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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Study on Modeled Wind Speed Errors Using the U.S. Department of Energy Buoys

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies Office. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of wind energy in the U.S. waters. The first deployment for one buoy was off the coast of Virginia from December 2014 to May 2016, and the first deployment for the other buoy was off the coast of New Jersey from November 2015 until February 2017. This report describes recent analysis of data collected during these first two deployments. Specifically, we compare hub-height wind speed estimates using Monin-Obukhov Similarity Theory (MOST) to the lidar measurements, and examine how those errors are affected by wind direction, atmospheric stability, wind-wave direction differences, and various measures of the wave-state. The comparisons are done using standard similarity functions based on MOST; including the Businger - Dyer, the Beljaars & Holtslag and the Vickers & Mahrt similarity functions. All models produce large errors over the range of atmospheric stabilities that were observed, with the largest errors occurring for stable flows. The Vickers & Mahrt function resulted in the largest overall bias and standard deviation, while Beljaars & Holtslag function gave the smallest bias and standard deviation due to its better performance under stable conditions. The models perform best under unstable conditions, but even in this regime there is a consistent overestimation of the wind speed of between roughly 0 to 1 ms -1 compared to the lidar measurements. We identify specific metocean conditions (i.e. stability and wind and wave directions) at each of the deployment locations that lead to large errors in MOST predictions. Finally, a coupled ocean-atmosphere model framework was investigated to simulate large errors in weather research forecasting (WRF).

17 WIND ENERGY↗
DOE OSTI2025

Leveraging intermediate resonances to probe CP violation at colliders

We explore the phenomenological impact of interference in tree-level contributions to three-body final states in $2\rightarrow 3$ scattering processes. This work introduces a novel search strategy leveraging asymmetries to enable sensitivity to CP-violating effects in less well-explored regions of phase space. Analytically, we demonstrate the effectiveness of this observable in probing interference between Standard Model charged-current decays and effective left-handed vector interactions, illustrated in a toy model featuring a scalar leptoquark, $S_1 \sim (3, 1, -\,1/3)$. Numerically, we apply this framework to studying the process $pp\rightarrow b \tau \nu $; unlike traditional high-$p_T$ searches or “bump hunts”, this approach utilizes an intermediate energy regime – where new physics is neither light enough to be produced on shell or heavy enough to justify an effective field theory treatment. A proof-of-principle analysis at parton level demonstrates a percent-level asymmetry, with sensitivity also to BSM weak-CP phase. While the specific phase sensitivity is diminished at particle level due to showering and detector effects, a machine learning classifier can recover sensitively to the presence of SM-BSM interference, significantly outperforming standard analysis methods. Notably discrimination between BSM signal and SM background could be achieved at the 2$\sigma $ level for the current LHC dataset and 8$\sigma $ at the High-Luminosity LHC. Moreover, this asymmetry observable as defined can also be more broadly applied to other searches for CP-violation in $2\rightarrow 3$ processes in present and future collider environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Energy-based break-even transportation distance of biomass feedstocks

The distance a solid biomass feedstock could be used to transport the feedstock when used as biobased fuel is critical information for transportation analysis. However, this information is not available. The break-even transportation distance (BTD) of various fuels from biomass feedstocks and fossil sources was analyzed for truck, rail, and ship transport modes based on bulk density, moisture content, and specific energy. Fourteen different biomass feedstocks, such as crop residues (e.g., corn stover), woody biomass (e.g., wood chips), including thermally pretreated (torrefied) and densified forms (pellets), cattle feedlot compost, and three standard fossil fuels, namely, coal, lignite, and diesel, were considered for BTD analysis and comparison. The BTD values were derived by comparing the energy content of biomass feedstocks with the energy expended in transporting the fuels through selected transportation modes. For ready reference, an alternative derivation of BTD equations and example calculations were also presented. Among the biomass feedstocks, torrefied pellets had the highest BTD (4.16 × 10 4 , 12.47 × 10 4 , and 54.14 × 10 4 km), and cattle feedlot compost had the lowest BTD (1.29 × 10 4 , 3.88 × 10 4 , and 9.23 × 10 4 km), respectively, for truck, rail, and ship. Higher bulk density and higher specific energy of the biomass feedstocks increased the BTD for all modes of transport. Transport is most efficient when mass-limited. Biomass feedstock bulk densities where transportation becomes mass-limited are 223, 1,480, and 656 kg/m 3 for truck, rail, and ship, respectively. Truck transport is typically mass-limited (payload limit restriction; increased BTD), whereas rail transport is entirely volume-limited (cargo space restriction; decreased BTD), and ship transport is mostly volume-limited for biomass feedstocks and mass-limited for densified biomass feedstocks. Ship transport is the most efficient, followed by rail and truck; on average for the materials (17) studied, rail is 3.1 times and ship is 9.2 times the truck's BTD. Based on the bulk density and higher specific energy of the biomass feedstocks, regardless of the refinery location, interstate truck transport of these feedstocks is not a limiting factor in the bio-refining process., with the studied biomass feedstock BTD per truckload representing between 0.89 and 2.88 times the US perimeter.

09 BIOMASS FUELS↗
DOE OSTI2021

Development of a rapid viability RT-PCR (RV-RT-PCR) method to detect infectious SARS-CoV-2 from swabs

Since the rapid onset of the COVID-19 pandemic, its causative virus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), continues to spread and increase the number of fatalities. To expedite studies on understanding potential surface transmission of the virus and to aid environmental epidemiological investigations, here we developed a rapid viability reverse transcriptase PCR (RV-RT-PCR) method that detects viable (infectious) SARS-CoV-2 from swab samples in <1 day compared to several days required by current gold-standard cell-culture-based methods. The method integrates cell-culture-based viral enrichment in a 96-well plate format with gene-specific RT-PCR-based analysis before and after sample incubation to determine the cycle threshold (C T ) difference (ΔC T ). An algorithm based on ΔC T ≥ 6 representing ~ 2-log or more increase in SARS-CoV-2 RNA following enrichment determines the presence of infectious virus. The RV-RT-PCR method with 2-hr viral infection and 9-hr post-infection incubation periods includes ultrafiltration to concentrate virions, resulting in detection of <50 SARS-CoV-2 virions in swab samples in 17 h (for a batch of 12 swabs), compared to days typically required by the cell-culture-based method. The SARS-CoV-2 RV-RT-PCR method may also be useful in clinical sample analysis and antiviral drug testing, and could serve as a model for developing rapid methods for other viruses of concern.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Field retrieved photovoltaic backsheet survey from diverse climate zones: Analysis of degradation patterns and phenomena

Understanding the impact of climate stressors on photovoltaic (PV) backsheet degradation in real-use conditions is critical to improve the accelerated testing exposures, extend the backsheet lifetime, and increase the confidence in PV reliability. Here, in this work, a total of 33 PV module backsheets were retrieved from six climatic zones worldwide with 2 - 28 years of exposure. These modules included five types of backsheet air-side materials (or outer layer): poly(vinylidene fluoride) (PVDF), poly(tetrafluoroethylene-co-hexafluoropropylene-co-vinylidene fluoride) (THV), poly(vinyl fluoride) (PVF), poly(ethylene terephthalate) (PET), and polyamide (PA). Attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) was used to identify air-side materials. The degradation induced color change, gloss loss, and chemical material changes analyzed using optical microscopy, differential scanning calorimetry (DSC), scanning electron microscopy (SEM), colorimetry (yellowness index (YI)), and gloss measurements. PVDF, THV, and PVF air-side layer backsheets, in particular PVF, had minimal degradation in the air-side layer appearance and chemical structures after exposure in different climatic zones. The PET air-side backsheets exhibited obvious color increase (22.55 YI units after about 9 years exposure) and the PA/PA/PA backsheets showed large gloss loss (up to 76.4 %) relative to the unexposed backsheets. Severe cracks between cells that penetrated through the entire thickness of backsheets are observed on PA/PA/PA backsheets after 4-6 years of exposure in 6 climatic zones. The current indoor exposure standards were not sufficient to identify this degradation type. However, fluoropolymer based PV backsheets showed lower levels of degradation predictors and increased climatic resistance. Specific samples (PVF) showed little change from baseline after 28 years of outdoor exposure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Methodology for the development of empirical models relating 13C NMR spectral features to fuel properties

Effective formulation of new gasoline or diesel fuels for internal combustion engines would benefit from the development of reliable models for predicting key fuel properties based on a set of molecular descriptors obtained from a single measurement. This is particularly relevant in the case of renewable fuels, where the available fuel sample quantity may be limited. In this work, we present a statistically-based methodology for building empirical models to predict multiple properties from one-dimensional 13C nuclear magnetic resonance (NMR) spectra measured on around 200 microliters of a liquid fuel. NMR spectra contain information about the molecular composition of a sample and the carbon types and molecular substructures therein. Our approach uses this information to build sparse, interpretable models, where the predicted properties are linked to specific molecular features. The approach takes into consideration the constrained nature of the features making up the one-dimensional NMR spectrum, which, after standardization, represent a relative fuel composition. We point to the limitations in interpretability that arise when building this type of empirical predictive model and suggest how these limitations may be diminished. Among the many properties important for maximizing engine performance and minimizing emissions, we build models that predict derived cetane number and distillation temperatures as these are of particular interest because of their links to fuel economy, drivability, and engine-out emissions. Results suggest that the properties of interest may be impacted by only a few of the 27 13C NMR regions represented in the data, pointing to new directions for further testing in the development of improved fuels.

predictive models, biofuels., NMR Analysis↗
DOE OSTI2024

Shock compression of cemented tungsten carbides to 100 GPa: Structure of shock waves, Hugoniot relations, and strength

Plate impact experiments are conducted on cemented tungsten carbides (WC) with a 3.7 and 6.0 wt. % cobalt binder to better understand their dynamic, high-pressure response to 100 GPa. The measured wave profiles show propagation of steady structured waves. Standard impedance matching procedures are used to determine the Hugoniot relations in the shock velocity–particle velocity (U s –v p ) and Hugoniot stress–specific volume (P–V/V o ) planes. The Hugoniot elastic limit of the samples is controlled by ductility of the Co binder and is determined to be 4.45 ± 0.29 GPa for cemented WC with 3.7 wt. % cobalt and 3.72 ± 0.24 GPa for a 6.0 wt. % cobalt binder. Both grades show a non-linear U s –v p relationship depending on whether the particle velocity is in the strength dominated or hydrodynamic regime. In the strength dominated regime, a non-linear decrease in U s is observed as v p increases from ambient to the material’s hydrodynamic limit. In the hydrodynamic regime, the U s –v p Hugoniot is linear and is determined to be U s = 4.97(±0.006)+1.446(±0.018)v p km/s for WC with 3.7 wt. % Co and U s = 4.93(±0.006)+1.463(±0.017)v p km/s for 6 wt. % Co. Both WC grades indicate shear-stress hardening with mean stress immediately after yield, followed by pressure softening, and then a sharp fall in stress carrying capacity as the mean stress is increased to ≈70 GPa (hydrodynamic limit) and beyond. This behavior is in contrast to pure WC ceramics, which show continued shear-stress hardening with mean stress to ≈80 GPa.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Artificial Soiling Replication of Field Losses on Commercial Photovoltaic Modules

Here, this study demonstrates the capabilities of an indoor artificial soiling approach developed to closely replicate the natural, cyclic soil accumulation processes in the field-dust suspension, deposition, and sedimentation/cementation-for a subtropical climate. In this work, a near-field environment is replicated in an artificial soiling cubic chamber through controlled regulation of humidity, temperature, dust type, and dust concentration, based on site-specific historical climate data. Two different models (MA and MB) of full-size commercial photovoltaic modules from a single manufacturer, installed side by side in the mid-Atlantic United States, were retrieved and subjected to artificial soiling experiments and various characterization measurements, including short-circuit current, colorimetry, reflectance, X-ray fluorescence, laser diffraction, and optical microscopy. Both in the field and in our improved field-representative artificial soiling tests, the MA model experienced roughly twice the soiling loss as the MB model. To closely replicate the field soiling losses for a site-specific climate, it is critical to include: 1) The use of field-collected dust with identical dust chemistry and particle distribution instead of standardized ISO 12103 Arizona Road dusts, 2) the use of only a small amount of field-collected dust inside the chamber during the deposition process (e.g., 0.15 g), and 3) the preconditioning of the surface coating for the partial/full dose of UV stress as experienced in the field during sunlight exposure and the abrasion as experienced in the field during regular module cleaning activities, if/as needed. The field-representative artificial soiling method developed here could potentially be adopted for rank ordering of various antisoiling coatings developed by researchers and industry stakeholders.

14 SOLAR ENERGY↗
DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗
DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗
DOE OSTI2022

Electrolytic Renewable Fuel Production Optimal Operation Investigation - H2@Scale: Cooperative Research and Development Final Report, CRADA Number CRD-19-00818

This work explored the optimal design and operation of electrolytic hydrogen production from renewable power. While there are many financial incentives for renewable hydrogen, this work investigated the effects of Low Carbon Fuel Standards (LCFS) on the hydrogen breakeven cost. To this end, this project developed optimized operational strategies for electrolytic hydrogen facilities in the California. Four specific model projects were used as the basis for the analysis. Three model projects will be located in California and one in Texas or the Midwest to be determined during the project. All projects include interconnection to the natural gas grid as a method of transport for the product fuel and the hydrogen cases will also consider alternative modes of transport. The operational optimization will maximize project return through fuel production and grid services based on future scenarios for the value of each. The effort will rely on modeling tools developed by NREL and the UCI for grid modeling as well as the integrated resource planning tool (IRP), RESOLVE, which is the official IRP tool used by the California Public Utility Commission (CPUC). The development of this project can help to inform future hydrogen deployment, policy and regulation makers, research and development decisions, and private and public investment.

08 HYDROGEN↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NOx during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

02 PETROLEUM↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NO x during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

42 ENGINEERING↗
DOE OSTI2022

Comparing Calibration Algorithms for the Rapid Characterization of Pretreated Corn Stover Using Near-Infrared Spectroscopy

Rapid characterization of biomass composition is a key enabling technology for biorefineries—the ability to measure the chemical composition of biomass materials entering the biorefinery as well as the composition of key process intermediate streams would allow real-time process control and the development of robust models to predict process performance. The utility of near-infrared (NIR) spectroscopy for rapid characterization requires multivariate algorithms for building calibration models. The most prevalent algorithm used for building calibration models using NIR spectra is the linear modeling algorithm Partial Least Squares Regression (PLS). Nonlinear regression algorithms (which are typically more computationally intensive than linear modeling approaches) have gained popularity in recent years due to their ability to solve a wide variety of classification and regression problems and the dramatic increase in available computational resources. In this work, we demonstrate that a calibration model can predict the composition of corn stover process intermediate samples pretreated with three different treatments—hot water (HW), dilute acid (DA), and deacetylation followed by dilute acid (DDA). We quantitatively compare three different algorithms for building prediction models based on near-infrared spectroscopy—partial least squares (PLS), support vector machines (SVM), and random forests (RF). We demonstrate the utility of improving model performance by accounting for instrument performance variability using repeated measurements of standard materials (e.g., the “repeatability file” strategy) and investigate its performance with nonlinear regression techniques, and we discuss methods for quantifying the uncertainties of specific predictions among the three methods.

09 BIOMASS FUELS↗
DOE OSTI2022

Understanding Solid-Electrolyte Interphase Formation at the Lithium Metal Anode of Lithium-Sulfur Batteries

Lithium-sulfur (Li-S) batteries are one of the most promising chemistries for the next generation of energy storage devices. In a standard Li-S cell, the metallic lithium anode is an essential component of the battery due to its low density, extremely high theoretical specific capacity, and very low negative electrochemical potential. However, several challenges related to the use of Li metal have prevented this battery technology from becoming commercially available. For instance, Li-anodes are highly reactive, which results in the continuous decomposition of the electrolyte and the formation of the solid-electrolyte interphase (SEI) layer. This, combined with the non-uniform deposition of Liions during plating at the anode surface, can seriously affect the performance, cycling, and safety of the battery. In addition, it has been suggested that a controlled SEI formation at the metallic Li anode can yield enhanced battery performance. Hence, a comprehensive understanding of how the SEI layer is formed can help elucidate improvements in this battery technology. In this work, the formation of the SEI layer in Li-S batteries is investigated using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. The stability of liquid organic-based electrolyte components, salt concentration, electron-rich environments, and the use of inorganic solid-state electrolyte (SSE) materials are explored in order to provide molecular-level fundamental insights into how the nature and composition of the electrolyte can alter the initial stages of the SEI formation. The products of electrolyte decomposition and electro-/chemical reaction pathways of liquid electrolyte component such as solvent and salts are predicted from AIMD simulations of electrolyte-anode interfaces. Energetics of reactions from DFT ground-state optimizations are also presented to confirm the decomposition mechanisms. Some significant differences are then drawn regarding the use of low and high salt concentrations. Simulations under electron-rich environments show additional multielectron electrochemical reactions of solvent and salt decomposition taking place due to the excess of electrons and the presence of radical anions in the solution. Finally, the initial stages of formation, stability, and main constituents of interfaces between Li-metal and Sbased electrodes with sulfide-based SSEs are characterized in detail.

25 ENERGY STORAGE↗
DOE OSTI2020

Integrating Electrical Resistivity Tomography with Vadose Zone Characterization - 20246

The environmental management mission at the U.S. Department of Energy Hanford Site includes addressing subsurface contaminants that are present in the vadose zone because of historical waste discharges. Characterization is needed to support evaluation of whether contaminants at specific disposal sites will migrate to and contaminate the groundwater in the future. Drilling of boreholes and analysis of retrieved sediment samples is a standard approach to evaluating contaminant distribution in the vadose zone for the types of inorganic and radionuclide contaminants at the Hanford Site. However, borehole sampling has limitations for understanding the volumetric distribution of contaminants. The Hanford Site is now applying electrical resistivity tomography to characterize the path and volumetric extent of contaminant migration in the vadose zone. This information aids selection of appropriate locations for obtaining borehole samples and provides three-dimensional contaminant distribution information for incorporation into conceptual site models and to help configure fate and transport models. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Advanced Test Reactor Neutron Dosimetry Report - 173B

Results of neutron dosimetry measurements in ATR for cycle 173B-1&2 are presented. Cobalt and nickel wires were installed in numerous irradiation locations for the duration of the cycle. The specific activity of these wire segments can be used in conjunction with the presented cycle history information to determine the thermal- and fast-neutron fluence rates for each irradiation position during the cycle using the referenced standard test methods.

173B↗