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At least 487 records · Page 27

Spent Nuclear Fuel and Reprocessing Waste Inventory: Spent Fuel and Waste Disposition

This report provides information on the inventory of spent nuclear fuel (SNF) in the United States located at Nuclear Power Reactor (NPR) and Independent Spent Fuel Storage Installation (ISFSI) sites, as well as SNF and reprocessing waste located at U.S. Department of Energy (DOE) sites and other research and development (R&D) centers as of the end of calendar year 2021. Actual or estimated quantitative values for current inventories are provided along with inventory forecasts derived from examining different future nuclear power generation scenarios, based on information available and assumptions made at the time the scenarios were developed in the spring of 2022. The report also includes select information on the characteristics associated with the wastes examined (e.g., type, packaging, heat generation rate, decay curves).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Carbon Storage Site Mapping Inquiry Tool (MapIT)

To date, 48 projects, consisting of 139 wells, are currently under review with the Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) Program for Class VI – wells used for geologic sequestration of carbon dioxide. The number of applications submitted is expected to increase in coming years with the increase of the 45Q tax credit available to projects that initiate construction prior to 2033. The amount of data collected to submit a Class VI permit is vast, and often disparate, coming from state, federal, and commercial entities, as well as field-specific data collected within an area of interest. When preparing for site selection and permitting, the initial aggregation of relevant public data can be time intensive. The Carbon Storage Site Mapping Inquiry tool (MapIT) was created to support and accelerate the discovery and accessibility of open-source data and information available across the USA. Data was aggregated and organized based on data types described within the EPA UIC Class VI permit documentation. The online tool enables users to explore hundreds of geospatial data layers and connect to additional external resources, leveraging API and REST services where possible to ensure updates to data in real time. MapIT enables users to explore state and federal data related to geologic, geophysical, structural, hydrologic, and contextual information. In addition to displaying spatial data and linking to external resources, MapIT leverages custom widgets to ensure that internal data and external data are discoverable and accessible. The widgets connect users to resources such as the USGS publications and the USGS Earthquake Catalog based on a user-defined location. This talk will describe data aggregation workflows, data types, data preparation, and tool development for MapIT. The Carbon Storage Site Mapping Inquiry Tool and underlying database are valuable, intuitive resources that empower government, academic, commercial and industry stakeholders to explore, analyze, and acquire carbon storage related data.

Morkner, Paige↗

Nuclear Energy in Long-Term System Models: A Multi-Model Perspective

Long-term energy system models–including electric sector capacity expansion models–are widely used tools for informing planning, technology assessment, and policy analysis. Recent decarbonization goals and rapid technological change have increased the need to appropriately represent economic characteristics and technical details of energy system resources, including variable renewable energy, energy storage technologies, carbon-capture-equipped capacity, and nuclear energy. Nuclear power represents about 20% of electricity generation and 50% of carbon-free electricity in the United States as of 2021. However, there are many perspectives on the role of existing and new nuclear in the future U.S. energy system, which is reflected in the broad range of potential contributions reported in the literature. This project aims to understand how issues central to nuclear energy are represented in long-term energy models. Building on earlier collaborations that focused on variable renewable energy and energy storage, this project convenes four modeling teams that use national-scale long-term energy system models from the Electric Power Research Institute, the National Renewable Energy Laboratory, the U.S. Energy Information Administration, and the U.S. Environmental Protection Agency to share methods and data, update models, run coordinated scenarios, and identify research needs. Improving tools can provide more insightful analyses and ensure that methods are more transparent.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Fission Product Gas Monitoring During Fuel Drying Operations - 20094

The goal of the project was to devise and implement a method of accurately qualifying any noble gas emitted during fuel drying operations (part of the dry fuel storage process) in order to provide the data needed to validate the off-site dose calculations. The imputes of the project was the NRC publishing Information Notice 18-01, 'Noble Fission Gas Releases During Spent Fuel Cask Loading Operations' in February 2018. The information notice describes some industry events that occurred during vacuum drying operations as part of a dry fuel campaign. The solution was devised by Fort Calhoun Station staff in partnership with Mirion Technologies technical staff. The monitoring was accomplished by directing all the effluent from the fuel storage cask vacuum drying machine through a sample chamber containing a compact ISOCS characterized CZT based gamma spectrometer connected to a Mirion Data Analyst. This resulted near real-time quantification of the radionuclides of concern. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

Modeling direct air carbon capture and storage in a 1.5 °C climate future using historical analogs

Limiting the rise in global temperature to 1.5 °C will rely, in part, on technologies to remove CO 2 from the atmosphere. However, many carbon dioxide removal (CDR) technologies are in the early stages of development, and there is limited data to inform predictions of their future adoption. Here, we present an approach to model adoption of early-stage technologies such as CDR and apply it to direct air carbon capture and storage (DACCS). Our approach combines empirical data on historical technology analogs and early adoption indicators to model a range of feasible growth pathways. We use these pathways as inputs to an integrated assessment model (the Global Change Analysis Model, GCAM) and evaluate their effects under an emissions policy to limit end-of-century temperature change to 1.5 °C. Adoption varies widely across analogs, which share different strategic similarities with DACCS. If DACCS growth mirrors high-growth analogs (e.g., solar photovoltaics), it can reach up to 4.9 GtCO 2 removal by midcentury, compared to as low as 0.2 GtCO 2 for low-growth analogs (e.g., natural gas pipelines). For these slower growing analogs, unabated fossil fuel generation in 2050 is reduced by 44% compared to high-growth analogs, with implications for energy investments and stranded assets. Residual emissions at the end of the century are also substantially lower (by up to 43% and 34% in transportation and industry) under lower DACCS scenarios. The large variation in growth rates observed for different analogs can also point to policy takeaways for enabling DACCS.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Wabash CarbonSAFE. Geologic Analysis of the Potosi Dolomite Reservoir Interval and Potential Confining Units, at the Wabash #1 Well, Vigo Co., Indiana, Subtask 7.2, Technical Report

The Wabash CarbonSAFE project drilled the Wabash #1 stratigraphic test well (ID# 168045) at the Wabash Valley Resources (WVR) IGCC facility in Vigo County, Indiana, to evaluate the feasibility of commercial-scale CO 2 storage near the site. The Mt. Simon Sandstone was the initial target for storage evaluation and was found to have generally poor reservoir qualities in the Wabash #1 well. Focus was then placed on the secondary target, the Potosi Dolomite. For storage and containment evaluation an extensive suite of geophysical logs was collected throughout the borehole, and whole cores were obtained from the Mt. Simon Sandstone, Eau Claire Formation, and Maquoketa Group. The Maquoketa Group is identified as a regionally extensive secondary sealing interval above the Mt. Simon Sandstone and as a primary seal for the Potosi Dolomite storage complex. Due to concerns over potential lost circulation zones in the Potosi Dolomite during drilling, no core samples or Formation Micro Imager (FMI) logs were acquired in the Potosi Dolomite. However, cased hole well testing was performed over a 20 feet (6 m) interval within the Potosi Dolomite during which a fluid swab sample was obtained. Approximately 35 miles of 2D seismic information was acquired in the project area near Wabash #1 to aid in reservoir and caprock characterization. The highly porous and permeable zones in the Potosi Dolomite and lower Oneota Dolomite observed within the Wabash #1 well are, for the purpose of this report, considered the same reservoir package and referred to as the Potosi reservoir interval. This report presents a regional depositional context for the Potosi reservoir interval, the overlying confining zones in the Knox Group, and Maquoketa Group primary seal. This context is developed through detailed information obtained from the Wabash #1 well such as lithologic data collected from cuttings and core, geophysical logging, geomechanical analysis of core samples, and well testing and fluid sampling within the Potosi reservoir interval. Information discussed in this report was compiled as a basis for the development of static and dynamic geologic models of the Potosi Dolomite storage complex. The static and dynamic modeling of CO 2 injection in the Potosi reservoir interval are discussed in a separate report (Dessenberger et al., 2022) under the Wabash CarbonSAFE project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Joint Optimization of Well Completions and Controls for CO 2 Enhanced Oil Recovery and Storage

CO 2 storage through CO 2 enhanced oil recovery (EOR) is considered as one of the technologies to help promote larger scale deployment of CO 2 storage because of associated economic benefits through oil recovery, 45Q tax credits and the utilization of existing infrastructure. The objective of this study is to demonstrate how optimal reservoir management and operation strategies (including well completions and controls) can be used to optimize both CO 2 storage and oil recovery. The optimization problem was focused on jointly estimating the well completions (i.e., fraction of injection/production well perforations in each reservoir layer) and CO 2 injection/oil production controls that maximize the net present value (NPV) in a CO 2 EOR and storage operation. We utilized the newly developed StoSAG algorithm, one of the most efficient optimization algorithms in the reservoir management community, to solve the optimization problem. The performance of joint optimization approach was compared with the performance of well control only optimization approach. In addition, the performance of co-optimization of CO 2 storage and oil recovery approach was compared with the performances of maximization of only CO 2 storage and maximization of only oil recovery approaches. The optimization results showed that a joint optimization of well completions and well controls can achieve an 8.84% higher final NPV than the one obtained from the optimization of only well controls. It was observed that the NPV incremental for joint optimization is mainly due to the fact that the optimal well completions and controls approach results in efficient CO 2 storage and oil production from different reservoir layers depending on the differences in individual layer properties. Comparison of co-optimization (i.e., maximization of NPV) and maximization of only CO 2 storage or only oil recovery showed that the co-optimization and maximization of only oil recovery result in significantly higher final NPV than that obtained through maximization of only CO 2 storage approach while maximization of only CO 2 storage can achieve significantly higher CO 2 storage in the reservoir compared to the other two scenarios. The similar results for co-optimization and maximization of oil production are obtained because of the difference in oil revenue compared to CO 2 storage tax credit. To the best of our knowledge, this is the first study in oil/gas industry and CO 2 storage community to perform joint optimization of well completions and well controls in the fields. We expect that the proposed optimization framework will be a useful and efficient tool for field engineers to optimally manage CO 2 EOR projects to maximize revenue through oil recovery as well as CO 2 storage by taking advantage of the new 45Q tax law.

Artificial intelligence↗

On the Feasibility of Market Manipulation and Energy Storage Arbitrage via Load-Altering Attacks

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

25 ENERGY STORAGE↗

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multi-threaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0x and 6.8x on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3x over the multi-thread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗

VDiSC: An Open Source Framework for Distributed Smart City Vision and Biometric Surveillance Networks

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose the Distributed Smart City framework for Vision, or VDiSC. VDiSC serves as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. VDiSC additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. VDiSC ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), VDiSC also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.

Brogan, Joel↗

wmpy-power: A Python package for process-based regional hydropower simulation

Hydropower is an important source of renewable energy in many parts of the world. The generation potential for a hydropower facility can vary greatly due to fluctuations in precipitation and snowmelt patterns impacting streamflow and reservoir storage. Human activities such as irrigation, manufacturing, and hydration can also influence water availability at nearby and downstream facilities. wmpy-power--the hydropower model described in this work--is process-based, leveraging explicit reservoir storage and release data to address impacts on hydropower from climate change and human adaptive behaviors to inform long-term planning and resource-adequacy considerations.

13 HYDRO ENERGY↗

A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 5 Report Field Scale Stress Modeling)

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686, for the project “A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage.” The purpose of this DOE funding is to develop non-invasive methods to obtain important information about subsurface stresses. The overall research goal of the project was to develop and improve methods for determining the spatial distribution of stresses, including magnitude and orientation of the three principal stress components in the subsurface, based on new methods that extract stress information from seismic data combined with well measurements, extended with well tests and logs, and unified in a numerical model that permits computation of the full stress tensor throughout the domains under investigation.

58 GEOSCIENCES↗

Best Practices for Resilience Hub Development and Management

The Carbon League and its community partners in East St. Louis, Illinois, have identified five facilities to serve as resilience hubs. These hubs are intended to support the local community through a range of services and resources during blue-sky (everyday), gray-sky (pre-event), and black-sky (emergency) conditions. Transforming these facilities into fully functional resilience hubs requires a broad operational improvement and programmatic planning roadmap. This memo outlines best practices to guide the development of these resilience hubs in East St. Louis, including recommendations for infrastructure services; safety and physical protection; community services; operational protocols; and a phased implementation strategy aligned with realistic funding and capacity constraints. Infrastructure recommendations include strengthening electric power, communications, water, sanitation, and transportation/logistics capabilities, all of which are essential for hubs that may serve as cooling and warming centers, distribution points, and information hubs during emergencies. Safety recommendations focus on accessibility, emergency action planning, indoor air quality, and secure storage of critical equipment. A phased roadmap provides guidance from immediate, low-cost readiness actions to long-term optimization and community integration. Performance metrics and maintenance protocols ensure continuous improvement and operational readiness. This guidance draws on best practices that can be used to support the development of resilient, community-centered hubs capable of enhancing public safety, health, and well-being during everyday operations and emergencies alike.

99 GENERAL AND MISCELLANEOUS↗

High-Throughput Optical Mapping for Accelerated Stress Testing of PV Module Materials

The study will provide guidance towards the design and application of larger and/or outdoor-use intended instruments. Measurement capability will be benchmarked against existing specimens, examined in a round-robin study using conventional spectrophotometer instruments. Data acquisition and storage will be integrated into the DuraMAT DataHub network along with data visualization tool, facilitating sharing of information.

14 SOLAR ENERGY↗

Petrophysical and geomechanical properties of gas hydrate-bearing sediments recovered from Alaska North Slope 2018 Hydrate-01 Stratigraphic Test Well

Knowledge of petrophysical and geomechanical properties of gas hydrate-bearing sediments are essential for predicting reservoir response due to gas production from gas hydrate reservoirs. That information will be critical parameters for designing production well completion such as specification of depressurization pump, water storage tank, and mesh size of sand screen. In December 2018, Stratigraphic Test Well Hydrate-01 was drilled in the western part of the Prudhoe Bay Unit, Alaska North Slope as part of the technical planning effort for a future long-term production test by collaborative team of DOE/NETL, USGS, and MH21-S (Boswell et al., 2020, Collett et al., 2020, Okinaka et al., 2020). Data set of logging-while-drilling (LWD) were acquired (Haines et al., 2020, Suzuki et al., 2019) and core sampling depth was determined on-site.Side-wall pressure coring was conducted to recover gas hydrate-bearing sediments from two reservoir sections named Unit-B and Unit-D. A total of 34 cores were successfully recovered by 5 runs of a wire-line deployed pressure corer (CoreVault® System - Halliburton). Core analysis scheme of this project are shown in Figure 1. All cores were quenched in liquid nitrogen while at high pressure in the laboratory of Stratum Reservoir, LLC. at Anchorage (Figure 1, a)). And the cores were removed from the pressure corer autoclave with temperature support of dry ice and stored under liquid nitrogen at atmospheric pressure. 19 damaged cores were processed to index property measurements directly such as grain size, grain density. 4 of another 17 cores were depressurized and trimmed for making a plug to analyze petrophysical properties of host sediments. Unsteady-state permeability test was conducted to obtain relative water permeability to gas and core scale NMR T2 distribution measurement was performed for evaluating pore size distribution at Houston (Figure 1, b)). Remained high quality 13 cores were preserved with gas hydrate for advanced laboratory analysis. National Institute of Advanced Industrial Science and Technology, as a part of the Japanese National Hydrate Research Program (MH21-S, funded by Ministry of Economy, Trade and Industry), received the samples at Sapporo, Japan for advanced core analysis. High-resolution X-ray CT was used to analyze the quality of the samples, which showed undisturbed lithological layers. Cores were lathed into cylindrical shape and distributed for multi property measurements (Figure 1, c)).<p>As a result, sediment from Unit-D is categorized as silty sand at ~37% porosity with ~80% gas hydrate saturation. An average hydration number n = 6.16 was measured by Raman spectroscopy. An average intrinsic permeability of ~400 mD and in situ effective permeability (with hydrate) on the order of ~10 mD. The Unit B recovered cores consisted of well sorted sand at ~40% porosity with ~95% gas hydrate saturation. An average intrinsic permeability of ~1 Darcy and in situ effective permeability on the order of ~30 mD was measured for the Unit B cores. There was a small permeability reduction due to porosity loss with increasing effective stress that simulated consolidation behavior along with depressurization in the highly permeable sandy sediment. The apparent minimum change in porosity and permeability may be caused by the low compressibility of quartz sand grains in the recovered cores. XRD and thermal conductivity analysis also suggested high quartz content. Triaxial compression tests established internal friction angles based on the Mohr-Coulomb's failure criterion, which are 40° for hydrate-bearing sediment and 29.8° for hydrate free sediment.</p>

Yoneda, Jun↗

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗

Application of Cyber-Informed Engineering for Protecting BESS

This white paper synthesizes an array of crucial grid services provided by BESS technology, assesses its architecture and communications, and presents a case study for analysis against the principles introduced by Cyber-Informed Engineering (CIE). Furthermore, in walking through the analysis, this paper presents a framework to evaluate risks and solutions when considering BESS components. Asset owners and buyers could perform this analysis to assess their BESS product implementations, alternative inverter-based resources (IBR), and energy management systems (EMS). Battery systems fulfill various roles contingent on the unique market demands and the specific challenges presented by regional grid infrastructures. These roles also vary due to the differing utility models for ownership and operation, which are adapted to meet regional and local capabilities and requirements. Concerns have been raised regarding the potential for adversaries to exploit knowledge of battery operational patterns to orchestrate decisive attacks. However, the security of operational data for these systems may not be the primary vulnerability, as much of this information is already well-understood within the community. Applying a modest degree of subject matter expertise can often yield valuable predictions regarding how a battery will respond under certain conditions, such as grid emergencies, high or low-temperature days, Public Safety Power Shutoff (PSPS) events, and outages. The operational characteristics of batteries are well-documented, and their capabilities, including the risks associated with misoperation and the resulting consequences, are published and understood within the industry. CIE practices represent the next step in gaining functional assurance and providing an acceptable level of risk, regardless of whether a battery vendor can support a trusted and validated supply chain. While this issue has exacerbated supply chain challenges, it is not an isolated condition. This foreign supply route is the primary source of BESS for the U.S. market. Significant efforts are underway through the Bipartisan Infrastructure Law (BIL) to change that. Still, strategic short-term operational mitigations are needed to ensure the security of our operational technology (OT) systems, which are enhanced by instilling trust and are separate from vendors implementing CIE principles.

25 ENERGY STORAGE↗

Direct Observation of Ammonia Storage in UiO-66 Incorporating Cu(II) Binding Sites

The presence of active sites in metal–organic framework (MOF) materials can control and affect their performance significantly in adsorption and catalysis. However, revealing the interactions between the substrate and active sites in MOFs at atomic precision remains a challenging task. Here, we report the direct observation of binding of NH 3 in a series of UiO-66 materials containing atomically dispersed defects and open Cu(I) and Cu(II) sites. While all MOFs in this series exhibit similar surface areas (1111–1135 m 2 g –1 ), decoration of the -OH site in UiO-66-defect with Cu(II) results in a 43% enhancement of the isothermal uptake of NH 3 at 273 K and 1.0 bar from 11.8 in UiO-66-defect to 16.9 mmol g –1 in UiO-66-Cu II . A 100% enhancement of dynamic adsorption of NH 3 at a concentration level of 630 ppm from 2.07 mmol g –1 in UiO-66-defect to 4.15 mmol g –1 in UiO-66-Cu II at 298 K is observed. In situ neutron powder diffraction, inelastic neutron scattering, and electron paramagnetic resonance, solid-state nuclear magnetic resonance, and infrared spectroscopies, coupled with modeling reveal that the enhanced NH 3 uptake in UiO-66-Cu II originates from a {Cu(II)···NH 3 } interaction, with a reversible change in geometry at Cu(II) from near-linear to trigonal coordination. This work represents the first example of structural elucidation of NH 3 binding in MOFs containing open metal sites and will inform the design of new efficient MOF sorbents by targeted control of active sites for NH 3 capture and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗