell measurement, with good agreement between the methods. Rate capability test demonstrates capacity improvement at fast charge at the beginning of life, from 37% to 55%, respectively for high and low additive loading during 6C CC charging, in agreement with macroscale model, and attributed to a combination of lower cathode impedance, reduced electrode tortuosity and cathode thickness.

carbon-binder additives↗
DOE OSTI2022

Produced Water and Waste Heat-aided Blowdown Water Treatment: Using Chemical and Energy Synergisms for Value Creation

The project objective was to develop a cooling blowdown water (BDW) treatment process utilizing produced water (PW) and low-grade heat to maximize water reuse and saleable by-product generation while reducing chemical and energy footprints of the treatment. The proposed treatment process consists of mixing, softening, organics and suspended solids removal, reverse osmosis (RO), thermal desalination, and brine electrolysis. BDW samples collected from a local coal-fired power plant and PW samples from two shale gas production wells were used in this study. Each treatment unit was first designed and tested to quantify its treatment efficiency, and its chemical and energy requirements. In addition, a process model was developed and model simulations were conducted based on the experimental results and literature data to optimize the treatment process. A techno-economic analysis was conducted to quantify chemical and energy savings as well as production of 10-lb brine as a saleable product. With the field-collected BDW and PW samples, mixing experiments determined a volumetric mixing ratio 10:1 (BDW:PW) resulted in the best performance of multivalent ions removal and largest chemical savings for softening. Softening of the BDW/PW mixtures using alkaline chemicals (Na 2 CO 3 and NaOH) achieved 95%-100% removal of scaling-forming cations (Ca, Mg, Fe, Ba, Sr) and 60% of silicon, and 10% of total organic carbon (TOC). The mixing and softening treatments yielded an effluent with total dissolved solids (TDS) concentration of 23 g/L. Activated carbon (AC) filtration removed TOC to a low level (< 3 mg/L) and further removed remaining scale-forming divalent metals and silica from the softened water. The AC filtration resulted in a slight reduction of TDS from 23 g/L to 20 g/L, leaving behind only mostly monovalent ions (i.e., sodium and chloride) in the filtered water. These pretreatments yielded a feed water that met the criteria of the downstream reverse osmosis (RO) to prevent membrane fouling. A cross-flow RO system was used to further concentrate the TDS of the AC effluent. Various factors including TDS, pH, and applied pressure were examined and optimal conditions were determined for the co-treatment process. An integrated process consisting of mixing, softening, AC filtration and RO was used to treat a continuous flow (0.25 – 1.2 L/min, or 0.07 – 0.32 gpm) and successfully generated RO permeate as product water (TDS < 0.5 g/L) for reuse in cooling operation, and a concentrate (TDS ~ 45 g/L) to be further treated in a thermal desalination unit. These flow rates meet the FOA’s criterion of 0.01 – 1 gpm. Overall, the co-treatment of BDW/PW allowed shorter ramp-up time compared to treatment of BDW alone. It resulted in 40% and 55% savings of Na 2 CO 3(s) and NaOH, respectively, compared to treating the BDW and PW individually for the same level of softening. The co-treatment also resulted in a 29% energy saving compared to treatment of BDW only for the level of TDS concentration. A thermal desalination system was designed using CFD simulations and manufactured in the WVU Innovation Hub for further treatment of the RO concentrate to generate 10-lb brine. The system has a design flow rate of 2 gpm and has been successfully tested. A bench-scale brine electrolysis system was developed for on-site generation of chlorine/hypochlorite (Cl 2 /OCl - ) and caustic soda (NaOH) as useful chemicals for the co-treatment process. Using salt solutions (0.5 M and 1 M), the system achieved faradaic efficiencies of 93%-97% and 70%-77% for caustic soda and chlorine/hypochlorite generation, respectively. An economic analysis showed that the electricity costs for on-site generation of these chemicals were significantly lower than the chemical prices offered by suppliers. An industrial-scale process model consisting of mixing, softening, AC filtration, RO, thermal desalination, and brine electrolysis was developed using the Aspen Plus V9 in conjunction with Aspen Custom Modeler V9. The model serves as a solvable Aspen Plus model and as basis to form the costing infrastructure. In addition, techno-economic analysis considering capital, operating, and transportation costs was conducted. An optimization solution showed that produced water for mixing is still advantageous in low quantities. The optimum solution approaches a leveled cost of water (LCW) of 2 $/m 3 which becomes cost competitive with nominal water treatment prices.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Metabolic Engineering of Nonmodel Yeast Issatchenkia orientalis SD108 for 5–Aminolevulinic Acid Production

Biological production of 5-aminolevulinic acid (5-ALA) has received growing attention over the years. However, there is the tradeoff between 5-ALA biosynthesis and cell growth because the fermentation broth will become acidic due to the production of 5-ALA. To address this limitation, we engineered an acid-tolerant yeast, Issatchenkia orientalis SD108, for 5-ALA production. We first discovered that the cell growth rate of I. orientalis SD108 was boosted by 5-ALA and its endogenous ALA synthetase (ALAS) showed higher activity than those homologs from other yeasts. The titer of 5-ALA was improved from 28 mg/L to 120-, 150-, and 300 mg/L, by optimizing plasmid design, overexpressing a transporter, and increasing gene copy number, respectively. After redirecting the metabolic flux using the pyruvate decarboxylase (PDC) knockout strain (SD108ΔPDC) and culturing with urea, we increased the titer of 5-ALA to 510 mg/L, a 13-fold enhancement, proving the importance of the newly identified IoALAS with higher activity and the strategic selection of nitrogen sources for knockout strains. This study demonstrates the acid-tolerant I. orientalis SD108ΔPDC has a high potential for 5-ALA production at a large scale in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Ultrafast Oxygen Conduction in Sillén Oxychlorides

Oxygen ion conductors are crucial for enhancing the efficiency of various clean energy technologies, including fuel cells, solid oxide air batteries, electrolyzers, membranes, sensors, and more. In this study, a structure-similarity analysis of ≈62k oxygen-containing compounds identified the MBi 2 O 4 X (M = rare-earth element, X = halogen element) family as promising candidates for fast oxygen transport. Among these, LaBi 2 O 4 Cl is found as an ultrafast oxygen conductor with an ultralow migration barrier of 0.1 eV based on ab initio studies. Its 2D layered structure, featuring a “triple fluorite” layer, supports diffusion of both oxygen vacancies and interstitials. In addition to vacancy diffusion with a 0.1 eV barrier, ab initio studies show interstitial diffusion exhibits a modest barrier of 0.6–0.8 eV. Frenkel pairs are found to be the dominant defects in intrinsic LaBi 2 O 4 Cl, facilitating significant vacancy-mediated oxygen diffusion at elevated temperatures. With 2.8% oxygen vacancies, LaBi 2 O 4 Cl is predicted to achieve a conductivity of 0.3 S/cm at 25 °C in a single crystal. Experimental synthesis and characterization of polycrystalline LaBi 2 O 4 Cl and Sr-doped LaBi 2 O 4 Cl revealed conductivity exceeding that of YSZ and LSGM below 400 °C, with lower activation energies, achieving a total conductivity of 0.1−0.2 mS/cm at 300 °C. Here, while these results confirm its potential of fast oxygen transport, we suggest further experimental optimization of LaBi 2 O 4 Cl, including aliovalent doping and microstructure refinement, could significantly enhance its performance, facilitating fast oxygen conduction approaching room temperature.

Defects↗
DOE OSTI2020

Method for Comparing Porous Carbon Electrode Performance in Redox Flow Batteries

In this paper, we present a method for comparing porous electrodes for use in redox flow batteries. By comparing physical and electrochemical properties of six carbon cloth electrodes (CCEs) including hydraulic permeability, Ohmic resistance, and mass transport overpotential, we show how optimal engineering conditions such as compression and electrolyte flow rates can be derived. These results are combined and applied to a quinone-based redox flow battery, leading to a peak power density around 0.4 W cm –2 for several different CCEs. This method can be applied broadly to other electrode architectures and flow battery chemistries, and provides a comprehensive framework for their comparison.

25 ENERGY STORAGE↗
DOE OSTI2025

Three-dimensional reconstruction of THz near-fields from a LiNbO3 optical rectification source

Terahertz (THz) generation by optical rectification in LiNbO3 (LN) is a widely used technique for generating intense THz radiation. The spatiotemporal characterization of THz pulses from these sources is currently limited to far-field methods. While simulations of tilted pulse front THz generation have been published, little work has been done to measure the near-field properties of the THz source. A better understanding of the THz near-field properties will improve optimization of THz generation efficiency, transport, and coupling. We demonstrate a technique for quantitative spatiotemporal characterization of single-cycle strong-field THz pulses with 2-D near-field electro-optic imaging. We have reconstructed the full temporal 3D THz near-field and shown how the phase front can be tailored by controlling the incident pump pulse.

Gabriel, Annika↗
DOE OSTI2021

A two-dimensional analytical unit cell model for redox flow battery evaluation and optimization

Cell performance optimization is important for improving the overall system efficiency of a redox flow battery. To gain better insights into key controlling factors of system efficiency, this work first proposed a theoretical model for a unit flow battery cell by extending a two-dimensional analytic model to a full battery cell. Such a model is then used for cell performance optimization after validating it with experimental and numerical modeling data. With the model results, the activation, equilibrium, and pump energy losses are identified as the dominant sources of battery energy losses. Further, a guideline for reducing these sources is also proposed. Following the guideline, the mass transport coefficient is shown as a key control factor of the equilibrium energy loss and Coulombic efficiency (CE). Approaches are then proposed to improve CE and the overall system efficiency. The mass transport is also revealed as the mechanism of pump rate optimization where an optimal pump rate significantly reduces the equilibrium energy loss. With both low equilibrium and pump energy losses, an optimal electrode porosity or specific area design can further improve a battery's system efficiency based on an optimal porosity predicted by the present model. The model also demonstrates distinct behaviors and overestimation in the system efficiency when reduced to a zero-dimensional model with neglected mass transport resistance. With the new model, the guideline, and new insights, this work provides a reliable and efficient tool for the evaluation and optimization of redox flow battery design in practical applications.

25 ENERGY STORAGE↗