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DOE OSTI2022

Defining Lipidomic Responses to Coronavirus Infection

Highly pathogenic human coronavirus infection can cause a severe atypical, rapid onset pneumonia with a mortality rate of up to 10% for severe acute respiratory syndrome coronavirus 1 (SARS-CoV 1), 35% for Middle East respiratory syndrome coronavirus (MERS-CoV), or 1% for severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2 causative agent of COVID 19). While medical countermeasures successfully controlled the worldwide SARS-CoV epidemic, the MERS-CoV epidemic is still ongoing and continues to be a concern for travelers in the Middle East and the multi-year SARS-CoV 2 pandemic underscore the importance of defining biomarkers that are diagnostic and/or predictive of severe disease outcomes for respiratory viruses. Systems biology approaches provide global snapshots of infection induced changes in host cells/tissues and provide extremely rich datasets for understanding host pathogen interactions. Metabolites, especially lipids, are critical for viral replication but less is understood about infection induced changes to lipids due to limits in lipid species detection and identification. To characterize how individual lipid species and proteins with lipid associated functions contribute to highly pathogenic human coronavirus replication and disease severity, existing datasets were probed to determine cell type specific lipid responses to MERS-CoV infection and verification studies were performed to determine if modification of the host lipid signature (by inhibiting enzymatic functions that produce specific lipid species) can perturb CoV replication in human lung cells. MERS-CoV infects human lung epithelial, endothelial and fibroblast cells. All three cell types were infected with MERS-CoV and samples collected to analyze lipids, proteins, metabolites, and transcripts from 12 to 48 hours post infection. Time matched mock-infected cells were collected in parallel for each cell type. Following sample and statistical analysis, functional enrichment and bioinformatic analysis was performed to identify differentially expressed lipids and proteins. Two lipid species were found to be significantly upregulated following MERS-CoV infection, ceramides, and triacylglycerol both of which are indicative of cells undergoing apoptotic cell death. In contrast, sphingomyelins (lipid molecules that can serve as a precursor for one pathway for ceramide synthesis) had significantly decreased expression in MERS-CoV infected cells. An inhibitor of acid sphingomyelinase (that converts sphingomyelin to ceramides and phosphorylcholine) reduced MERS-CoV replication suggesting that production of ceramides is key for successful viral replication and transmission. Acyl-CoA-synthetase 3 (ACSL3), the only protein whose function is lipid associated and had increased differential expression in our dataset, regulates the synthesis of triacylglycerol (increased expression). Inhibitors that directly block ACSL3 (Triacsin C) but not steps later in the triacylglycerol synthesis pathway (Etomoxir) inhibit MERS-CoV replication suggesting that triacylglycerol production and/or ACSL3 activity is also key for viral replication. ACSL3 expression is also upregulated in lung cancer cells and is predicted to promote continued cell viability which would also enhance viral replication. The differentially expressed lipid and lipid-associated protein species identified in our studies suggest that MERS-CoV infection is activating cellular death pathways to limit the number of cells that become infected but also stimulating the production of lipid-associated enzymes that can prolong host cell viability and the amount of time progeny virions can be produced and released. As the inhibitors that worked against MERS-CoV infection were also efficacious against SARS-CoV 2 infection, countermeasures that target these host pathways may provide novel ways to block highly pathogenic human coronavirus infection and/or prevent severe disease outcomes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Comparing the response of the indigenous microbial community to crude oil amendment in oxic versus hypoxic conditions

The Caspian Sea is the world’s largest landlocked saline lake which lies between Europe and Asia. This region is particularly known for its large-scale oil reserves, pipelines, and drilling activities, which have contributed to the environmental decline of this lake. In addition to pollution from the petroleum industry, drainage from various river basins brings an influx of residential, industrial, and agricultural effluents that induce eutrophication and hypoxic conditions in deeper, colder waters, creating an oxygen gradient. The temperature and oxygen stratification in this environment has presented a unique opportunity to investigate the potential of the biodegradative processes carried out by the indigenous microbial community. We believe these indigenous microbes possess different metabolic capabilities to degrade oil as they adapted to declining oxygen concentrations and temperatures with increasing depths over a prolonged period. Hence, community structure and composition will vary with depth. Microcosms were set up to observe the indigenous microbial reaction after a 60 ppm native crude oil amendment over 115 days. Surface water microcosms were incubated at 28ºC and aerated while deep water microcosms were incubated at 8ºC under anaerobic conditions. These two environmental conditions represent the temperature and oxygen extremes along the gradient and were selected as we try to simulate the indigenous community’s response to this oil contamination. DNA was extracted and amplified from these microcosms and sequenced. Bioinformatic analysis was performed to track changes in the abundance of taxa present and biodiversity over different time points to show the progression of community structure. All microcosms showed the presence of hydrocarbon-degrading phyla, whose presence is consistent with other reports from oil-enriched environments. However, distinct communities were observed in oxic versus hypoxic microcosms. Orders of Bacteria related to sulfate and nitrogen cycling were found in hypoxic microcosms, indicating a possible mechanism for the anaerobic biodegradation of crude oil. GC-MS analysis of initial and final microcosms also provided evidence of degradation of hydrocarbon fractions in both warm, oxic and cold, hypoxic conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks

Abstract Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Scalable Pattern Matching in Metadata Graphs via Constraint Checking

Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

ECAR-8848 Rev 2 MARVEL Primary Coolant System Lifting Analysis

The objective of this ECAR is to perform a lifting analysis of the Microreactor Applications Research Validation and Evaluation (MARVEL) Primary Coolant System (PCS) vertically, pickup from horizontal, the temporary lifting plate, and spreader.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2020

Performance evaluation of an air conditioning system based on quasi isothermal dehumidifcation

This work presents a first study of novel air conditioning system based on quasi isothermal dehumidification combined with dew-point evaporative cooling through the Maisotsenko cycle (M-Cycle). The dehumidification process is realized in desiccant coated heat exchanger with heat rejection system. The key idea behind the solution is to maximize the cooling effectiveness by creating favourable conditions for dehumidification and evaporative cooling process. This is achieved by rejecting the heat of sorption in the desiccant system and providing cold and dry air for M-Cycle air cooler, which allows the system to be powered by using low-grade heat energy at 50 °C. The analysis is performed on the basis of experimentally validated numerical models of each component combined in a unified model of the system. The goal of the study is to determine the performance of the solution under different operational conditions. In addition two types of M-Cycle air coolers (i.e. cross-flow and regenerative unit) are compared to establish which unit is more suitable as the source of cooling energy for the system. It was established that proposed system is able to provide comfortable room parameters (supply air temperature equal 17 °C and humidity ratio equal 0.010 kg/kg-da) for outdoor air temperatures ranging from 28 to 36 °C and outdoor air humidity ranging from 0.010 to 0.018 kg/kg-da. This means that the proposed solution is able to provide comfortable conditions in temperate and humid climates. Results also indicate that the system consisting of the regenerative M-Cycle HMX allows better cooling load management, offers higher thermal Coefficient of Performance (COP th ) and uses less water compared to the system with the cross-flow HMX. However, the system with cross-flow heat and mass exchanger (HMX) has a relatively greater electrical Coefficient of Performance (COP el ) in most cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Sedimentary Geothermal Resources in Nevada, Utah, Colorado, and Texas

The objectives of this project were to (1) perform a literature review of sedimentary geothermal resources, (2) identify data sources and develop data-collection methodologies that characterize selected resources, (3) screen sedimentary basins and formations for sedimentary geothermal potential, and (4) evaluate the technical feasibility of one or more selected locations. Numerous publications have characterized geothermal resources within sedimentary basins. A literature search reviewed publications describing resources located in Colorado, Louisiana, Nevada, Texas, Utah, and Wyoming. The most attractive resources have high temperature gradients, low drilling costs, and reservoir permeabilities greater than 10 millidarcies (mD). Prospects in Colorado, Nevada, Texas, and Utah exhibit attractive characteristics and were chosen for further analysis. Sedimentary resources in Nevada and Utah are most attractive, followed by tested resources in Texas and untested resources in Colorado. The identified resources in Wyoming and Louisiana had lower geothermal gradients and were not evaluated. Reservoir modeling and techno-economic analysis were performed at Marys River Basin–North in Nevada. Geothermal energy production at this location is expected to have a levelized cost of energy (LCOE) ranging between 10 and 20 cents/kWh. Additional work may result in lower LCOE estimates at this location and at other attractive prospects in these three regions. The Great Basin carbonate and alluvial aquifer system of eastern Nevada and western Utah includes a lower carbonate aquifer unit, which has the potential for hosting both conduction- and convection-dominated geothermal systems. Mapping has identified lateral thickness variability expressed as a roughly 120–150-km-wide central corridor, which hosts the thickest and most continuous formations and extends from near Las Vegas north to the Idaho border. Purchase and analysis of privately held legacy seismic data could potentially compensate for the lack of sufficient data documenting measured depth to the lower carbonate aquifer unit. Multiple orogenies, extension episodes, and intrusive events have deformed and displaced the target formations of the lower carbonate aquifer unit. This structural complexity and potential for dipping reservoirs emphasizes the need for detailed geological, geophysical, and reservoir modeling. Heat flow within three Colorado sedimentary basins reviewed as part of this study was calculated in targeted studies by the Colorado Geologic Survey and Colorado School of Mines. These calculations are based on bottom-hole temperature data sets with significant limitations and some variability but produce values consistently higher than the global continental average of 65 mW/m2 for all three basins. Heat flow in the Raton Basin is the highest; however, permeability measurements from specific sedimentary formations with high heat flow have not been obtained. Promising formations for sedimentary geothermal systems were found in all three regions studied—Nevada-Utah, Colorado, and Texas. The next steps for developing sedimentary geothermal resources vary due to differences in available data and resource uncertainties. Additional scopes of work are recommended for identified basins in these three regions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

An Impedance Humidity Sensor Based on CVD Grown WSe 2 2D Films

2D materials-based planar devices have been sought after for gas and humidity sensor applications owing to their high sensitivity due to their ultrathin nature and increased surface area. Herein, 2D WSe 2 films-based planner devices were fabricated to evaluate their performance for potential application in humidity sensors. The structure and morphology of the WSe 2 sensing films were studied by Raman spectroscopy, photoluminescence, optical microscopy, and scanning electron microscopy (SEM) techniques. The relative humidity-dependent sensing performances of the 2D WSe 2 films were evaluated using current–voltage (I–V), relative impedance, and complex total impedance (electrochemical impedance spectroscopy, EIS) techniques. This sensor showed a very stable and repeatable performance investigated over the period of 18 months. The response and recovery times of the WSe 2 -based impedance sensors were found to be 40 and 30 s, respectively. The WSe 2 sensor exhibits a 2 ± 1% minimum hysteresis at a lower humidity level (50% RH) and around 8 ± 2% maximum hysteresis at relative humidity level (70% RH). This value of hysteresis is in the acceptable range during the initial investigation of any material to explore its potential for application in humidity sensors. The density functional theory (DFT) analysis was performed with the interaction from water molecules of the WSe 2 sample for a better understanding of the sensing mechanism of devices.

2D- WSe2↗

DOE OSTI2025

Measurement of off-shell Higgs boson production in the $H^*\rightarrow ZZ\rightarrow 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector

A measurement of off-shell Higgs boson production in the $H^*\to ZZ\to 4\ell$ decay channel is presented. The measurement uses 140 fb −1 of proton–proton collisions at $\sqrt{s} = 13$ TeV collected by the ATLAS detector at the Large Hadron Collider and supersedes the previous result in this decay channel using the same dataset. The data analysis is performed using a neural simulation-based inference method, which builds per-event likelihood ratios using neural networks. The observed (expected) off-shell Higgs boson production signal strength in the $ZZ\to 4\ell$ decay channel at 68% CL is $0.87^{+0.75}_{-0.54}$ ($1.00^{+1.04}_{-0.95}$ ). The evidence for off-shell Higgs boson production using the $ZZ\to 4\ell$ decay channel has an observed (expected) significance of 2.5σ (1.3σ). The expected result represents a significant improvement relative to that of the previous analysis of the same dataset, which obtained an expected significance of 0.5σ. When combined with the most recent ATLAS measurement in the $ZZ\to 2\ell 2\nu$ decay channel, the evidence for off-shell Higgs boson production has an observed (expected) significance of 3.7σ (2.4σ). The off-shell measurements are combined with the measurement of on-shell Higgs boson production to obtain constraints on the Higgs boson total width. The observed (expected) value of the Higgs boson width at 68% CL is $4.3^{+2.7}_{-1.9}$ ($4.1^{+3.5}_{-3.4}$ ) MeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗

DOE OSTI2022

Evolution of carbide precipitates in Haynes® 282 superalloy processed by wire arc additive manufacturing

Precipitation hardening regulated with heat treatments is a critical technique for tuning the mechanical properties of Ni-based superalloys fabricated with additive manufacturing, where mechanical deformation is no longer viable for microstructural refinement. As one of the key precipitates, carbides prevail in the additively manufactured alloys, whose precipitation kinetics remain largely vague. This work studies the influence of heat treatments on the evolution of MC- and M 23 C 6 -type carbides within a Haynes® 282 superalloy prepared by wire arc additive manufacturing (WAAM). SEM-EDS analysis was performed to identify the evolution pathways of the carbides, and TEM and XRD characterizations were carried out to track the crystallographic features. The MC-type carbides are originally present in the as-printed alloy, which distribute uniformly across the whole material and remain largely unchanged in size during the heat treatments. By contrast, the incipiently non-existent M 23 C 6 -type carbides precipitate during an aging treatment at 1010 °C, which majorly extend along the grain boundaries as kinetically feasible sites. Further experimental and computational analysis confirms the enhancing effect of the M 23 C 6 -type carbides on the Vickers hardness. For the first time, this work revealed the evolution pathways of carbides within a Haynes® 282 superalloy prepared by WAAM, providing critical information for tunning the microstructure and mechanical properties. Finally, the carbide evolution kinetics established in this work can be potentially extended to other alloys prepared with WAAM, providing critical information to prevent mechanical failures associated with carbide coarsening.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Pilot Study for Determining HVAC Fault Prevalence from Fault Monitoring Data

HVAC faults in U.S. commercial buildings are estimated to waste 0.7 quads of energy annually and many companies have deployed software at scale for fault detection and diagnosis (FDD). Yet the lack of comprehensive published data on fault prevalence makes it difficult for researchers, software developers, and building operators to target the most important types of faults and accelerate the widespread deployment of this technology. A multi-lab effort is underway to quantify the prevalence of commercial building HVAC faults. This paper describes initial analysis conducted on over 2-years of FDD data from 12 campus buildings to inform the methodology and data collection strategy of the full study. The analysis found a large variability in fault prevalence across different buildings, pieces of equipment, and fault types, many duplicate fault alarms, and challenges in validating faults with secondary data sources such as manually entered work order data. This paper also discusses the requirements for a successful full study, which necessitates the acquisition of a large, diverse dataset, from buildings that vary in end-use, climate region, and installed FDD software platform, as well as robust validation data easily linked to the FDD data. It is especially important to perform this analysis on data from multiple FDD providers and building owners to ensure that the results are agnostic to the software methodology used and building-specific settings. Future work will present the results of the full study, which will contain data from several different partner organizations, spanning a large proportion of U.S. climate zones.

Newman, Sarah F.↗

DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Separation, speciation, and mechanism of astatine and bismuth extraction from nitric acid into 1-octanol and methyl anthranilate

We report a detailed study of At and Bi extraction from nitric acid media into conventional solvents, namely 1-octanol and methyl anthranilate, has been performed. The analysis includes a mathematical modeling which allows the fitting of experimental data and determination of extraction constants of the two above mentioned elements. Also, this approach helped to estimate a stability constant of a weak AtO(NO3) complex along with thermodynamic constants describing the redox process of At species in the acidic solution and formation of an adduct of Bi in the presence of methyl anthranilate. The results of the fitting have been used to calculate corresponding separation factors of Bi and At as well. Moreover, a computational study has been performed to evaluate At interaction with the above mentioned solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Characterization of Shallow, Undoped Ge/SiGe Quantum Wells Commercially Grown on 8-in. (100) Si Wafers

Hole spins in Ge quantum wells have shown success in both spintronic and quantum applications, thereby increasing the demand for high-quality material. We performed material analysis and device characterization of commercially grown shallow and undoped Ge/SiGe quantum well heterostructures on 8-in. (100) Si wafers. Material analysis reveals the high crystalline quality, sharp interfaces, and uniformity of the material. We demonstrate a high mobility (1.7 × 10 5 cm 2 V –1 s –1 ) 2D hole gas in a device with a conduction threshold density of 9.2 × 10 10 cm –2 . We study the use of surface preparation as a tool to control barrier thickness, density, mobility, and interface trap density. We report interface trap densities of 6 × 10 12 eV –1 . Our results validate the material’s high quality and show that further investigation into improving device performance is needed. We conclude that surface preparations which include weak Ge etchants, such as dilute H 2 O 2 , can be used for postgrowth control of quantum well depth in Ge-rich SiGe while still providing a relatively smooth oxide–semiconductor interface. Our results show that interface state density is mostly independent of our surface preparations, thereby implying that a Si cap layer is not necessary for device performance. Transport in our devices is instead limited by the quantum well depth. Commercially sourced Ge/SiGe, such as studied here, will provide accessibility for future investigations.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Automated Energy-Dispersive X-ray Spectroscopy Analysis for Multi-Modal Few-Shot Learning

Scanning transmission electron microscopy (STEM) is a powerful tool that allows for the atomic-scale analysis of a materials’ structure, chemistry, and defect domains (Akers et al. 2021). The current generation of microscopes generate vast amounts of data, surpassing the limits of effective manual analysis traditionally performed by domain experts (Spurgeon et al. 2021). While recent strides in machine learning have significantly enhanced the processing of large and intricate datasets acquired through electron microscopy, the prevalent use of proprietary software packages for initial data collection poses a challenge. In many cases, these software packages act as a ‘black box’, constraining user functionality and hindering the output of data in a format that is conducive to seamless integration into machine learning models. This work addresses these challenges by adapting HyperSpy, an open-source Python library, for the analysis and quantification of raw energy dispersive spectroscopy (EDS) data acquired through STEM. The modified HyperSpy code successfully facilitates user-defined segmentation of the data, enabling the integration of atomic %, weight %, and raw EDS spectra for each segmented region into an existing few-shot machine learning model. While initial results reveal discrepancies in quantified atomic and weight percentages when compared to proprietary software, ongoing efforts aim to rectify this issue by refining the fit of the HyperSpy model to the EDS spectra. Overall, this research underscores the potential of open-source tools like HyperSpy to enhance the accessibility of analytical tools, fostering a transparent and user-friendly environment for seamlessly incorporating electron microscopy data into machine learning models.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Probing the antiknock effect of anisole through an ignition, speciation and modeling study of its blends with isooctane

In order to unravel the reaction pathways relevant to anisole co-oxidation within a fuel blend, a detailed study of isooctane/anisole blends was performed with the ULille RCM. Ignition delays as well as mole fraction profiles were measured during a two-stage ignition delay using sampling and GC techniques. These results are used to validate a kinetic model developed from ab initio calculations for the most relevant rate constants which included H-atom abstraction reactions from anisole, and reactions on the potential energy surfaces of methoxyphenyl + O 2 and anisyl + O 2 . Pressure dependent rate constants were computed for the methoxyphenyl + O 2 and anisyl + O 2 reactive systems using master equation code analysis. The new kinetic model shows good agreement with the experimental data. Dual brute-force sensitivity analysis was performed, on both first- and second-stages of ignition, allowing the identification of the most important reactions in the prediction of both ignition delays. It was observed that while pure anisole does not show NTC behavior, a 60/40 isooctane/anisole blend displays such behavior, as well as two-stage ignition. This suggests anisole addition may not be as beneficial to knock resistance as expected from its high octane number. The kinetic modeling results demonstrate the importance of H-abstraction reactions both from the methoxy group and from the aryl ring in ortho-position and the addition of the resultant radicals to O2, mostly leading to the formation of polar or non-aromatic products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗