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Openpronghorn

OpenPronghorn is a simulation tool specifically tailored for modeling thermal-hydraulic phenomena in advanced nuclear reactors. It is built on the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source platform that facilitates the development of high-performance scientific computing applications. OpenPronghorn solves the Navier-Stokes equations, which describe the conservation of mass, momentum, and energy in fluid flows, using the finite volume numerical method. The code supports a wide range of fluid flow conditions that are applicable to nuclear reactors, including incompressible and weakly compressible flows, as well as single-phase and multiphase flows. It is capable of modeling diverse flow regimes, including laminar and turbulent flows, using various turbulence models such as the standard k-epsilon models, the v2f model, and the mixing length model. For multiphase flows, OpenPronghorn employs a mixture a Eulerian modeling approach with mixture, drift-flux, and full Eulerian models, and includes open-sourced interfacial transfer correlations for drag, exchange, and heat transfer coming from the scientific literature. OpenPronghorn's modular design allows it to handle multiscale simulations, ranging from detailed Reynolds-Averaged Navier Stokes (RANS) simulations to coarse-mesh and lumped parameter models. This flexibility enables users to perform high-fidelity simulations of specific reactor components as well as system-level analyses of entire reactor circuits. The code can be coupled with other MOOSE-based tools using the MultiApp system, allowing for the transfer of coupling quantities such as mass flow rates, heat fluxes, and boundary conditions between different simulation scales. One of the main features of OpenPronghorn is the it includes built-in validation cases from the open-source scientific literature and supports the implementation of user-defined models and correlations through MOOSE's FunctorMaterial system. OpenPronghorn is designed to be computationally efficient, leveraging the SIMPLE projection method for large-scale problems, and can be run on high-performance computing systems to handle the extensive computational demands of detailed reactor simulations. Overall, OpenPronghorn is a versatile and robust tool that provides critical insights into the thermal-hydraulic behavior of advanced nuclear reactors, supporting the design, safety, and optimization of next-generation nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

SANDWake3D

SAND2026-23958O SANDWake3D models atmospheric surface layers and turbine wakes using a 3D parabolic RANS solver. This Python-based tool uses an efficient method to simulate wakes, capturing complex wake behaviors such as wind shear, veer, and atmospheric stratification, naturally. It has been calibrated and validated against higher-fidelity large eddy simulations (LES), providing reliable results for wind energy and atmospheric research applications. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

IRMA

IRMA (In)elastic Representation of Materials As S(α,β) evaluations IRMA turns one phonon model into three outputs that usually require three separate tool chains: an evaluated nuclear-data file, predicted neutron-scattering spectra, and scattering kernels for Monte Carlo transport. The three outputs draw on a single, consistent description of the material, so the evaluation, the spectroscopy that can validate it, and the transport that uses it always agree about the physics. Nuclear data. IRMA writes ENDF-6 File 7 thermal scattering evaluations on automatically constructed (α, β) grids. This part reimplements and generalizes NJOY's LEAPR: the classic kernels reproduce freshly generated NJOY2016 tapes digit for digit and published reference tapes to about 1e-4, and the generalized paths add the exact coherent one-phonon term, anisotropic Debye-Waller tensors, coherent elastic for arbitrary crystals, and a per-species partition for polyatomic materials. The tapes feed NJOY, AMPX, FUDGE, and every transport code downstream of them. Neutron spectroscopy. The irma.spectra forward model projects the same physics onto an instrument's kinematics and resolution: INS spectra for VISION and generic indirect geometries, and 2-D S(Q,E) powder maps for direct-geometry spectrometers, from a phonopy model or straight from a phonon DOS. It can be used to predict a proposed measurement before beam time; in analysis, it supplies the calculated single-scattering counterpart of a measured spectrum, from the same material description the evaluation was built from. Monte Carlo transport. The irma.ncrystal exporter writes per-temperature scattering kernels for the companion NCrystal plugin, so McStas, OpenMC, and other NCrystal-aware codes sample the same physics. The exported kernels carry the per-site anisotropic Debye-Waller tensors, keeping directional coherent-elastic physics that NCrystal's standard scalar treatment does not represent. With the same physics inside a transport code, an entire beamline becomes a virtual experiment: IRMA's end-to-end validation ran a custom McStas implementation of the ARCS spectrometer, assembled from the existing McVine and McStas models, against measured data. From a bare crystal structure. The irma mlip front end builds the phonon model itself: a structure file and a choice of potential are enough. Nine pretrained machine-learned interatomic potentials are supported, on a laptop CPU, with no first-principles calculation; an approximate phonon model for a new material costs minutes, not a DFT campaign, and the build emits prefilled inputs for all three outputs. The result is a good starting point rather than a finished evaluation: survey-quality physics with every parameter exposed for review. A converged atomistic calculation enters the same way, as a phonopy model, when higher fidelity is needed.

Ramic, Kemal [Oak Ridge National Laboratory (ORNL)↗

The Interaction between the Nocturnal Amazonian Low-Level Jet and Convection in CESM

A nocturnal Amazonian low-level jet (ALLJ) was recently diagnosed using reanalysis data. This work assesses the ability of CESM1.2.2 to reproduce the jet and explores the mechanisms by which the ALLJ influences convection in the Amazon. The coupled CESM simulates the nocturnal ALLJ realistically, while CAM5 does not. A low-level cold air temperature bias in the eastern Amazon exists in CAM5, and thus the ALLJ is weaker than observed. However, a cold SST bias over the equatorial North Atlantic in the coupled model offsets the cold air temperature bias, producing a realistic ALLJ. Climate models significantly underestimate March–May (MAM) precipitation over the eastern Amazon. Here, we ran two sensitivity experiments using the coupled CESM by adding bottom-heavy diabatic heating at noon and midnight for 2.5 h along the coastal Amazon during MAM to mimic the occurrence of shallow precipitating convection. When heating is added during the early afternoon, coastal convection deepens and the ALLJ transports moisture inland from the ocean, preconditioning the environment for deep convective development during the ensuing hours. The increased convection over the eastern Amazon also moderately alleviates the equatorial Atlantic westerly wind bias, leading to deepening of the east Atlantic thermocline in the following months and partially improving the simulated June–August (JJA) Atlantic cold tongue in the coupled model. When heating is added at night, coastal convection does not strengthen as much and the ALLJ transports less moisture. Improvements in the simulated Atlantic winds and SST are negligible. Therefore, diurnal circulations matter to the organization of convection and rain across the Amazon, with impacts over the equatorial Atlantic.

54 ENVIRONMENTAL SCIENCES↗

Floods due to Atmospheric Rivers along the U.S. West Coast: The Role of Antecedent Soil Moisture in a Warming Climate

Precipitation extremes are projected to become more frequent along the U.S. West Coast due to increased atmospheric river (AR) activity, but the frequency of less intense precipitation events may decrease. Antecedent soil moisture (ASM) conditions can have a large impact on flood responses, especially if prestorm precipitation decreases. Taken together with increased antecedent evaporative demand due to warming, this would result in reduced soil moisture at the onset of extreme precipitation events. In this work, we examine the impact of ASM on AR-related floods in a warming climate in three basins that form a transect along the U.S. Pacific Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. Additionally, we ran the Distributed Hydrology Soil Vegetation Model (DHSVM) over the three river basins using forcings downscaled from 10 global climate models (GCMs). We examined the dynamic role of ASM by comparing the changes in the largest 50, 100, and 150 extreme events in two periods, 1951–2000 and 2050–99. In the Chehalis basin, the projected fraction of AR-related extreme discharge events slightly decreases. In the Russian basin, this fraction increases, however, and more substantially so in the Santa Margarita basin. This is due to increases in AR-related extreme precipitation events, as well as the fact that the relationship of extreme precipitation to extreme discharge is strengthened by projected increases in year-to-year volatility of annual precipitation in California, which increases the likelihood of concurrent occurrence of large storms and wet ASM conditions.

54 ENVIRONMENTAL SCIENCES↗

Fuel-air mixing in motored CFR engine at research octane number (RON) relevant condition

This paper presents a three-dimensional (3-D) computational fluid dynamics (CFD) study of a motored cooperative fuel research (CFR) engine at research octane number (RON) relevant condition. The boundary conditions for 3-D simulations were generated with a one-dimensional GT-Power model. For the first time in literature, a carburetor was added to a virtual CFR engine model with 3-D CFD. Therefore, the proposed setup can simulate the fuel and thermal stratifications inside the engine cylinder with realistic detail. The transient simulations in this work were performed within the Reynolds-averaged Navier-Stokes (RANS) framework with a Realizable k-ε turbulence model. Major conclusions from the present work are: (1) The in-cylinder flow of the CFR engine is swirl-dominated due to the existence of the intake valve shroud. (2) There is a significant amount of liquid droplets entering the cylinder during the intake stroke. The maximum instantaneous amount of liquid for 50% PRF 87 (containing 87% iso-octane and 13% n-heptane (v/v)) and 50% ethanol mixture is indicated to be around 26% of total injected fuel mass. (3) The heat of vaporization (HoV) of the fuel is responsible for creating both temperature and charge stratification inside the cylinder.

33 ADVANCED PROPULSION SYSTEMS↗

EGS Collab Experiment 3: 4100 Tensile Stimulation and Thermal Circulation Testing

These data and test descriptions are from a set of primarily tensile hydraulic-fracture stimulations in wells E2-TC and E2-TU and a subsequent chilled water circulation test conducted by injecting in well E2-TU on the 4100 level of the Sandford Underground Research Facility (SURF). Stimulations were carried out between April and May of 2022. The thermal circulation test ran semi-continuously from May 19 through August 26, 2022, though chilled water injection began on June 3. More information about the test, rationale, and processing of data is available on the EGS Collab project page, which is linked below.

15 GEOTHERMAL ENERGY↗

2001 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2001 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey. The NHTS ran from March 2001 through May 2002, with data collection for the Wisconsin add-on occurring May 1, 2001, to May 6, 2002. The NHTS collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 17,610 household samples in Wisconsin. (The total number of household samples nationwide is 26,038.)

1Hz data↗

2017 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey. The NHTS ran from April 19, 2016, through April 25, 2017. It collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 11,675 household samples in Wisconsin. (The total number of household samples nationwide is 129,112.)

1Hz data↗

2017 National Household Travel Survey—Georgia Add-On

The National Household Travel Survey (NHTS) ran from April 19, 2016, through April 25, 2017. It collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Georgia Department of Transportation participated in the NHTS add-on program and received a total of 8,631 household samples in Georgia. (The total number of household samples nationwide is 129,112.)

1Hz data↗

2017 National Household Travel Survey—Indian Nations Council of Governments (Oklahoma) Add-On

The National Household Travel Survey (NHTS) ran from April 19, 2016, through April 25, 2017. It collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Indian Nations Council of Governments participated in the NHTS add-on program and received a total of 1,049 household samples in Oklahoma. (The total number of household samples nationwide is 129,112.)

1Hz data↗

2017 National Household Travel Survey—Iowa Add-On

The National Household Travel Survey (NHTS) ran from April 19, 2016, through April 25, 2017. It collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Des Moines Area Metropolitan Planning Organization participated in the NHTS add-on program and received a total of 1,293 household samples in Iowa. (The total number of household samples nationwide is 129,112.)

1Hz data↗

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Bike and Scoot to Transit

In this project, we developed a micromobility first-mile service for customers to access public transit. We launched this service as a pilot in the Seattle area that incentivized transit customers to bike or scoot to transit. The objective was to learn how to integrate different micromobility services and provide a unified reward program. Our pilot was called “Bike and Scoot to Transit” and ran from November 2022 to September 2023. More than a dozen locations were selected near transit hubs and light rail/train stations as preferred parking locations. Trips ending at those locations were partially funded. The pilot supported 19,226 qualified trips and distributed $73,000 in total benefits for the participants of the pilot. This dataset was collected from our pilot, which includes the following: - Monthly data: Monthly trip and funding summaries. - Data summary: Data broken down based on the micromobility service provider and equipment. - Trip data: List of all trips recorded during the pilot. - Pricing models: Fees charged by micromobility service providers. ![bike to transit](bike-transit.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Geospatial Data from the Alpine Treeline Warming Experiment (ATWE) on Niwot Ridge, Colorado, USA

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive.As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

54 ENVIRONMENTAL SCIENCES↗

Supporting data for "Frozen flume experiments indicate rapid permafrost riverbank erosion depends on bank roughness"

This dataset contains supporting files detailing five frozen flume experiments conducted at the Caltech Earth Surface Dynamics Laboratory to investigate rates of ablation-limited permafrost riverbank erosion under controlled conditions. Water flowed past a bank of saturated, frozen sand and ice and gradually eroded the bank by thawing pore ice and immediately entraining sand and washing it downstream. Experiments were scaled for flow hydraulics and heat transfer allowing comparisons between our results and natural permafrost riverbanks. For each experiment, we measured the initial and final sand bank topography using a Keyence laser scanner, water surface slope at 3-min intervals throughout the experiment using a Massa sonar scanner, bank erosion using 10-sec overhead timelapse imagery taken by an overhead camera, water and bank temperature using thermistors frozen into the sand bank and sampling at 2 Hz, and water discharge using an in-line flow meter. We include calibration data for the carriage (engineered by the Saint Anthony Falls Laboratory) used to make sonar and laser topography measurements. We also include calibration data for temperature sensors, water discharge measurements, and images of a regular grid placed in the flume to align overhead camera images with the carriage datum. Grain size analysis for the channel bed (gravel) was produced using a pebble count and bank sand was measured using a Camsizer X2. In addition to the five frozen experiments, we include sonar scans of water surface slope and Keyence scans of bed and bank topography for calibration experiments ran with an immobile gravel bank and bed.

54 ENVIRONMENTAL SCIENCES↗

A search for new physics in low-energy electron recoils from the first LZ exposure

Searches for new physics signatures in low-energy electron recoils using a fiducial LXe mass of 5.5±0.2 t tonnes over a period of 60±1 live days. The following evidences of recoiling electrons from interactions with; - Solar axions - Axion-like particles - Hidden photons - Solar neutrino magnetic moment - Solar neutrino millicharge are investigated in the study. Data reported here are from Science Run 1, which ran from 23 Dec 2021 to 11 May 2022 under stable detector conditions. Single-scatter (SS) reconstruction and analysis cuts are applied to data, and the region of interest (ROI) is defined as follows; - in the range of 3–80 phd for S1c. - in the range of uncorrected S2 greater than 600 phd and S2c less than 10^{5} phd for S2c.

Source record↗

Materials Data on BaSr7Co5Cu3O20 by Materials Project

BaSr7Co5Cu3O20 crystallizes in the triclinic P1 space group. The structure is three-dimensional. there are four inequivalent Ba2+ sites. In the first Ba2+ site, Ba2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Ba–O bond distances ranging from 2.67–2.89 Å. In the second Ba2+ site, Ba2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Ba–O bond distances ranging from 2.65–2.93 Å. In the third Ba2+ site, Ba2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Ba–O bond distances ranging from 2.64–2.99 Å. In the fourth Ba2+ site, Ba2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Ba–O bond distances ranging from 2.63–3.19 Å. There are twenty-eight inequivalent Sr2+ sites. In the first Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.47–3.03 Å. In the second Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.46–3.00 Å. In the third Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–3.07 Å. In the fourth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–2.95 Å. In the fifth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–2.97 Å. In the sixth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.43–3.12 Å. In the seventh Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–3.02 Å. In the eighth Sr2+ site, Sr2+ is bonded in a 8-coordinate geometry to eight O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–2.84 Å. In the ninth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.44–2.99 Å. In the tenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.44–3.15 Å. In the eleventh Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.45–3.01 Å. In the twelfth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.50–2.97 Å. In the thirteenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–3.01 Å. In the fourteenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.45–2.97 Å. In the fifteenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–3.03 Å. In the sixteenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.43–3.00 Å. In the seventeenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.46–3.12 Å. In the eighteenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.45–3.01 Å. In the nineteenth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.50–3.02 Å. In the twentieth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.47–3.01 Å. In the twenty-first Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.46–3.08 Å. In the twenty-second Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–2.96 Å. In the twenty-third Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.47–3.00 Å. In the twenty-fourth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–3.06 Å. In the twenty-fifth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–3.02 Å. In the twenty-sixth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.48–2.99 Å. In the twenty-seventh Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.43–3.17 Å. In the twenty-eighth Sr2+ site, Sr2+ is bonded in a 9-coordinate geometry to nine O2- atoms. There are a spread of Sr–O bond distances ranging from 2.47–3.02 Å. There are twenty inequivalent Co+3.60+ sites. In the first Co+3.60+ site, Co+3.60+ is bonded to five O2- atoms to form CoO5 square pyramids that share corners with four CoO6 octahedra and a cornercorner with one CuO4 tetrahedra. The corner-sharing octahedra tilt angles range from 4–10°. There are a spread of Co–O bond distances ranging from 1.92–2.05 Å. In the second Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form CoO6 octahedra that share corners with four CoO6 octahedra and corners with two CuO4 trigonal pyramids. The corner-sharing octahedra tilt angles range from 2–11°. There are a spread of Co–O bond distances ranging from 1.82–2.23 Å. In the third Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with four CoO6 octahedra, a cornercorner with one CoO4 tetrahedra, and a cornercorner with one CuO4 tetrahedra. The corner-sharing octahedra tilt angles range from 1–12°. There are a spread of Co–O bond distances ranging from 1.84–2.57 Å. In the fourth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with four CoO6 octahedra, a cornercorner with one CoO4 tetrahedra, and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 1–13°. There are a spread of Co–O bond distances ranging from 1.83–2.51 Å. In the fifth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form CoO6 octahedra that share corners with four CoO6 octahedra, a cornercorner with one CuO4 tetrahedra, and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 1–8°. There are a spread of Co–O bond distances ranging from 1.83–2.39 Å. In the sixth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form CoO6 octahedra that share corners with four CoO6 octahedra and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 1–9°. There are a spread of Co–O bond distances ranging from 1.83–2.19 Å. In the seventh Co+3.60+ site, Co+3.60+ is bonded to five O2- atoms to form distorted CoO5 square pyramids that share corners with four CoO6 octahedra and a cornercorner with one CuO4 tetrahedra. The corner-sharing octahedra tilt angles range from 3–14°. There are a spread of Co–O bond distances ranging from 1.82–2.08 Å. In the eighth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form CoO6 octahedra that share corners with four CoO6 octahedra and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 2–9°. There are a spread of Co–O bond distances ranging from 1.83–2.24 Å. In the ninth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with two CoO6 octahedra, corners with two CoO5 square pyramids, and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 3–8°. There are a spread of Co–O bond distances ranging from 1.82–2.42 Å. In the tenth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with four CoO6 octahedra, a cornercorner with one CoO4 tetrahedra, and a cornercorner with one CuO4 tetrahedra. The corner-sharing octahedra tilt angles range from 2–11°. There are a spread of Co–O bond distances ranging from 1.91–2.52 Å. In the eleventh Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with two CoO6 octahedra, corners with two CoO5 square pyramids, and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 1–7°. There are a spread of Co–O bond distances ranging from 1.83–2.47 Å. In the twelfth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with four CoO6 octahedra, a cornercorner with one CoO4 tetrahedra, and a cornercorner with one CuO4 tetrahedra. The corner-sharing octahedra tilt angles range from 1–13°. There are a spread of Co–O bond distances ranging from 1.84–2.57 Å. In the thirteenth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with two CoO6 octahedra, corners with two CoO5 square pyramids, a cornercorner with one CoO4 tetrahedra, and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 1–12°. There are a spread of Co–O bond distances ranging from 1.83–2.47 Å. In the fourteenth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form CoO6 octahedra that share corners with four CoO6 octahedra, a cornercorner with one CuO4 tetrahedra, and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedra tilt angles range from 1–5°. There are a spread of Co–O bond distances ranging from 1.84–2.21 Å. In the fifteenth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with two CoO6 octahedra, corners with two CoO5 square pyramids, and corners with two CuO4 trigonal pyramids. The corner-sharing octahedra tilt angles range from 4–8°. There are a spread of Co–O bond distances ranging from 1.82–2.45 Å. In the sixteenth Co+3.60+ site, Co+3.60+ is bonded to six O2- atoms to form distorted CoO6 octahedra that share corners with four CoO6 octahedra, a cornercorner with one CoO4 tetrahedra, and a cornercorner with one CuO4 tetrahedra. The corner-sharing octahedra tilt angles range from 2–13°. There are a spread of Co–O bond distances ranging from 1.83–2.46 Å. In the seventeenth Co+3.60+ site, Co+3.60+ is bonded to four O2- atoms to form CoO4 tetrahedra that share a cornercorner with one CoO6 octahedra and a cornercorner with one CuO4 trigonal pyramid. The corner-sharing octahedral tilt angles are 34°. There are a spread of Co–O bond distances ranging from 1.78–1.82 Å. In the eighteenth Co+3.60+ site, Co+3.60+ is bonded to four O2- atoms to form CoO4 tetrahedra that share corners with two CoO6 octahedra and a cornercorner with one CoO4 tetrahedra. The corner-sharing octahedra tilt angles range from 22–31°. There are a spread of Co–O bond distances ranging from 1.81–1.93 Å. In the nineteenth Co+3.60+ site, Co+3.60+ is bonded to four O2- atoms to form CoO4 tetrahedra that share a cornercorner with one CoO6 octahedra and a cornercorner with one CoO4 tetrahedra. The corner-sharing octahedral tilt angles are 29°. There are a spread of Co–O bond distances ranging from 1.80–1.93 Å. In the twentieth Co+3.60+ site, Co+3.60+ is bonded to four O2- atoms to form CoO4 tetrahedra that share corners with two CoO6 octahedra and corners with two CoO4 tetrahedra. The corner-sharing octahedra tilt angles range from 20–29°. There are a spread of Co–O bond distances ran

36 MATERIALS SCIENCE↗