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All sources NASA NTRS DOE OSTI

DOE OSTI2023

A Comparative Evaluation and Selection of High-Temperature Heat Exchangers for Application to Integrated Energy Systems

The following report aims to create a refined and well-structured method for comparatively evaluating heat exchanger technologies for integrated energy systems that caters customers’ specific needs while meeting engineering requirements. For the evaluation, this study elevates previous evaluation metrics, enhances the knowledge base via literature and market surveys, and identifies the figures of merit with robust rationales to enhance the quality of decisions made throughout the proposed heat exchanger evaluation process. The heat exchanger designs evaluated as part of the case study are shell and tube heat exchangers, printed circuit heat exchangers, plate heat exchangers, spiral heat exchangers, and heat pipe heat exchangers. The information presented in this report is meant for industries interested in making a preliminary screening process to identify the most suitable heat exchanger design for their application of interest.

42 ENGINEERING↗

DOE OSTI2023

A Comparative Evaluation and Selection of High-Temperature Heat Exchangers for Application to Integrated Energy Systems

42 - ENGINEERING↗

DOE OSTI2022

“Multiagent” Screening Improves Directed Enzyme Evolution by Identifying Epistatic Mutations

Enzyme evolution has enabled numerous advances in biotechnology and synthetic biology, yet still requires many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which is caused by epistatic mutations that only offer improvements when combined with other mutations. We report an approach that incorporates diverse substrate analogues in the screening process, where multiple substrates act like multiple agents navigating the fitness landscape, identifying epistatic mutant residues without a need for testing the entire combinatorial search space. We initially validate this approach by engineering a malonyl-CoA synthetase and identify numerous epistatic mutations improving activity for several diverse substrates. The majority of these mutations would have been missed upon screening for a single substrate alone. We expect that this approach can accelerate a wide array of enzyme engineering programs.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1990

Spacecraft preliminary orbit determination using tracking measurements obtained from the Tracking and Data Relay Satellite System (TDRSS) and the Ground Spaceflight Tracking and Data Network (GSTDN)

In order to validate the operational and computational capabilities of the Preliminary Orbit Determination System (PODS), tests were performed using tracking measurements for several systems including the ERB satellite, the SMM, the STS and Landsat-4. POD procedures are utilized to generate a state vector following an unplanned orbital perturbation or spacecraft maneuver, when an estimation process such as a differential correction orbit determination cannot obtain a solution. Results are presented to demonstrate POD for several situations involving different qualities of a priori target state vectors, data type combinations, data arc lengths, and mixtures of single-TDRS, dual-TDRS, and GSTDN measurements. The system's ability to determine accurately the state vector for the spacecraft and the effectiveness of the solution screening process are discussed. It is shown that PODS is capable of determining a spacecraft vector when differential correction orbit determination processes fail.

Kirschner, S. M.↗

DOE OSTI2022

Assessment of oil and gas fields in California as potential CO 2 storage sites

California's total annual greenhouse gas (GHG) emissions (425.3 MtCO 2 e) in 2018 were about 6.4% of the US total (6,677 MtCO 2 e) and around 1% of global emissions. About 39% of 2018 GHG emissions in California were from the industrial and electrical sectors. Many of these emissions were from large stationary point sources and were suitable for carbon capture retrofit with subsequent storage of the captured carbon dioxide (CO 2 ) in geological formations. Previous studies of California found suitable geology and CO 2 storage resource. This study refines and furthers prior work using a three-stage screening process of oil fields, gas fields, and underground natural gas storage (UGS) sites by combining criteria from previous studies while excluding sites that pose technical risk or are located in regions with surface restrictions including sensitive habitats and dense populations. In the first stage, 129 CO 2 storage sites in California were identified using qualification criteria based upon formation properties including geological conditions and pore pressure. The second stage identified sensitive sites by applying conservative screens including seismic activity, faulting, population density, restricted lands, and sensitive habitats. During the third stage, 61 CO 2 potential storage sites were identified by subtraction of stage 2 areas from stage 1. The potential storage volume in the third stage ranged from 1.0 to 2.0 GtCO 2 . Finally, we applied a scoring system with seven parameters to rank the 61 potential sites based on subsurface technical criteria. The scored sites are classified as high priority, medium priority, and sites for future study. Prospective CO 2 storage sites with high and moderate priority were selected and linked to CO 2 sources. There are 14 prospective sites (above 20 MtCO 2 storage resource per site) with a total storage resource of 1024 MtCO 2 distributed in Northern and Southern California. Of these sites, there are 9 potential CO 2 -EOR sites and 1 depleted oil field with a total estimated CO 2 storage volume of ~800 MtCO 2 in the Southern San Joaquin and Ventura Basin. These 10 prospective sites with a storage resource greater than 20 MtCO 2 could potentially deliver more than 20 years of storage with an average injection rate of 40 MtCO 2 /year. The remaining 4 highly prospective sites are in Northern California. Additionally, study results suggest that saline formations should be re-evaluated in concert with storage in oil, gas, and natural gas storage reservoirs.

58 GEOSCIENCES↗

DOE OSTI2022

Methodology for assessing the maximum potential impact of separations opportunities in industrial processes

Separation technologies currently used in U.S. manufacturing industries are estimated to account for more than 20% of plant energy consumption. However, accurately determining the impact of new separation technology solutions can sometimes be difficult, especially when evaluating a slate of new candidate separation technologies, each of which has its own separation performance, energy demand, and capital cost. In these cases, a typical approach is to assess each new separation technology by collecting performance and cost information and then using that information to develop a techno-economic analysis to identify overall benefits. While this approach is thorough, it can be time consuming and can hinder reaching a critical understanding of the potential of a given separation challenge, especially when there is no known solution. To address these issues, we developed an assessment methodology, using industrial screening processes, that can be used to better understand the potential impacts of addressing a given separation challenge. This paper presents an overview of our separation challenge stream assessment methodology. The methodology involves defining an “ideal” separator and deriving the associated minimum separation energy. The “ideal” separator represents the most optimistic outlook of a given opportunity so the maximum impact from existing and not-yet-developed solutions can be assessed. Using established biorefinery models, we applied the methodology to 10 different separation challenge streams from two different biomass conversion platforms to identify the type of information that can be obtained. Three of the ten challenge streams assessed had maximum possible cost savings predictions >20%, and associated reductions in process energy carbon intensity ranging from 0 to 54%. Two streams had cost and energy savings potential that were < 5%. Some of the opportunity drivers from the various assessments include higher product yields, reduction or elimination of downstream equipment, new co-products, and cost savings associated with raw materials and energy consumption. The information from these assessments can help guide the selection or development of new separation technology solutions based on the various potential factors that drive the projected benefits.

09 BIOMASS FUELS↗

DOE OSTI2023

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Longitudinal analysis, machine learning, cancer, e↗

DOE OSTI2022

Droplet Evaporation-Based Approach for Microliter Fuel Property Measurements

Small-volume, high-throughput screening techniques are sought to enable downselection from a large candidate pool of bio-blendstocks to a select few, having physical properties consistent with requirements of downsized, turbo-boosted internal combustion engines. Herein, this work presents a droplet evaporation-based approach to predict heat of vaporization, vapor pressure, diffusion coefficient, and Lennard–Jones parameters for an unknown fuel. Two different schemes, considering the isothermal evaporation of a moving droplet in ambient air, are proposed, which combine droplet velocity and temperature measurements, with some known properties to predict unknown properties. The schemes utilize an inverse solution of a transient model of droplet evaporation solved in an iterative fashion. A baseline scheme, which only requires droplet size change measurements, is evaluated using test data for three liquid fuels, comprising of alkanes and alcohols, as obtained in a temperature-controlled chamber. Results yield temperature-dependent heat of vaporization and vapor pressure predictions within 10 % and 22 %, respectively, of reference values. The advanced scheme, which additionally requires droplet temperature measurement, is numerically evaluated in the current work and will be experimentally validated in future efforts. The advanced scheme is found to significantly improve prediction quality, with deviations less than 2 % and 1 % for heat of vaporization and vapor pressure, while also predicting diffusion coefficient and Lennard–Jones parameters within 5 % and 8 %, respectively. The combined set of approaches, which primarily track droplet evaporation, can be incorporated into a small-volume, high-throughput fuel screening process.

09 BIOMASS FUELS↗

DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Trace explosives sampling for security applications (TESSA) study: Evaluation of procedures and methodology for contact sampling efficiency

We report the detection of trace amounts of explosive materials is critical to the security at mass transit centers (e.g., airports and railway stations). In a typical screening process, a trap is used to probe a surface of interest to collect and transfer particulate residue to a detector for analysis. The collection of residues from the surface being probed is widely viewed as the limiting step in this process. A multi-institutional study was performed to establish a methodology for the evaluation of sampling media collection efficiencies. Dry deposited residues of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), C-4 (an RDX-based explosive), and pentaerythritol tetranitrate (PETN) were harvested from acrylonitrile butadiene styrene (ABS) plastic, ballistic nylon (NYL), and uncoated aluminum surfaces using muslin, Texwipe cotton, and stainless-steel mesh traps. Transfer and collection efficiencies of the sample media were calculated based on liquid chromatography-mass spectrometry analysis. Dry transfer efficiencies (DTE%) to all tested surfaces were greater than 75%, with transfer to ABS plastic being the lowest. Collection efficiency (CE%) varied significantly across the traps and the surfaces, yet some conclusions can be drawn; nylon had the lowest CE% for all cases (~10%), and the stainless steel mesh had the lowest CE% for the evaluated traps (~20%). Though the testing parameters have been standardized among the participants to establish a framework for an independent comparison of contact sampling media and surfaces, substantial variations in the DTE% and the CE% were observed, suggesting that other variables can affect contact sampling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Projector-Based Quantum Embedding for Molecular Systems: An Investigation of Three Partitioning Approaches

Projector-based embedding is a relatively recent addition to the collection of methods that seek to utilize chemical locality to provide improved computational efficiency. This work considers the interactions between the different proposed procedures for this method and their effects on the accuracy of the results. The interplay between the embedded background, projector type, partitioning scheme, and level of atomic orbital (AO) truncation are investigated on a selection of reactions from the literature. The Huzinaga projection approach proves to be more reliable than the level-shift projection when paired with other procedural options. Active subsystem partitioning from the subsystem projected AO decomposition (SPADE) procedure proves slightly better than the combination of Pipek-Mezey localization and Mulliken population screening (PMM). Along with these two options, a new partitioning criteria is proposed based on subsystem von Neumann entropy and the related subsystem orbital occupancy. This new method overlaps with the previous PMM method, but the screening process is computationally simpler. Finally, AO truncation proves to be a robust option for the tested systems when paired with the Huzinaga projection, with satisfactory results being acquired at even the most severe truncation level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Reply to: “Extracting Kondo temperature of strongly-correlated systems from the inverse local magnetic susceptibility”

In his comment1, Katanin reanalyzes our LDA + DMFT results for the temperature-dependent static local spin susceptibility of Sr 2 RuO 4 and V 2 O 3 fitting them to a Curie–Weiss (CW) form, χ(T) ≃ a/(T + θ). Invoking Wilson’s analysis of the impurity susceptibility of the spin-½ one-channel Kondo model (1CKM) in the wide-band limit, he extracts spin Kondo temperatures using T K = θ/√2, obtaining T K = 350 K and 100 K for Sr 2 RuO 4 and V 2 O 3 , respectively. Noting that these are significantly smaller than the scales $T$$^{onset}_{sp}$ = 2300 K and 1000 K reported in ref. 2, he argues that our $T$$^{onset}_{sp}$ scales “do not characterize the screening process”.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Three-dimensional photoluminescence imaging of threading dislocations in GaN by sub-band optical excitation

GaN is rapidly gaining attention for implementation in power electronics but is still impacted by its high density of threading dislocations (TDs), which have been shown to facilitate current leakage through devices limiting their performance and reliability. Here, we discuss a novel implementation of photoluminescence (PL) imaging to study TDs in regions within vertically structured p-i-n GaN (PIN) diodes consisting of metalorganic chemical vapor deposition (MOCVD) epitaxial layers grown on ammonothermal GaN (am-GaN) substrates. PL imaging with a sub-bandgap excitation energy (3.1 eV) reveals TDs with excellent clarity in three dimensions within the am-GaN substrate. Galvanometric-driven PL imaging allows the microstructure of hundreds of devices to be characterized in a single session, enhancing the screening process through the addition of device specific TD location tracking and density mapping. The visibility, structural characteristics, luminescent nature and evolution of TDs through the GaN growth process are described, potentially providing the ability to define TD structures associated with leakage current.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Band gap predictions of double perovskite oxides using machine learning

Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.

36 MATERIALS SCIENCE↗

DOE OSTI2021

High-throughput search for magnetic topological materials using spin-orbit spillage, machine learning, and experiments

Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation. Here, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from the ≈ 40000 three-dimensional materials in the JARVIS-DFT database. First, we screen materials with net magnetic moment > 0.5 μB and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The spillage acts as a signature of spin-orbit induced band-inversion. Then, we carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall conductivities, surface bandstructures, and Fermi-surfaces to determine interesting topological characteristics of the screened compounds. We also train machine learning models for predicting the spillage, bandgaps, and magnetic moments of new compounds, to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Control of plasmons in doped topological insulators via basis atoms

Collective excitations in topologically nontrivial systems have attracted considerable attention in recent years. Here we study plasmons in the Su-Schrieffer-Heeger model whose low-energy electronic band is only partially filled, such that the system is metallic. Using the random phase approximation, we calculate the intra- and interband polarization functions and determine the bulk plasmonic dispersion from the dielectric function. In this work, we find that the sublattice basis states strongly affect the polarization functions and therefore control the system’s plasmonic excitations. By varying the real-space separation of these local orbitals, one can thus selectively enhance or suppress the plasmonic energies via a tunable trade-off between intraband and interband screening processes. Specifically, this mechanism can be used to stabilize undamped high energy plasmons that have already been reported in related models. We propose scenarios on how to control and observe these effects in experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Leveraging Temperature-Dependent (Electro)Chemical Kinetics for High-Throughput Flow Battery Characterization

The library of redox-active organics that are potential candidates for electrochemical energy storage in flow batteries is exceedingly vast, necessitating high-throughput characterization of molecular lifetimes. Demonstrated extremely stable chemistries require accurate yet rapid cell cycling tests, a demand often frustrated by time-denominated capacity fade mechanisms. We have developed a high-throughput setup for elevated temperature cycling of redox flow batteries, providing a new dimension in characterization parameter space to explore. We utilize it to evaluate capacity fade rates of aqueous redox-active organic molecules, as functions of temperature. We demonstrate Arrhenius-like behavior in the temporal capacity fade rates of multiple flow battery electrolytes, permitting extrapolation to lower operating temperatures. Collectively, these results highlight the importance of accelerated decomposition protocols to expedite the screening process of candidate molecules for long lifetime flow batteries.

25 ENERGY STORAGE↗

DOE OSTI2021

Blueprint for Integrating Grid-Interactive Efficient Building (GEB) Technologies into U.S. General Services Administration Performance Contracts

This document provides recommended strategies for increasing implementation of grid-interactive efficient building (GEB) measures in federal performance contracts. This document outlines distributed energy resource screening processes, contractual considerations for GEB technologies within performance contracts, and guidance for integration of GEB analysis procedures and technologies into each phase of a performance contract.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗