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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 OSTI2024

Unconventional Anomalous Hall Effect Driven by Self‐Intercalation in Covalent 2D Magnet Cr 2 Te 3

Covalent 2D magnets such as Cr 2 Te 3 , which feature self-intercalated magnetic cations located between monolayers of transition-metal dichalcogenide material, offer a unique platform for controlling magnetic order and spin texture, enabling new potential applications for spintronic devices. Here, it is demonstrated that the unconventional anomalous Hall effect (AHE) in Cr 2 Te 3 , characterized by additional humps and dips near the coercive field in AHE hysteresis, originates from an intrinsic mechanism dictated by the self-intercalation. This mechanism is distinctly different from previously proposed mechanisms such as topological Hall effect, or two-channel AHE arising from spatial inhomogeneities. Crucially, multiple Weyl-like nodes emerge in the electronic band structure due to strong spin-orbit coupling, whose positions relative to the Fermi level is sensitively modulated by the canting angles of the self-intercalated Cr cations. These nodes contribute strongly to the Berry curvature and AHE conductivity. This component competes with the contribution from bands that are less affected by the self-intercalation, resulting in a sign change in AHE with temperature and the emergence of additional humps and dips. The findings provide compelling evidence for the intrinsic origin of the unconventional AHE in Cr 2 Te 3 and further establish self-intercalation as a control knob for engineering AHE in complex magnets.

2D magnets↗
DOE OSTI2026

Dynamical moment fragmentation in the all-in all-out pyrochlore Nd 2 ⁢Sn 2⁢ O 7

We report single crystal neutron spectroscopy and bulk characterization on hydrothermally grown Nd 2 Sn 2 O 7 , revealing magnetic moment fragmentation embedded within the all-in–all-out ordered state. The spectra reveal a nearly flat band with pinch-point momentum dependence accompanied by dispersive branches that produce half-moon features across multiple Brillouin zones. These defining signatures are captured quantitatively by a minimal dipolar-octupolar spin Hamiltonian, demonstrating excellent agreement between experiment and theory. The higher flat-mode energy helps account for the absence of dynamical interference in prior μSR studies, while the lack of any photon-like excitation imposes strict constraints on the proposed Coulombic antiferromagnet scenario. Furthermore, our results extend moment fragmentation to Nd 2 Sn 2 O 7 and identify it as a clean, tractable platform for quantitative exploration of emergent gauge field physics in frustrated magnets.

DC susceptibility measurements↗
DOE OSTI2023

Blockchain for Fault-Tolerant Grid Operations

Radial topology and vast geographic coverage make distribution systems prone to widespread power outages upon the failure of a single (or multiple) upstream component. Fault-handling algorithms depend heavily on correct estimations of the system’s state to effectively isolate the affected area and reduce the number of affected customers while maintaining operational safety. The work described here leverages the core features of distributed, consensus-based decision-making processes and the immutability of blockchain, and demonstrates their value in improving fault-tolerant grid operations. In this work, blockchain was used to create a trusted data-sharing platform that enables independent actors to reconstruct the system state; this enables distributed resources to make intelligent decisions with limited knowledge. Although the process requires data sharing, its algorithms have been designed to limit the amount of private information that is exchanged, which helps preserve business-sensitive data and maintain customer privacy. In addition, by reducing the information that must be shared, the communication requirements are also reduced; (however, an in-depth analysis of the communication requirements is beyond the scope of this project). The proposed use cases are intended to represent a foundational basis for third parties to develop functional solutions that can eventually be deployed in the field. To further provide guidance, the envisioned use cases have incorporated design requirements that consider the blockchain characteristics and a need to limit information from surrounding resources, which preserve the assumption and the possibility that such resources could belong to different entities. This report presents a detailed design of the three use cases with the tools needed to enable the analysis being tested. The implemented gross error detection method can detect mismatches when the error exceeds 3.8 times the sensor’s rated accuracy. Detection of the circuit breaker state successfully identified the correct states across all simulation tests. A distribution-system power-flow solution in the simulator OpenDSS generally possesses a convergency tolerance of 0.01% on the voltage magnitude. The evaluation of possible reconnection using voltage magnitude—preserving the data ownership—has a voltage magnitude difference smaller than 0.001% from the OpenDSS result. The results preserving data ownership have a difference within the expected power flow tolerance with full knowledge of the system, which surpasses expectations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Catalytic Upgrading of Renewable Feedstock (Final Technical Report)

The goal of this DOE-funded project was to investigate the fundamental science related to the development of homogeneous (de)hydrogenation catalysts in order to enable energy-relevant transformations of bio-relevant chemical feedstocks including ethanol. The primary focus was to improve the activity of ethanol upgrading catalysts, specifically informed through mechanistic studies, in order to enable rational design optimization strategies. Following in depth mechanistic studies, targeted reaction optimization approaches included ligand redesign to improve catalyst stability, developing new carbon-carbon bond forming reactions using ethanol as a precursor, and examining photochemically-mediated reactions, ultimately to integrate within other reactor designs such as continuous flow reactors. The high modularity of the catalyst components (ligands) has enabled the preparation and analyses of multiple catalyst precursors. These studies uncovered an unexpectedly beneficial substitution pattern of the ligand structure that led to the development of new catalysts that are the best in class for upgrading ethanol to butanol with a turnover number of 155,890 and a turnover frequency of 12,690 h –1 . In addition to upgrading ethanol to butanol, cascade reaction sequences were developed to form new C-C bonds using ethanol as a bio-relevant feedstock, providing access to platform chemicals from renewable sources. As part of the reaction discovery process, new mechanistic details were uncovered that provided insights into: a) catalyst speciation, b) decomposition pathways, c) carbon monoxide releasing pathways, and d) carbon-carbon and carbon hydrogen bond breaking pathways. Most of these outcomes were previously not known; however, they provide important directions for new catalyst design strategies. Finally, use of high throughput and in situ photochemical reaction analyses enabled detailed studies into changes to the catalyst structure upon irradiation. Irradiation was found to improve hydrogen transfer catalysis, by promoting a ligand dissociation event.

09 BIOMASS FUELS↗
DOE OSTI2022

Dynamics and growth rate implications of ribosomes and mRNAs interaction in E. coli

Understanding how cells grow and adapt under various nutrient conditions is pivotal in the study of biological stoichiometry. Recent studies provide empirical evidence that cells use multiple strategies to maintain an optimal protein production rate under different nutrient conditions. Mathematical models can provide a solid theoretical foundation that can explain experimental observations and generate testable hypotheses to further our understanding of the growth process. In this study, we generalize a modeling framework that centers on the translation process and study its asymptotic behaviors to validate algebraic manipulations involving the steady states. Using experimental results on the growth of E. coli under C-, N-, and P-limited environments, we simulate the expected quantitative measurements to show the feasibility of using the model to explain empirical evidence. Our results support the findings that cells employ multiple strategies to maintain a similar protein production rate across different nutrient limitations. Moreover, we find that the previous study underestimates the significance of certain biological rates, such as the binding rate of ribosomes to mRNA and the transition rate between different ribosomal stages. Furthermore, our simulation shows that the strategies used by cells under C- and P-limitations result in a faster overall growth dynamics than under N-limitation. In conclusion, the general modeling framework provides a valuable platform to study cell growth under different nutrient supply conditions, which also allows straightforward extensions to the coupling of transcription, translation, and energetics to deepen our understanding of the growth process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Rays for Roots - Integrating Backscatter X-Ray Phenotyping, Modeling and Genetics to Increase Carbon Sequestration and Switchgrass Resource Use (Final Report)

To increase carbon (C) deposition in the soil and enhance crop resource use efficiency, characterizing root form and function is essential. Several root and soil traits have been linked to increased root-to-soil C transfer. Technology that could provide high-resolution characterization of many of these traits in field conditions would revolutionize our ability to study and understand how to increase C sequestration. In this effort, we developed an initial early prototype backscatter X-ray system for non-destructive imaging of root traits. We collected initial backscatter X-ray data in field and lab settings and carried out early analysis of these data. Along with this prototype, we also developed a suite of root phenotyping approaches including advanced minirhizotron image analysis, soil core imaging, and mesocosm imaging. Minirhizotron (MR) tubes are clear tubes inserted into the soil in the field and used to image roots and the surrounding soil. Our team has developed deep learning-based methods that can segment roots from soil that can learn from imprecise image-level labels. The ability to learn or fine-tune our deep learning algorithms from image-level labels allows easier and faster application of these approaches to new locations and new plant species. We have successfully implemented and applied our MR analysis approaches to thousands of switchgrass MR images collected across geographical regions. An advantage of MR imaging is the ability to collect root and soil images over time. Our soil core analysis included collecting hundreds of soil core samples from harvested switchgrass fields and imaging these cores with both X-ray CT and backscatter X-ray imaging. Initial segmentation approaches for the X-ray CT images of these cores have been developed and applied. An advantage of soil core analysis is that it preserves the three-dimensional structures of the roots and soil in the core collected. Our group also developed photogrammetry-based mesocosm root imaging and phenotyping approaches. In this approach, a plant was grown in a large mesocosm with a three-dimensional grid of thin supporting lines inserted throughout the mesocosm. After the plant (and, correspondingly, the root architecture is grown and established) the soil media was removed and the supporting lines approximately preserved the three-dimensional root architecture. Then, we applied photogrammetry techniques to create a three-dimensional digital representation of the root architecture for which we developed analysis algorithms including skeletonization. We carried out our phenotyping development with powerful switchgrass resources and physiological and agroecosystem modeling to deliver novel technology. This project contributes to multiple ARPA-E missions including reduction of foreign imports of energy, reduction of energy-related emissions including greenhouse gases, and ensuring that the United States maintains a technological lead in developing and deploying advanced energy technology. Furthermore, the developed tools could transform public and private plant breeding and could be broadly applicable to other crops and, potentially, other application areas. Our team of engineers, plant and soil scientists, and modelers i) developed an early prototype backscatter X-ray platform that can operate in field conditions; ii) developed a suite of root phenotyping and characterization approaches as described above; iii) developed and carried out plant biology and physiology roots studies and; iv) developed and implemented mechanistic physiological modeling.

42 ENGINEERING↗
DOE OSTI2023

EDX Spatial: Leveraging cloud and hybrid data management resources for spatial data

In the last few years, the National Energy Technology Laboratory has started leveraging cloud-hosted services for hosting spatial data collections published on the Energy Data eXchange (EDX). Using cloud-hosted storage and compute options offers multiple benefits for visualization and tool development through utilization of spatial data resources. The transition from on-premises services to cloud-hosted services has enabled a few key features: increased accessibility of large derivative datasets, dynamic integration of external authoritative data resources directly from outside entities through representational state transfer application programming interfaces (REST API), and the ability to produce complex mapping applications, dashboards, and online maps. As a result of the shift, NETL has launched EDX Spatial, a platform that leverages on-premises resources combined with cloud compute capabilities to enable enhanced online mapping interfaces and optimize data access. In addition, a unified workflow for handling the public release of spatial data products through EDX has been developed, including standardization of data hosting practices, metadata, symbology, and application elements. This poster reviews the opportunity of leveraging cloud-hosted and hybrid data management solutions for spatial data, and discusses the benefits and lessons learned while leveraging these services through the data repository EDX.

Morkner, Paige↗
DOE OSTI2021

Application of an energy-dependent instrument response function to analysis of nTOF data from cryogenic DT experiments

Neutron time-of-flight (nTOF) detectors are used to diagnose the conditions present in inertial confinement fusion (ICF) experiments and basic laboratory physics experiments performed on an ICF platform. The instrument response function (IRF) of these detectors is constructed by convolution of two components: an x-ray IRF and a neutron interaction response. The shape of the neutron interaction response varies with incident neutron energy, changing the shape of the total IRF. Analyses of nTOF data that span a broad range of energies must account for this energy-dependence in order to accurately infer plasma parameters and nuclear properties in ICF experiments. This work briefly reviews a matrix multiplication approach to convolution which allows for an energy-dependent change in the shape of the IRF. This method is applied to synthetic data resembling symmetric cryogenic DT implosions to examine the effect of the energy-dependent IRF on the inferred areal density. Here, results of forward fits that infer ion temperatures and areal densities from nTOF data collected during cryogenic DT experiments on OMEGA are also discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Demonstration of neutron-yield enhancement by laser preheating and magnetization of laser-driven cylindrical implosions

Magnetized liner inertial fusion (MagLIF) is a fusion concept that uses magnetized, preheated fuel to reduce the implosion velocities and convergence ratios required for ignition. A scaled, laser-driven experimental platform to study MagLIF has been demonstrated on the OMEGA laser system, providing comprehensive experimental data on MagLIF scaling, utilizing the higher shot rate on OMEGA compared to the Z machine. Using this platform, a broader experimental space for MagLIF has been studied. Presented in this article are experimental results that demonstrate that the combination of preheat and magnetization enhances the neutron yield by 470% compared to a reference implosion, significantly more than the yield enhancement by the field or preheat alone. These results are achieved while maintaining a relatively low convergence ratio (<20). Here, the experiments were supported by one-, two-, and three-dimensional radiation-hydrodynamics simulations, all of which suggest that multiple sources of mix play different key roles depending on the scale of the MagLIF experiment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

L0 Data from the 2018 NGEE Arctic LiDAR and Imagery Unoccupied Aerial System Campaign at the Teller 27 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 27 along the Teller road between Nome, Alaska and Teller, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data along 12 flight paths, and DJI Phantom 4 Advanced was used to collect optical red/green/blue (RGB) imagery at regular intervals along 5 flight paths. This data package contains unprocessed data products (processing level 0) including flight paths, raw photos, and raw lidar data files (*.kml, *.jpg, and *.lpd formats). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included (see Supplemental Files, *.rinex, and *.rtcm3 files). NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.- The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

UAS remote sensing (DJI Phantom 4 RTK platform): RGB orthomosaic, digital surface and canopy height models, plant functional type map, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *hdr,), ENVI image file (*.dat), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning

ABSTRACT In this work, we explore the possibility of applying machine learning methods designed for 1D problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly steps, such as the point spread function deconvolution and the training and application of complex Convolutional Neural Networks of thousands or even millions of parameters. In our approach, we extract features from the galaxy images by analysing the elliptical isophotes in their light distribution and collect the information in a sequence. The sequences obtained with this method present definite features allowing a direct distinction between galaxy types. Then, we train and classify the sequences with machine learning algorithms, designed through the platform Modulos AutoML. As a demonstration of this method, we use the second public release of the Dark Energy Survey (DES DR2). We show that we are able to successfully distinguish between early-type and late-type galaxies, for images with signal-to-noise ratio greater than 300. This yields an accuracy of $86{{\ \rm per\ cent}}$ for the early-type galaxies and $93{{\ \rm per\ cent}}$ for the late-type galaxies, which is on par with most contemporary automated image classification approaches. The data dimensionality reduction of our novel method implies a significant lowering in computational cost of classification. In the perspective of future data sets obtained with e.g. Euclid and the Vera Rubin Observatory, this work represents a path towards using a well-tested and widely used platform from industry in efficiently tackling galaxy classification problems at the peta-byte scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Battery Lifecycle Framework

The Battery Lifecycle (BLC) Framework is an open-source platform that provides tools to visualize and share battery data from material characterization, cell testing, manufacturing, and field testing through the technology development cycle. BLC has three components: data importers, a front-end for querying the data and creating visualizations, and an application programming interface to provide access to the data from Python. BLC has been deployed for tracking the development of a battery from the lab to a manufacturing line and systems installed in the field and for comparing studies of multiple cells of the same battery chemistry and configuration. The code was developed around Redash, a robust open-source extract-transform-load engine. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-4546 O

De Angelis, Valerio↗
DOE OSTI2023

UAS remote sensing (3DR SOLO platform): multispectral reflectance, canopy height model, normalized difference vegetation index, Seward Peninsula, Alaska, 2021

Airborne remote sensing data collected using a Parrot Sequoia+ multispectral sensor installed on a 3DR SOLO unoccupied aerial system (UAS) - operated by the Terrestrial Ecosystem Science Technology group at Brookhaven National Laboratory. This package includes data from 10 flights flown over the NGEE-Arctic Council Mile Marker (MM) 71, Kougarok MM64, and Teller MM27 sites on the Seward Peninsula, Alaska, in August 2021. Derived image products include point cloud, ortho-mosaiced multispectral image, ortho-mosaiced RGB image, a digital surface model (DSM) using the structure from motion (SfM) technique, a canopy height model (CHM), and a normalized difference vegetation index (NDVI) map. Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.txt, *.json, *hdr,), tabular (*.dat, *.csv), point cloud (*.laz), Cloud Optimized GeoTIFF (COG, *.tif), and image (*.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

UAS remote sensing (3DR SOLO platform): multispectral reflectance and normalized difference vegetation index, Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using a Parrot Sequoia+ multispectral sensor installed on a 3DR SOLO unoccupied aerial system (UAS) – operated by the Terrestrial Ecosystem Science & Technology group https://www.bnl.gov/envsci/testgroup/ at Brookhaven National Laboratory. This package includes data from 19 flights flown over the NGEE-Arctic, Kougarok Mile Marker (MM) 80, Kougarok Fire Complex (KFC) and Teller MM 27 sites in July 2022. Derived image products include point cloud, ortho-mosaiced multispectral image, a digital surface model (DSM) using the structure from motion (SfM) technique, and a normalized difference vegetation index (NDVI) map. Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.txt, *.json, *hdr,), tabular (*.dat, *.csv), point cloud (*.laz), Cloud Optimized GeoTIFF (COG, *.tif), and image (*.jpg, *.tif, *png) formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Optimization of a Lethal, Combat-Relevant Model of Sterile Inflammation in Mice for Drug Candidate Screening

ABSTRACT Introduction Extensive trauma, commonly seen in wounded military Service Members, often leads to a severe sterile inflammation termed systemic inflammatory response syndrome (SIRS), which can progress to multiple organ dysfunction syndrome (MODS) and death. MODS is a serious threat to wounded Service Members, historically causing 10% of all deaths in trauma admissions at a forward deployed combat hospital. The importance of this problem will be exacerbated in large-scale combat operations, in which evacuation will be delayed and care of complex injuries at lower echelons of care may be prolonged. The main goal of this study was to optimize an existing mouse model of lethal SIRS/MODS as a therapeutic screening platform for the evaluation of immunomodulatory drugs. Materials and Methods Male C57BL/6 mice were euthanized, and the bones and muscles were collected and blended into a paste termed tissue–bone matrix (TBX). The TBX at 12.5%–20% relative to body weight of each recipient mouse was implanted into subcutaneous pouches created on the dorsum of anesthetized animals. Mice were observed for clinical scores for up to 48 hours postimplantation and euthanized at the preset point of moribundity. To test effects of anesthetics on TBX-induced mortality, animals received isoflurane or ketamine/xylazine (K/X). In a separate set of studies, mice received TBX followed by intraperitoneal injection with 20 mg/kg or 40 mg/kg Eritoran or a placebo carrier. All Eritoran studies were performed in a blinded fashion. Results We observed that K/X anesthesia significantly increased the lethality of the implanted TBX in comparison to inhaled anesthetics. Although all the mice anesthetized with isoflurane and implanted with 12.5% TBX survived for 24 hours, 60% of mice anesthetized with K/X were moribund by 24 hours postimplantation. To mimic more closely the timing of lethal SIRS/MODS following polytrauma in human patients, we extended observation to 48 hours. We performed TBX dose–response studies and found that as low as 15%, 17.5%, and 20% TBX caused moribundity/mortality in 50%, 80%, and 100% mice, respectively, over a 48-hour time period. With 17.5% TBX, we tested if moribundity/mortality could be rescued by anti-inflammatory drug Eritoran, a toll-like receptor 4 antagonist. Neither 20 mg/kg nor 40 mg/kg doses of Eritoran were found to be effective in this model. Conclusions We optimized a TBX mouse model of SIRS/MODS for the purpose of evaluating novel therapeutic interventions to prevent trauma-related pathophysiologies in wounded Service Members. Negative effects of K/X on lethality of TBX should be further evaluated, particularly in the light of widespread use of ketamine in treatment of pain. By mimicking muscle crush, bone fracture, and necrosis, the TBX model has pleiotropic effects on physiology and immunology that make it uniquely valuable as a screening tool for the evaluation of novel therapeutics against trauma-induced SIRS/MODS.

General & Internal Medicine↗