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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 OSTI2025

Estimating Soil Thermal Inertia Profiles From the Passive Equilibration of a Temperature Probe

Knowledge of the distribution of soil thermal properties is important for understanding subsurface hydrological and biogeochemical processes. This study describes and evaluates quick thermal profiling (QTP), a new measurement technique aimed at providing rapid, depth-resolved measurements of soil thermal inertia at numerous locations across the landscape. A cylindrical probe with temperature sensors at multiple depths is quickly inserted into the ground, and soil thermal inertia is estimated from how quickly the probe temperature equilibrates with the soil. To this end, a finite volume heat transfer model is used to generate temperature equilibration time series across combinations of controlling factors, and a gridded search inversion approach is applied to infer soil thermal inertia. Field tests in the Arctic indicate that QTP measurements have a minimum uncertainty of 0.14 J m −2 K −1 s −1/2 and covary with dual-probe heat pulse thermal analyzer measurements (concordance correlation coefficient = 0.56) with a root-mean-square error of 0.40 J m −2 K −1 s −1/2 . Besides demonstrating the value of QTP for estimating thermal inertia, this study identifies various sources of measurement uncertainty, particularly probe-soil contact resistance and frictional heating. Further, analysis of soil samples indicates that thermal inertia can be used to estimate thermal conductivity and dry bulk density in the studied area, although such inferences are highly site-specific. Overall, the QTP method holds promise to generate thermal inertia data products and to complement other characterization approaches for advancing understanding of soil properties across far more locations than is currently possible.

Lamb, J. R. [Lawrence Berkeley National Laboratory↗
DOE OSTI2025

Demonstration of a 1820 channel multiplexer for transition-edge sensor bolometers

The scalability of most transition-edge sensor arrays is limited by the multiplexing technology, which combines their signals over a reduced number of wires and amplifiers. Here, in this Letter, we present and demonstrate a multiplexer design optimized for transition-edge sensor bolometers with 1820 sensors per readout unit, a factor of two more than the previous state-of-the-art. The design is optimized for cosmic microwave background imaging applications, and it builds on previous microwave superconducting quantum interference device multiplexers by doubling the available readout bandwidth to the full 4–8 GHz octave. Evaluating the key performance metrics of yield, sensitivity, and crosstalk through laboratory testing, we find an end-to-end operable detector yield of 78%, a typical nearest-neighbor crosstalk amplitude of ∼0.4%, and a median white noise level of 83 pA/$\sqrt{\textrm{Hz}}$ due to the multiplexer, corresponding to an estimated contribution of 4% to the total system noise for a ground-based cosmic microwave background telescope. Additionally, we identify a possible path toward reducing resonator loss for future designs with reduced noise. We expect these developments to alleviate the system complexity, cryogenic requirements, and cost of future large arrays of low temperature detectors.

cosmic microwave background↗
DOE OSTI2022

A PWM Strategy for Cascaded H-bridges to Reduce the Loss Caused by Parasitic Capacitances of Medium Voltage Dual Active Bridge Transformers

In this paper, to reduce the power loss caused by the parasitic capacitances of medium voltage medium frequency transformers in a two-stage dc/ac converter, including a dual active bridge (DAB)-based dc/dc stage and a cascaded H-bridge (CHB)-based dc/ac stage, a PWM strategy is proposed for the dc/ac stage. For each H-bridge, by shifting the high switching frequency to one half-bridge and having the other half-bridge switching at the fundamental frequency of the ac voltage, equivalent frequencies of transformer terminal-to-ground voltages are reduced. As a result, the frequencies of charging and discharging current to the parasitic capacitance are reduced, and therefore the loss caused by the parasitic capacitances is reduced. Compared to the conventional phase-shift PWM, the proposed PWM strategy can reduce the parasitic capacitance loss by about 75%. Experimental test results of one phase in the 13.8 kV/ 100 kW converter prototype are provided to validate the approach.

Li, Haiguo↗
DOE OSTI2021

A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Bringing Automated Fault Detection and Diagnostics Tools for HVAC&R Into the Mainstream

Heating, ventilation, and air-conditioning (HVAC) systems consume over 5 quads of energy annually, representing 30% of energy consumption in the U.S. commercial buildings. Additionally, commercial refrigeration (R) systems add about 1 quad to commercial buildings energy consumption. Most HVAC systems operate with one or more faults that result in increased energy consumption. Fault detection and diagnostics (FDD) tools have been developed to address this national issue, and many tools are commercially available. FDD tools have the potential to save considerable energy for an existing commercial rooftop unit (RTU) and refrigeration systems. These devices can be used for both retro commissioning and, when faults are addressed, continuous commissioning. However, there appears to be multiple market barriers for this technology. Although there are efforts to develop FDD tool standards, currently there are no standards and methods to define functions, capabilities, accuracy, and reliability of FDD tools in the field. Moreover, most of the commercial FDD tools have not been verified in the field independently. This paper presents a comprehensive approach for bringing HVAC FDD tools into the mainstream. The approach involves demonstrating ten commercially available FDD tools installed at ten different sites, independent testing and evaluation of the FDD tools, communication with various stakeholders, identifying and assessing market barriers, creating a process evaluation methodology, and assisting utility companies in developing incentive programs. Furthermore, the preliminary baseline results from the case study demonstrate how the use of an independent monitoring system (IMS) can be used for ground-truth in evaluating FDD tools in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Computational Methods to Characterize Panel Loading Conditions for Accelerated Testing

Panel cracking and degradation due to wind loading are known to have a detrimental effect on power output. Recreating these damaging conditions in a controlled experimental setting requires an understanding of the loads being generated at the panel surface as a function of both wind speed, wind direction, and panel orientation. To better understand these relationships, a computational fluid dynamics (CFD) simulation package was constructed using an open-source Python library for solving partial differential equations with the finite element method. This CFD package allows the simulated wind speed and panel orientation of a multi-panel array to be easily changed and provides the capability to measure the traction forces at discrete points along the sun-facing and ground-facing surface of an interior panel residing in the wake of one or more upstream panels. A parameter exploration was performed in which the panel angle was varied in even increments from -70 deg to 70 deg and the wind speed was varied from 2 to 30 m/s. During post processing, the measured traction along the panel surface was averaged spatially and interpreted as a time-varying signal, where further processing of this signal yielded the root mean square amplitude and a characteristic frequency associated with the loading. This study found that higher wind speeds are generally associated with increased amplitude of loading and that the panel orientation angle can significantly exacerbate or mitigate this loading. These outcomes are presented along with current work on higher-fidelity verification simulations and recommendations for performing accelerated experimental testing.

loading↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2023

Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, V c,max and maximum electron transport rate, J max ). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (V c,max25 and J max25 ) traits (n=135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. Here we further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate V c,max (R 2 =0.55, nRMSE=11.79%), Jmax (R 2 =0.54, nRMSE=12.34%), and five additional foliar traits (R 2 =0.38-0.60, nRMSE=10.11-13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectratrait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Low-Lying Excited States of Linear All- Trans Polyenes: Insights from Analytic Gradient and Nonadiabatic Coupling Calculations Based on Multireference Configuration Interaction

Polyenes serve as a rigorous test for theoretical models and electronic structure methods, playing a key role in advancing computational and theoretical chemistry. Here, we present a high-level theoretical investigation of linear, all-trans polyenes using energy gradients and nonadiabatic coupling vectors based on an MR-CISD wave function to describe electronic transitions involving the ground state (1 1 A g – ) and three low-lying excited states (2 1 A g – , 1 1 B u + , and 2 1 B u – ) of hexatriene, octatetraene, and decapentaene. This approach enables accurate evaluation of both adiabatic and vertical excitation and emission energies, yielding results in excellent agreement with experiment, as well as locating minima on the crossing seam between adiabatic states. Our results show that vertical excitation energies to the 1 1 B u + state are blue-shifted by 0.2–0.3 eV relative to the experimental absorption maximum, whereas the vertical emission energy from the 2 1 A g – state is red-shifted by ∼0.2 eV relative to the experimental emission maximum. Upon relaxation from the Franck–Condon geometry, the 2 1 A g – state stabilizes by around 1 eV, compared to 0.2–0.3 eV for the 1 1 B u + state. An analysis of the S 1 /S 0 crossing seam in hexatriene shows that its minimum involves asymmetric backbone deformations and provides an efficient channel for ultrafast internal conversion to the ground state, consistent with the absence of detectable fluorescence in this molecule. These results demonstrate the power of analytic gradients and nonadiabatic coupling vectors based on an MR-CISD wave function for accurately characterizing the electronic structure and photophysics of polyenes.

Excited states↗
DOE OSTI2024

Constraints on fifth forces and ultralight dark matter from OSIRIS-REx target asteroid Bennu

It is important to test the possible existence of fifth forces, as ultralight bosons that would mediate these are predicted to exist in several well-motivated extensions of the Standard Model. Recent work indicated asteroids as promising probes, but applications to real data are lacking so far. Here we use the OSIRIS-REx mission and ground-based tracking data for the asteroid Bennu to derive constraints on fifth forces. Our limits are strongest for mediator masses m ~ (10 -18 -10 -17 ) eV, where we currently achieve the tightest bounds. These can be translated to a wide class of models leading to Yukawa-type fifth forces, and we demonstrate how they apply to U(1) B dark photons and baryon-coupled scalars. Our results demonstrate the potential of asteroid tracking in probing well-motivated extensions of the Standard Model and ultralight bosons near the fuzzy dark matter range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environment, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. Here, the results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Integrating crop growth models with remote sensing for predicting biomass yield of sorghum

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.

Yang, Kai-Wei↗
DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2021

Annual Status Report (FY 2020): Performance Assessment for the Disposal of Low Level Waste in the 200 East Area Burial Grounds

This annual review provides the projected dose estimates of radionuclide inventories disposed in the 200 East Area Low-Level Waste Burial Grounds (LLBGs) since September 26, 1988. These estimates are calculated using the original dose methodology developed in the performance assessment (PA) analysis (WHC-SD-WM-TI-730). The estimates are compared with requirements of DOE O 435.1 Chg 1 and performance objectives defined in companion documents DOE M 435.1-1 Chg 1and DOE-STD-5002-2017). All performance objectives are currently satisfied, and operational waste acceptance criteria (HNF-EP-0063) and waste acceptance practices continue to be sufficient to maintain compliance with performance objectives. Inventory estimates and associated dose estimates from future waste disposal actions are unchanged from previous years’ evaluations that indicate potential impacts well below performance objectives; therefore, future compliance with DOE O 435.1 Chg 1 is expected. A new PA study was initiated in fiscal year (FY) 2019 for evaluation of active disposal sites within the 200 East and 200 West Areas (Trench 94 in 200 East; Trenches 31 and 34 in 200 West) due to extended time elapsing between the current annual status report and the original PA for the active disposal sites. The new PA for the active disposal sites is expected to be completed in FY 2021. Within the active burial grounds in the 200 East Area, low-level waste and mixed low-level waste will continue to be disposed of in the dedicated U.S. Navy reactor compartment trench at the 218-E-12B Burial Ground (Trench 94). During this reporting period (FY 2020, from October 1, 2019, through September 30, 2020), two reactor compartments were disposed in Trench 94. Results from sorption experiments are summarized for this reporting period to quantify the efficacy of concrete waste forms in retaining key radionuclides (e.g., technetium-99 and iodine-129). The test durations ranged from 1 to 3 months. Continued groundwater monitoring of the 200 East Area LLBGs indicates no groundwater contamination due to LLBG waste. Current assumptions about future land use at the Hanford Site are consistent with PA analysis1 assumptions of a post-closure facility that will not be degraded by human activity. The LLBGs are located in an area identified for waste management and containment of residual contamination. This area will remain after final environmental remediation and the proposed shrinkage of Hanford Site boundaries to small sections within the 200 East and 200 West Areas in the Central Plateau (DOE/EIS-0391). The current closure plan for the LLBGs (DOE/RL-2000-707) estimates that the 200 East LLBGs will be closed in the 2050 timeframe. The Disposal Authorization Statement, other technical basis documents, and the radioactive waste management basis are of continued adequacy to meet the performance objectives of DOE O 435.1 Chg 1. Overall, there are no substantive changes to primary PA assumptions nor the PA analysis conclusion; therefore, compliance with DOE O 435.1 Chg 1 and the Disposal Authorization Statement is maintained.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Audit Report: Automated Meteorological Monitoring Stations, Lawrence Livermore National Laboratory

The Waste and Air Quality Offices Group at the Lawrence Livermore National Laboratory operates two main meteorological monitoring sites near Livermore, California. The primary met tower (Site 200) is located in the northwest corner of the lab and consists of a fifty-two (52) meter tower with vertical wind speed sensors, horizontal wind speed and direction sensors and air temperature sensors at fifty-two (52) meters, twenty-three (23) meters and ten (10) meters. In addition, there is a temperature and humidity probe at ten (10) meters. At two (2) meters there are upward and downward facing solar and infrared radiation sensors, an air temperature sensor, barometric pressure sensor and a temperature and relative humidity probe. At ground level there is a tipping bucket rain gauge located to the southwest of the tower. The datalogger, communication peripherals and the barometric pressure sensor are housed in a small building near the tower. The Site 300 met site is located approximately 10 miles east of Livermore within the boundary of the LLNL blast test site. This site also consists of a 52-meter tower which is configured the same as the 52-meter tower at Site 200. There is also a building at this site that houses the datalogger, communication peripherals and the barometric pressure sensor. At both Site 200 and Site 300, data are collected by Ethernet with telephone modem collection as a backup. The data collection interval for both sites is fifteen (15) minutes via workstations running Loggernet software located at the main lab facility. The audits of the Lawrence Livermore National Laboratory’s two automated meteorological stations located near Livermore, California were performed on October 24 & 25, 2023. These audits were performed under the guidelines of the Quality Assurance Handbook for Air Pollution Measurement Systems, Volume IV, Version 2 and the On-Site Meteorological Program Guidance for Regulatory Modeling Applications, U.S. EPA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying Species Populations in Multivalent Borohydride Electrolytes

Multivalent batteries represent an important beyond Li-ion energy storage concept. The prospect of calcium batteries, in particular, has emerged recently due to novel electrolyte demonstrations, especially that of a ground-breaking combination of the borohydride salt Ca(BH 4 ) 2 dissolved in tetrahydrofuran. Recent analysis of magnesium and calcium versions of this electrolyte led to the identification of divergent speciation pathways for Mg 2+ and Ca 2+ despite identical anions and solvents, owing to differences in cation size and attendant flexibility of coordination. To test these proposed speciation equilibria and develop a more quantitative understanding thereof, we have applied pulsed-field-gradient nuclear magnetic resonance and dielectric relaxation spectroscopy to study these electrolytes. Additionally, concentration-dependent variation in anion diffusivities and solution dipole relaxations, interpreted with the aid of molecular dynamics simulations, confirms these divergent Mg 2+ and Ca 2+ speciation pathways. These results provide a more quantitative description of the electroactive species populations. We find that these species are present in relatively small quantities, even in the highly active Ca(BH 4 ) 2 /tetrahydrofuran electrolyte. This finding helps interpret previous characterizations of metal deposition efficiency and morphology control and thus provides important fundamental insight into the dynamic properties of multivalent electrolytes for next-generation batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Spectrum of fully-heavy tetraquarks from a diquark+antidiquark perspective

Using a relativized diquark model Hamiltonian, we calculate the masses of J PC = 0 + + ground-state tetraquarks in the following systems: b s b ¯ s ¯ , b b n ¯ n ¯ ( n = u , d ), b b s ¯ s ¯ , c c c ¯ c ¯ , b b b ¯ b ¯ , b c b ¯ c ¯ and b b c ¯ c ¯ . We also compute extensive spectra for the fully-heavy quark flavour combinations. Finally, as a test of the diquark model approach, we compute the masses of fully-heavy baryons in the diquark model. Our results may be compared soon to the forthcoming experimental data for fully-heavy three-quark systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗