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At least 487 records · Page 27

Ultrahigh power and energy density in partially ordered lithium-ion cathode materials

There is an intensive search for high-performance cathode materials for rechargeable batteries. In this work the authors report that oxyfluorides with partial spinel-like cation order, made from earth-abundant elements, display both exceptionally high energy and power. The rapid market growth of rechargeable batteries requires electrode materials that combine high power and energy and are made from earth-abundant elements. In this paper we show that combining a partial spinel-like cation order and substantial lithium excess enables both dense and fast energy storage. Cation overstoichiometry and the resulting partial order is used to eliminate the phase transitions typical of ordered spinels and enable a larger practical capacity, while lithium excess is synergistically used with fluorine substitution to create a high lithium mobility. With this strategy, we achieved specific energies greater than 1,100 Wh kg -1 and discharge rates up to 20 A g -1 . Remarkably, the cathode materials thus obtained from inexpensive manganese present a rare case wherein an excellent rate capability coexists with a reversible oxygen redox activity. Our work shows the potential for designing cathode materials in the vast space between fully ordered and disordered compounds.

25 ENERGY STORAGE↗

Soluble and stable symmetric tetrazines as anolytes in redox flow batteries

We report nonaqueous redox flow batteries are a promising technology for grid-scale energy storage, however, their commercial success relies on identifying redox-active materials that exhibit extreme potentials, high solubilities in all states of charge, and long cycling stabilities. Meeting these requirements has been particularly challenging for molecules capable of storing negative charge. Within this context, tetrazines remain unexplored despite their unique structural properties that enable them to meet some of these challenges. Herein, we prepared symmetric s-tetrazines substituted with methyl, methoxy, and thiomethyl substituents and evaluated their electrochemical properties, solubility, and cycling stability. These studies revealed that highly soluble 3,6-dimethoxy-s-tetrazine undergoes a reversible one-electron reduction to generate a stable (t 1/2 > 1240 h) radical anion. When implemented in a lab-scale symmetric flow cell (0.125 M), it exhibited a relatively slow capacity fade of 8% over 50 cycles (17 h). Given their high solubility and cycling stability, we believe that s-tetrazine derivatives should be further explored for nonaqueous redox flow batteries.

36 MATERIALS SCIENCE↗

Carbazole-substituted benzobisoxazoles: near-UV fluorescent emitters and ambipolar hosts for organic light-emitting diodes

The design of organic light emitting diode (OLED) materials with the potential for exhibiting thermally-activated delayed fluorescence (TADF) is reported. Using computational methods (DFT/TD-DFT) as a guiding tool, six materials with a benzobisoxazole (BBO) core and donor–acceptor–donor architectures were designed by changing the conjugation position of carbazole-substituted phenyl substituents and the type of BBO isomer (cis- vs. trans-). Experimental steady-state and transient absorption spectroscopic techniques were utilized to probe the TADF activity of these molecules. Each material was then used in host–guest OLED devices as either near-UV dopants or host with low singlet-triplet energy differences. The electroluminescent properties show that when used as dopants these materials provide near-UV emission (CIE y < 0.06 and CIE x = 0.16), whereas when used as hosts, these materials show reduced operating voltages and increased performance efficiencies when compared to commercial materials.

36 MATERIALS SCIENCE↗

Continuous-flow reactor with superior production rate and stability for CO 2 reduction using semiconductor photocatalysts

Semiconductor photocatalyst approaches for solar CO 2 reduction are attractive due to their simplicity but have lagged in efficiency compared to less-integrated photoelectrochemical (PEC) approaches and to electrolysis reactors. We identify poor mass transport and catalyst deactivation as key constraints. To address them, we have developed a continuous-flow photocatalytic reactor system allowing us to control the triple-phase interface on the photocatalyst surface using the liquid and reactant gas flow rates. With the goal of selectively producing CO, the reactor is optimized by controlling the pressure and flow rates of the reactant gas and electrolyte in contact with both sides with the intermediately placed catalyst. In comparison to batch reactors with an immobile photocatalyst bed and gas phase CO 2 or CO 2 purged water, 10–24 times higher production rates are achieved for photocatalysts such as TiO 2 , ZnO, C 3 N 4 , and CdS by simply changing to the designed flow-type photoreactor without any catalyst modification. In addition, CO selectivity (93.2%) and long-term stability (>780 min) using the designed reactor are significantly enhanced compared to using the batch reactors (71.7%, <180 min for reduced 50% activity). Here, we propose that the enhanced mass transport on the photocatalyst surface accelerates the desorption of the initial photolysis product, CO, and prevents the poisoning effect from deactivating photocatalyst activity. This study has the potential to facilitate the utilization of semiconductor-based photocatalytic reactions for achieving superior performance wih gaseous reactants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Overcoming sparse datasets with multi-task learning as applied to high entropy alloys

Abstract The design of novel High Entropy Alloys for use in high-temperature applications is an area of active interest due to their potential to provide exceptional properties compared to conventional alloys. Since the increased popularity of machine learning, an important cog in the design process has been training surrogate models on alloy properties. However, these Single-Task models are trained on individual mechanical properties and do not take advantage of the relatedness between properties. Multi-Task models can capture the interdependencies between tasks, leading to potentially more accurate predictions for all tasks. In this paper, we investigate if Multi-Task models can show improvement over Single-Task models when used for predicting the mechanical properties of these alloys. To ensure fair evaluation between the models, we apply L 0 regularization and skip connections to the models, which allows them to adjust the number of model parameters and depth for optimal performance. We find that the Multi-Task models can leverage task relationships to perform better than Single-Task models, especially for high amounts of missing data in the tasks. Furthermore, adding simple auxiliary targets can boost Multi-Task performance even further despite not being effective as input descriptors to single-task models themselves. We anticipate that the proposed strategies can achieve more accurate predictions and consequently enable better design capabilities for such data-constrained domains without incurring much additional computational cost.

Debnath, Arindam (ORCID:0000000194274499)↗

Anthropogenic uranium signatures in turtles, tortoises, and sea turtles from nuclear sites

Chelonians (turtles, tortoises, and sea turtles) grow scute keratin in sequential layers over time. Once formed, scute keratin acts as an inert reservoir of environmental information. For chelonians inhabiting areas with legacy or modern nuclear activities, their scute has the potential to act as a time-stamped record of radionuclide contamination in the environment. Here, we measure bulk (i.e., homogenized scute) and sequential samples of chelonian scute from the Republic of the Marshall Islands and throughout the United States of America, including at the Barry M. Goldwater Air Force Range, southwestern Utah, the Savannah River Site, and the Oak Ridge Reservation. We identify legacy uranium ( 235 U and 236 U) contamination in bulk and sequential chelonian scute that matches known nuclear histories at these locations during the 20th century. Our results confirm that chelonians bioaccumulate uranium radionuclides and do so sequentially over time. This technique provides both a time series approach for reconstructing nuclear histories from significant past and present contexts throughout the world and the ability to use chelonians for long-term environmental monitoring programs (e.g., sea turtles at Enewetok and Bikini Atolls in the Republic of the Marshall Islands and in Japan near the Fukushima Daiichi reactors).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Effect of Quartz Aperture Covers on the Fluid Dynamics and Thermal Efficiency of Falling Particle Receivers

Falling particle receivers are an emerging technology for use in concentrating solar power systems. In this paper, quartz half-shells are investigated for use as full or partial aperture covers to reduce receiver thermal losses. Quartz half-shell aperture covers offer the ability to minimally interfere with incoming solar radiation from the heliostat field while obstructing thermal radiation and advection from leaving the receiver cavity. The fluid dynamics and heat transfer of a receiver subdomain and surrounding air are modeled using ANSYS®FLUENT We compare the percentage of total incident solar power lost due to conduction through the receiver walls, advective losses through the aperture, and radiation exiting the aperture. Contrary to expected outcomes, results show that quartz aperture covers can increase radiative losses and result in modest to nonexistent reductions in advective losses. The increased radiative losses are driven by elevated quartz half-shell temperatures and have the potential to be mitigated by active cooling and/or material selection. Quartz half-shell total transmissivity was measured experimentally using a radiometer and the National Solar Thermal Test Facility heliostat field with values up to 0.97 ± 0.01. Quartz half-shell aperture covers did not yield expected efficiency gains in numerical results due to increased radiative losses, but efficiency improvement in some numerical results and the performance of quartz half-shells subject to concentrated solar radiation suggest that quartz half-shell aperture covers should be investigated further.

14 SOLAR ENERGY↗

Platinum Surface Oxide and Oxygen Reduction Reaction Kinetics during Transient Fuel Cell Operation

Pt surface oxide coverage (POC) and Oxygen Reduction Reaction (ORR) were measured under a range of operating conditions in a Proton Exchange Membrane Fuel Cell (PEMFC). Sensitivity analysis suggests that adsorption of sulfonate groups of the ionomer on the Pt surface plays an important role in POC and ORR kinetics. Although both Pt oxide growth and ORR activity decay follow a logarithm of time behavior, it is found that ORR kinetics are affected by at least two different types of Pt oxide. A semi-empirical ORR kinetic model is proposed taking into account the effect of ionomer and Pt oxide types. The model is capable of providing a quantitative prediction of POC and ORR activity over a range of potential, temperature, relative humidity, and time that is relevant to normal PEMFC operation.

Electrochemistry↗

Mutations that confer resistance to broadly-neutralizing antibodies define HIV-1 variants of transmitting mothers from that of non-transmitting mothers

Despite considerable reduction of mother-to-child transmission (MTCT) of HIV through use of maternal and infant antiretroviral therapy (ART), over 150,000 infants continue to become infected with HIV annually, falling far short of the World Health Organization goal of reaching <20,000 annual pediatric HIV cases worldwide by 2020. Prior to the widespread use of ART in the setting of pregnancy, over half of infants born to HIV-infected mothers were protected against HIV acquisition. Yet, the role of maternal immune factors in this protection against vertical transmission is still unclear, hampering the development of synergistic strategies to further reduce MTCT. It has been established that infant transmitted/founder (T/F) viruses are often resistant to maternal plasma, yet it is unknown if the neutralization resistance profile of circulating viruses predicts the maternal risk of transmission to her infant. In this study, we amplified HIV-1 envelope genes ( env ) by single genome amplification and produced representative Env variants from plasma of 19 non-transmitting mothers from the U.S. Women Infant Transmission Study (WITS), enrolled in the pre-ART era. Maternal HIV Env variants from non-transmitting mothers had similar sensitivity to autologous plasma as observed for non-transmitting variants from transmitting mothers. In contrast, infant variants were on average 30% less sensitive to paired plasma neutralization compared to non-transmitted maternal variants from both transmitting and non-transmitting mothers (p = 0.015). Importantly, a signature sequence analysis revealed that motifs enriched in env sequences from transmitting mothers were associated with broadly neutralizing antibody (bnAb) resistance. Altogether, our findings suggest that circulating maternal virus resistance to bnAb-mediated neutralization, but not autologous plasma neutralization, near the time of delivery, predicts increased MTCT risk. These results caution that enhancement of maternal plasma neutralization through passive or active vaccination during pregnancy may potentially drive the evolution of variants fit for vertical transmission.

59 BASIC BIOLOGICAL SCIENCES↗

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗

Simulated tree recruitment at PA-BCI under four precipitation scenarios

Simulated recruitment rates are provided for four tropical tree plant functional types (PFTs) which vary in drought and shade tolerance, at Barro Colorado Island, Panama (PA-BCI). Recruitment rates are predicted using the Tree Recruitment Scheme (TRS; Hanbury-Brown et al., 2022) and model data output from the Ecosystem Demography model version 2 with hydrodynamics (ED2; Medvigy et al., 2009, Powell et al., 2018). Predictions of recruitment are provided under four precipitation scenarios: “BASE” = recycled 2008-2014 observed meteorology (Faybishenko & Paton, 2021), “SYN-ENSO” = two exceptionally strong El Niño events within 30 years (Powell et al., 2018), “WET” = 30% increase in precipitation compared to BASE, and DRY-DS = dry season (January–April) precipitation reduced by 75% compared to BASE. In addition to recruitment rates, this dataset provides TRS predictions of PFT-specific seed bank and seedling pool dynamics (tracked in units of carbon) and ED predictions of understory photosynthetically active radiation and soil matric potential at 6 cm soil depth.

54 ENVIRONMENTAL SCIENCES↗

Determining Unabated Airborne Radionuclide Emissions Monitoring Requirements Using Inventory-Based Methods

Compliance with the airborne radionuclide emission monitoring requirements in the National Emission Standards for Hazardous Air Pollutants (NESHAP; 40 CFR Part 61, Subpart H) and State requirements in Washington Administrative Code 246–247 and 173-480 were evaluated for Pacific Northwest National Laboratory (PNNL) operations. Additional guidance may be found in the U.S. Department of Energy Handbook, Environmental Radiological Effluent Monitoring and Environmental Surveillance. To meet regulatory requirements, reviews of planned or proposed projects and activities provide the basis for implementing necessary monitoring adjustments or for implementing changes to projects and activities in a timely manner. Potential unabated off-site doses were evaluated for emission locations managed by PNNL and licensed to the Department of Energy. These locations were at facilities in Richland, Washington (i.e., the Hanford Site and PNNL-Richland Campus) and in Sequim, Washington, (PNNL-Sequim Campus, which formerly was known as the Marine Sciences Laboratory). This report describes the inventory-based methods and provides the results for the NESHAP assessment performed in January 2021 for calendar year ending 2020.

40 CFR 61 Subpart H↗

A review of privacy in energy applications

As the distribution system continues to experience an increase in distributed energy resource (DER) and electric vehicle (EV) penetration, so does the need for new solutions that can help grid operators manage and leverage their capabilities. This will undoubtedly lead to new operational schemes and business opportunities that will transform the traditional consumer into a prosumer who will be more actively engaged in grid operations. Although the field is still under active development, many of the potential use cases presented in literature or industry are built upon edge computing, two-way communications, and other innovative computational constructs to attain their goals. However, at their core, many use cases assume a great level of data access to aid with the decision-making process, an assumption that may need to be revised to ensure fair and equitable operational processes are maintained. This may be particularly true as edge resources are predicted to participate in retail-side, many-to-many, or peer-to-peer markets and thus may lead to financial impacts if data access considerations are ignored. The need to revise data access mechanisms can be further justified by the introduction of new participants into the operational process, who do not have the same level of trust, nor the incentives to focus on energy delivery as their primary objective. At the same time, more consumers are becoming aware of their own data, and the potential impacts of its abuse. To help solution developers better understand these risks, this report has been developed to offer an initial introduction to the topic of privacy. This is achieved by 1) Highlighting the need for privacy-aware solutions; 2) Encouraging system designers to be inquisitive about the status quo; 3) Documenting the existing threat space; 4) Presenting and evaluating tools that may be helpful towards enabling better privacy postures; and 5) Making recommendations to encourage the adoption of better practices. From a technical perspective, the report focuses on evaluating two potential techniques by applying them to the Transactive Energy Space. Based on the obtained results, it can be established that differential privacy (DP) methods may have limited applicability when highly correlated, time-series data records need to be protected. However, DP may be a powerful tool when it is used to aggregate and analyze mid-size and large-size data sets in a more traditional statistical environment. The second tool under evaluation is threshold cryptography, which can guarantee complete secrecy (and thus privacy) but requires the establishment of key management procedures and dedicated communication channels for key coordination. Therefore, due to its increased computational overhead, the use of threshold cryptography must be weighted using a cost/benefit analysis on a per-application basis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Homomorphic Encryption for Electrical Metering Aggregation: Protecting the Privacy of Building Tenants

Electrical meters are devices that measure consumer electricity usage. The data collected by these meters is necessary for utility billing and electrical grid management but can also be used to assess the environmental impact of buildings. Prior research has found that unprotected metering data could potentially be used to infer some information about the behaviors of building tenants by detecting changes in electricity usage. For example, a period of low electricity usage could suggest that the tenants are not in the building. As smart metering becomes more common, there is a growing need for data privacy protections for metering data that do not negatively impact the quality and availability of data used for energy management and billing applications. To identify potential solutions, we developed a Python-based data aggregation platform to analyze the potential efficacy of privacy-enhancing technologies for energy metering applications. This platform aggregates groups of metering sites into virtual buildings, which could potentially detach changes in electrical activity from individual tenants, making it more difficult to track the activity of a specific tenant. To further protect data during analysis, this project utilizes homomorphic encryption as part of its initial approach. Homomorphic encryption offers a means of protecting energy consumption data while permitting mathematical operations to be performed without the need to know the data contents. This allows for data to be processed into usable statistics without revealing energy consumption information. A series of homomorphic encryption libraries were evaluated to determine their applicability and limitations in the context of metering data. The use of these techniques may help to reassure consumers and encourage further adoption of smart grid infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Determining Unabated Airborne Radionuclide Emissions Monitoring Requirements Using Inventory-Based Methods

Compliance with the airborne radionuclide emission monitoring requirements in the National Emission Standards for Hazardous Air Pollutants (NESHAP; Title 40 of the U.S. Code of Federal Regulations Part 61, Subpart H) and State requirements in Washington Administrative Code 246–247: Radiation Protection – Air Emissions and 173-480: Ambient Air Quality Standards and Emission Limits for Radionuclides were evaluated for Pacific Northwest National Laboratory (PNNL) operations. Additional guidance may be found in the U.S. Department of Energy Handbook, Environmental Radiological Effluent Monitoring and Environmental Surveillance. To meet regulatory requirements, reviews of planned or proposed projects and activities provide the basis for implementing necessary monitoring adjustments or for implementing changes to projects and activities in a timely manner. Potential unabated off-site doses were evaluated for emission locations managed by PNNL and licensed to the Department of Energy. These locations were at facilities in Richland, Washington (i.e., the Hanford Site and PNNL–Richland campus) and in Sequim, Washington, (PNNL–Sequim campus). This report describes the inventory-based methods and provides the results for the NESHAP assessment performed in January 2024 for calendar year ending 2023.

40 CFR 61 Subpart H↗

Managing Subsurface Pressure Buildup and Interference in Commercial-Scale CO 2 Storage Project with Proximal Injection Wells

Large-scale decarbonization using carbon capture and storage (CCS) is likely to involve many commercial-scale CO 2 storage projects located in close proximity to each other. This close proximity raises concerns over pressure interference among the storage projects. Pressure interference between injection and storage efforts can reduce the practicable CO 2 storage resource and force wells to inject CO 2 at a lower rate to avoid the fracture pressure thresholds per United States Environmental Protection Agency (EPA) Class VI well regulations to preserve injection and confining zone integrity and potentially mitigate against inducing seismic activity. These analyses employ numerical full-physics reservoir modeling to evaluate how pressure buildup fronts and CO 2 plumes evolve under commercial-scale injection volumes of CO 2 in which multiple storage sites located in close proximity occur in tandem. The simulation models mimic injection at pseudo basin-scale and assume homogeneous saline formation(s) as storage targets with a pair of upper and lower homogeneous seal layer/s. These analyses specifically investigate the efficacy of two basin-wide reservoir pressure management strategies in addressing the technical challenges associated with pressure buildup and CO 2 plume commingling. The strategies explored include: 1) enlarging the area of injection well spacing (WS) and 2) storing CO 2 in a stacked sequence (SSS) of saline formations compared to a single formation. The storage and confining zones properties assumed were held common across the scenarios, unless specified otherwise. Analyses results show that after injecting 4 million tons per year for 30 years using 4 separate wells (each injecting 1 million metric tons per year), the radius of CO 2 plume extends to a mere 3 km or less from injection wells. Meanwhile, the radius of pressure buildup ranges on the order of tens to a few hundreds of kilometers, depending on the magnitude of pressure buildup threshold that one would use to define the front. CO 2 plume commingling from different injection wells appears to occur 50 years post-injection, especially under scenarios with narrowly spaced (i.e., < 5 km apart) injection well locations. Findings from sensitivity cases on the well spacing suggest that storage formations modeled would require different well spacing to avoid fracture pressure thresholds. For instance, modeled storage formations with high fracture gradients (i.e., 0.8 psi/ft) would need less than 5–km well spacing, whereas those with lower fracture gradients (i.e., 0.7 psi/ft) would need approximately 20–km well spacing, based on assumed modeling parameters. Under stacked injection, the pressure challenges (described above) still exist but are more alleviated due to distributing the same injection volume across more available reservoir volume. These analyses demonstrate that stacked-sequence storage can effectively address the challenges, while still providing the same target CO 2 storage volumes and allowing a large number of storage projects to be deployed in the same basin by better utilizing the available storage resource across different reservoir depths. Among cases modeled, the resulting pressure buildup front is most suppressed when each storage project distributes injection volumes over several wells, each of which injects a portion of the total CO 2 across the stacked sequence. This strategy results in the smallest CO 2 aerial footprint amongst scenarios evaluated but also shows the largest reduction in the pressure buildup at the top of perforation at the injection wells (upwards of approximately 42 percent compared to the commercial-scale single-formation storage), the result of which is crucial to maintain caprock integrity. The findings presented by this research draw attention to the importance of greater coordination among storage operators and regulatory stakeholders to foster the upscaling and deployment of CCS. These analyses provide insights into required decision-making when considering multi-project deployment in a shared basin. Because these analyses evaluate a very specific geologic situation, they bear further investigations across other geologic situations.

42 ENGINEERING↗

Scalable Solar Fuels Production in A Reactor Train System by Thermochemical Redox Cycling of Novel Nonstoichiometric Perovskites

Hydrogen production via two-step thermochemical water splitting redox cycles using nonstoichiometric redox-active metal oxides has the potential to dramatically increase fuel production rates. At moderate-to-low water splitting temperatures, surface reaction kinetics co-limit the process. In such cases, stable and high surface area microstructures that allow exploitation of the full thermodynamic potential of the materials are essential as is tight thermal integration of the reactor module. This project’s goals were the development of novel nonstoichiometric perovskite oxides with high stability and favorable thermodynamic and kinetic properties, to optimize their microstructure for maximizing the fuel productivity, and to build a prototype reactor train system (RTS) comprising at least one reactor to meet specific performance targets: (1) capable of an in-house solar thermochemical hydrogen (STCH) productivity ≥ 12 mL g -1 for stable continuous operation ≥ 20 cycles; and (2) demonstration of scalable solar fuels production at practical solar reactor level in an industrial-scale concentrated solar tower (CST) using developed perovskites to achieve a hydrogen production rate ≥ 1 g h -1 .

08 HYDROGEN↗

Computationally Accelerated Discovery and Experimental Demonstration of Gd0.5La0.5Co0.5Fe0.5O3 for Solar Thermochemical Hydrogen Production

Solar thermochemical hydrogen (STCH) production is a promising method to generate carbon neutral fuels by splitting water utilizing metal oxide materials and concentrated solar energy. The discovery of materials with enhanced water-splitting performance is critical for STCH to play a major role in the emerging renewable energy portfolio. While perovskite materials have been the focus of many recent efforts, materials screening can be time consuming due to the myriad chemical compositions possible. This can be greatly accelerated through computationally screening materials parameters including oxygen vacancy formation energy, phase stability, and electron effective mass. In this work, the perovskite Gd 0.5 La 0.5 Co 0.5 Fe 0.5 O 3 (GLCF), was computationally determined to be a potential water splitter, and its activity was experimentally demonstrated. During water splitting tests with a thermal reduction temperature of 1,350°C, hydrogen yields of 101 μmol/g and 141 μmol/g were obtained at re-oxidation temperatures of 850 and 1,000°C, respectively, with increasing production observed during subsequent cycles. This is a significant improvement from similar compounds studied before (La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 and LaFe 0.75 Co 0.25 O 3 ) that suffer from performance degradation with subsequent cycles. Confirmed with high temperature x-ray diffraction (HT-XRD) patterns under inert and oxidizing atmosphere, the GLCF mainly maintained its phase while some decomposition to Gd 2-x La x O 3 was observed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗