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NASA NTRS1999

Micromechanics Analysis Code Post-Processing (MACPOST) User Guide

As advanced composite materials have gained wider usage. the need for analytical models and computer codes to predict the thermomechanical deformation response of these materials has increased significantly. Recently, a micromechanics technique called the generalized method of cells (GMC) has been developed, which has the capability to fulfill this -oal. Tc provide a framework for GMC, the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) has been developed. As MAC/GMC has been updated, significant improvements have been made to the post-processing capabilities of the code. Through the MACPOST program, which operates directly within the MSC/PATRAN graphical pre- and post-processing package, a direct link between the analysis capabilities of MAC/GMC and the post-processing capabilities of MSC/PATRAN has been established. MACPOST has simplified the production, printing. and exportation of results for unit cells analyzed by MAC/GMC. MACPOST allows different micro-level quantities to be plotted quickly and easily in contour plots. In addition, meaningful data for X-Y plots can be examined. MACPOST thus serves as an important analysis and visualization tool for the macro- and micro-level data generated by MAC/GMC. This report serves as the user's manual for the MACPOST program.

Goldberg, Robert K.↗

DOE OSTI2026

Data and code from: Selective oxidation of 3-hydroxypropionic acid to malonic acid over Pd/C: Mechanistic and kinetic study

Malonic acid (MA) is a high-value dicarboxylic acid with strong industrial demand, yet its current production heavily relies on petrochemical feedstocks. Here, we report the first systematic study for the sustainable production of MA via oxidation of 3-hydroxypropionic acid (3-HP) with a Pd/Carbon catalyst. The effects of oxidant type (O2 and H2O2), pH, and temperature on the reaction chemistry were comprehensively evaluated. Guided by experimental observation and DFT-calculated thermochemical energetics, reaction networks for 3-HP oxidation with both oxidants are proposed and validated through kinetic modeling. MA was identified as the primary oxidation product, while further oxidation yields acetic acid and oxalic acid. The kinetic model validated the network, displaying excellent agreement (R2 > 0.95). Kinetic observations also enabled a direct comparison between O2 and H2O2 and revealed their distinct behaviors. The model was further developed into a temperature-time map, providing insight into conditions that maximize MA production. Malonic acid selectivity of 56.9 % and yield of 50.5 % were achieved at 3 bar oxygen, equimolar NaOH:3-HP ratio, and 50 °C.

3-Hydroxypropionic Acid↗

DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Unlocking the Tight Oil Reservoirs of the Powder River Basin, Wyoming

The project focused on detailed geologic characterization, geomechanical studies, well completion optimization, stimulation monitoring, and field development strategies. A key aspect involved partnerships with industry and academic collaborators such as Occidental Petroleum, Southern Illinois University, Britt Rock Mechanics and Piri Technologies. Data acquisition included drilling, logging, coring, deployment of fiber optics, and microseismic monitoring. The project emphasized feedback loops for continuous model updating and integration of economic evaluations to guide development strategies.

02 PETROLEUM↗

DOE OSTI2018

A Real-Time Online Data Product that Automatically Detects Easterly Gap-Flow Events and Precipitation Type in the Columbia River Gorge

A real-time, hourly updated, online graphical data product that displays the depth and strength of easterly gap flow in the Columbia River Gorge using a 915-MHz Doppler wind profiler is presented. During precipitation events, this data product also displays observed precipitation accumulation and diagnosed precipitation type using measurements provided by a collocated heated tipping-bucket rain gauge, an optical disdrometer, and temperature and relative humidity sensors. Automated algorithms that determine the existence and depth of the gap flow, as well as precipitation type, are described. The Columbia River Gorge is the only major gap in the Cascade Mountains of Oregon and Washington. Consequently, both easterly and westerly directed gap-flow events are common in this region. Especially during late autumn and winter, easterly gap flow can cause hazardous and damaging weather (e.g., snow, freezing rain, and strong winds) in the Portland, Oregon–Vancouver, Washington metropolitan area. The product described here was developed to help forecasters at the Portland National Weather Service Forecast Office monitor cool-season easterly gap-flow events in order to provide situational awareness and guide warnings to the public about potential weather-related hazards.

42 ENGINEERING↗

DOE OSTI2025

Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots

The cultivation of sterile giant miscanthus (Miscanthus × giganteus, M × g) for bioenergy and bioproducts has expanded into grain-cropped land in the United States (US) as local markets developed for this high-yielding perennial grass (10–30 Mg DM ha −1 ). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing-derived vegetation indices to ground truth data from 105 hand-harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high-resolution satellite-sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha −1 ) and net profits ($\$$83 to $\$$1211.5 ha −1 ) was observed. All fields were profitable in all site-years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of $\$$130 Mg −1 varied from 9.0–12.1 Mg ha −1 at 15% moisture content (7.6–10.3 Mg DM ha −1 ). Breakeven prices ranged from $\$$73 to $\$$122.4 Mg −1 , matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial-scale M × g production.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

58 GEOSCIENCES↗

NASA NTRS2018

Intra-EVA Space-to-Ground Interactions when Conducting Scientific Fieldwork Under Simulated Mars Mission Constraints

The Biologic Analog Science Associated with Lava Terrains (BASALT) project is a four-year program dedicated to iteratively designing, implementing, and evaluating concepts of operations (ConOps) and supporting capabilities to enable and enhance scientific exploration for future human Mars missions. The BASALT project has incorporated three field deployments during which real (non-simulated) biological and geochemical field science have been conducted at two high-fidelity Mars analog locations under simulated Mars mission conditions, including communication delays and data transmission limitations. BASALT's primary Science objective has been to extract basaltic samples for the purpose of investigating how microbial communities and habitability correlate with the physical and geochemical characteristics of chemically altered basalt environments. Field sites include the active East Rift Zone on the Big Island of Hawai'i, reminiscent of early Mars when basaltic volcanism and interaction with water were widespread, and the dormant eastern Snake River Plain in Idaho, similar to present-day Mars where basaltic volcanism is rare and most evidence for volcano-driven hydrothermal activity is relict. BASALT's primary Science Operations objective has been to investigate exploration ConOps and capabilities that facilitate scientific return during human-robotic exploration under Mars mission constraints. Each field deployment has consisted of ten extravehicular activities (EVAs) on the volcanic flows in which crews of two extravehicular and two intravehicular crewmembers conducted the field science while communicating across time delay and under bandwidth constraints with an Earth-based Mission Support Center (MSC) comprised of expert scientists and operators. Communication latencies of 5 and 15 min one-way light time and low (0.512 Mb/s uplink, 1.54 Mb/s downlink) and high (5.0 Mb/s uplink, 10.0 Mb/s downlink) bandwidth conditions were evaluated. EVA crewmembers communicated with the MSC via voice and text messaging. They also provided scientific instrument data, still imagery, video streams from chest-mounted cameras, GPS location tracking information. The MSC monitored and reviewed incoming data from the field across delay and provided recommendations for pre-sampling and sampling tasks based on their collective expertise. The scientists used dynamic priority ranking lists, referred to as dynamic leaderboards, to track and rank candidate samples relative to one another and against the science objectives for the current EVA and the overall mission. Updates to the dynamic leaderboards throughout the EVA were relayed regularly to the IV crewmembers. The use of these leaderboards enabled the crew to track the dynamic nature of the MSC recommendations and helped minimize crew idle time (defined as time spent waiting for input from Earth during which no other productive tasks are being performed). EVA timelines were strategically designed to enable continuous (delayed) feedback from an Earth-based Science Team while simultaneously minimizing crew idle time. Such timelines are operationally advantageous, reducing transport costs by eliminating the need for crews to return to the same locations on multiple EVAs while still providing opportunities for recommendations from science experts on Earth, and scientifically advantageous by minimizing the potential for cross-contamination across sites. This paper will highlight the space-to-ground interaction results from the three BASALT field deployments, including planned versus actual EVA timeline data, ground assimilation times (defined as the amount of time available to the MSC to provide input to the crew), and idle time. Furthermore, we describe how these results vary under the different communication latency and bandwidth conditions. Together, these data will provide a basis for guiding and prioritizing capability development for future human exploration missions.

Beaton, Kara H.↗

DOE OSTI2025

A data-driven method to estimate the antiproton background in the Mu2e experiment

The Mu2e experiment at Fermilab will search for the Charged Lepton Flavour Violating (CLFV) process of coherent, neutrinoless µ− → e − conversion in the field of an aluminum nucleus. The expected signal is a monochromatic electron with the energy of 104.97 MeV, slightly below the muon rest mass. Observation of a CLFV process would provide unambiguous evidence for Beyond the Standard Model (BSM) physics. Mu2e is sensitive to a wide range of BSM models and has the capability to distinguish between them, guiding us towards the most accurate models. The key features of the Mu2e experiment are: (1) a high intensity pulsed negative muon beam with about 1010 stopped µ −/s, and (2) a sophisticated superconducting solenoid system with a gradient magnetic field to form and guide the intense muon beam to the target. The Mu2e physics data taking is expected to begin in 2027. For Run I, the expected 5σ discovery sensitivity is Rµe = 1.2 × 10−15, with a total expected background of 0.11 ± 0.03 events. In the absence of a signal, the expected upper limit is Rµe < 6.2 × 10−16 at 90% CL. The success of this experiment hinges on the accurate estimation of the background from various SM processes that could provide signal-like electrons. One of the background processes is antiprotons annihilating in the stopping target to produce signal like electrons through π0 → γγ decays followed by γ conversions, and π− → µ−ν¯ decays followed by µ− decay. It is a relatively small background with large uncertainty (100%) due to the lack of antiproton production cross section information for the Mu2e proton beam energy of 8 GeV. We have developed a novel methodology to estimate the antiproton background in-situ. This forms the main theme of the thesis. We observed that at Mu2e energies, antiproton annihilation in the stopping target is the only source of events with multiple, simultaneous particle trajectories. From Geant4 simulations, only about 0.2% of the simulated antiproton annihilation events have a signal-like electron. Meanwhile, ∼ 5% of events have multiple reconstructible particle tracks per event. Therefore, we have devised a methodology to reconstruct the multi-track events and estimate the antiproton background by exploiting the large ratio of the production rates of the two final states.

Chithirasreemadam, Namitha [Pisa U.] (ORCID:000000↗

DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Understanding the TiH (2-x) /TiO y System at Elevated Temperature: A Literature Review

Titanium hydride of varying TiH stoichiometry is used in pyrotechnic compositions. In order to yield consistent performance, manufacturing processes must be developed to ensure precise and reproducible material properties, including composition and morphology. Legacy synthesis protocols are not comprehensive nor are the required apparatuses still available. To guide the development of novel production procedures, this report reviews literature on relevant chemical reactions and diffusion events occurring at elevated temperature in the TiH (2-x) /TiO y system. Titanium hydride exposed to air spontaneously forms a passivating oxide layer. Upon heating, significant hydrogen release, which is accompanied by changes to the surface oxide layer, is noted by 375–400°C. At higher temperatures (above about 500°C) the oxide layer is reported to be essentially nonexistent as a result of oxide-layer dissolution processes and, potentially, oxide-layer reduction due to water formation. Based on the reviewed literature, we hypothesize that, by 500°C, the surface layer consists of an oxyhydride phase, which is a solid solution of oxygen in titanium hydride. We believe that hydrogen release from titanium hydride is controlled by the kinetics of molecular hydrogen desorption on the oxyhydride surface. No literature data is available for corresponding activation energies of the dynamic desorption process, and the equilibrium phase diagram of this three-component system remains largely unexplored as well. These gaps in knowledge might be addressed through coordinated computational modeling and experimental efforts.

36 MATERIALS SCIENCE↗

DOE OSTI2019

The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals

Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional type estimation procedures are inappropriate. Probabilistic classification is more appropriate for such data but is incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations like LSST intend to use the resulting classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLASTICC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLASTICC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitive notions of classification performance. We thus choose as a metric for PLASTICC a weighted modification of the cross-entropy because it can be meaningfully interpreted in terms of information content. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic data products.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

GOOML: Geothermal Operational Optimization with Machine Learning

Geothermal Operational Optimization with Machine Learning (GOOML) is a project focused on maximizing increased availability and capacity from existing industrial-scale geothermal generation assets. The GOOML project will develop a suite of machine learning-based algorithms that analyze historical production datasets and provide predictive setpoints for geothermal field operations. Historical datasets from New Zealand and the US will provide the input to develop digital geothermal system twins which allow prediction of market conditions, maintenance operations and steamfield optimization. The algorithms will identify key parameters within fields and suggest setpoints for components of the system to maintain optimal generation. Set-points can be instructed to follow mass flow restrictions, generation maximization and optimal field/reservoir balance and give field operators a guide by which generation can be optimized. The datasets that will be used to develop GOOML are sourced from operating geothermal fields in New Zealand and the United States with varying degrees of complexity. This will ensure that most geothermal systems can utilize the GOOML tool to assist in optimizing operations. GOOML aims to achieve a step-change in geothermal operations by developing state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind automated, intelligent geothermal system model.

algorithms↗

DOE OSTI2021

Data for Integrating Transcriptomic and Metabolomic Analysis of the Oleaginous Yeast Rhodosporidium toruloides IFO0880 During Growth under Different Carbon Sources

Rhodosporidium toruloides is an oleaginous yeast capable of producing a variety of biofuels and bioproducts from diverse carbon sources. Despite numerous studies showing its promise as a platform microorganism, little is known about its metabolism and physiology. In this work, we investigated the central carbon metabolism in R. toruloides IFO0880 using transcriptomics and metabolomics during growth on glucose, xylose, acetate, or soybean oil. These substrates were chosen because they can be derived from plants. Significant changes in gene expression and metabolite concentrations were observed during growth on these four substrates. We mapped these changes onto the governing metabolic pathways to better understand how R. toruloides reprograms its metabolism to enable growth on these substrates. One notable finding concerns xylose metabolism, where poor expression of xylulokinase induces a bypass leading to arabitol production. Collectively, these results further our understanding of central carbon metabolism in R. toruloides during growth on different substrates. They may also help guide the metabolic engineering and development of better models of metabolism for R. toruloides .

Conversion↗

DOE OSTI2021

Geo Thermal Cloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The project is motivated by the challenges, risks, and costs associated with geothermal exploration and production. Many processes and parameters impacting geothermal conditions are poorly understood. Diverse datasets are available to help characterize subsurface geothermal conditions (public and proprietary; satellite, airborne surveys, vegetation/water sampling, geological, geophysical, etc.). Yet, it is not clear how to properly leverage these datasets for geothermal exploration due to an incomplete understanding of how physical processes impacting subsurface geothermal conditions are represented in these observations. Recent advancements in machine learning (ML) provide great promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. Our goals and work under Phases 1 and 2 (as proposed) of this project address all these needs.

15 GEOTHERMAL ENERGY↗

NASA NTRS1987

Arcjet thruster research and technology, phase 1

The objectives of Phase 1 were to evaluate analytically and experimentally the operation, performance, and lifetime of arcjet thrusters operating between 0.5 and 3.0 kW with catalytically decomposed hydrazine (N2H4) and to begin development of the requisite power control unit (PCU) technology. Fundamental analyses were performed of the arcjet nozzle, the gas kinetic reaction effects, the thermal environment, and the arc stabilizing vortex. The VNAP2 flow code was used to analyze arcjet nozzle performance with non-uniform entrance profiles. Viscous losses become dominant beyond expansion ratios of 50:1 because of the low Reynolds numbers. A survey of vortex phenomena and analysis techniques identified viscous dissipation and vortex breakdown as two flow instabilities that could affect arcjet operation. The gas kinetics code CREK1D was used to study the gas kinetics of high temperature N2H4 decomposition products. The arc/gas energy transfer is a non-equilibrium process because of the reaction rate constants and the short gas residence times. A thermal analysis code was used to guide design work and to provide a means to back out power losses at the anode fall based on test thermocouple data. The low flow rate and large thermal masses made optimization of a regenerative heating scheme unnecessary.

Knowles, Steven C.↗

DOE OSTI2026

Plutonium migration and phase evolution in irradiated U-Pu-Zr metallic fuels: An integrated EPMA-SEM-TEM study

Constituent redistribution is a defining feature of irradiated U-Pu-Zr metallic fuels, yet its mechanisms and effects on fuel performance are not sufficiently resolved to guide model development. Although decades of irradiation testing have established broad trends, a true mechanistic understanding of constituent redistribution has not been achieved. Here, in this study, we use electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and transmission electron microscopy-based (TEM) selective area electron diffraction (SAED) on a EBR-II irradiated U-19 wt.% Pu-6 wt.% Zr fuel pin cross-section to correlate the composition, porosity, and crystallographic phases formed after irradiation. Constituent redistribution is thought to consist of three distinct zones, in which uranium and zirconium migrate while plutonium remains relatively unchanged. Our EPMA results resolve eight distinct compositional regions, and more importantly, show that plutonium redistributes alongside zirconium, contrary to historical assumptions. The distribution of fission products was highly asymmetric with a few large lanthanide precipitates observed at isolated sites on the pin periphery instead of a uniform distribution of smaller precipitates around the periphery. Using thermodynamic data from TAF-ID and the measured EPMA compositions, matrix phase fractions were predicted across the fuel radius. Phase predictions based on composition did not match TEM/SAED results, which revealed a much higher fraction of α−U phase than would be expected if phases were retained from reactor temperatures. These findings highlight the need for expanded SAED phase identification to capture post-irradiation and storage effects, as well as rigorous uncertainty quantification in fuel performance and phase diagram modeling to better constrain predictions from compositional data.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Template Assisted Lithium Superoxide Growth for Lithium-Oxygen Batteries

Developing batteries with energy densities comparable to internal combustion technology is essential for a worldwide transition to electrified transportation. Li-O2 batteries are seen as the ‘holy grail’ of battery technologies since they have the highest theoretical energy density of all battery technologies. Current lithium-oxygen (Li-O2) batteries suffer from large charge overpotentials related to electronic resistivity of the insulating lithium peroxide (Li2O2) discharge product. One potential solution is the formation and stabilization of a lithium superoxide (LiO2) discharge intermediate that exhibits good electronic conductivity. However, LiO2 is reported to be unstable at ambient temperature despite its favorable formation energy at -1.0 eV/atom. In this paper, based on our recent work on the development of cathode materials for aprotic lithium oxygen batteries including two intermetallic compounds, LiIr3 and LiIr, that are found to form good template interfaces with LiO2, a simple goodness of fit R factor to gauge how well a template surface structure can support LiO2 growth is developed. The R factor is a quantitative measurement to calculate the geometric difference in the unit cells of specific Miller Index 2D planes of the template surface and LiO2. Using this as a guide, the R factors for LiIr3, LiIr, and La2NiO4+, are found to be good. This guide is attested by simple extension to other noble metal intermetallics with electrochemical cycling data including LiRh3, LiRh, and Li2Pd. Finally, the template concept is extended to main group elements and the R factors for LiO2 (111) and Li2Ca suggest that Li2Ca is a possible candidate for the template assisted LiO2 growth strategy.

Intermetallics↗