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DOE OSTI2015

Synthesis, structural characterization, luminescent properties and theoretical study of three novel lanthanide metal-organic frameworks of Ho(III), Gd(III) and Eu(III) with 2,5-thiophenedicarboxylate anion

In this paper, the synthesis of three new metal-organic frameworks of lanthanides (LnMOFs) ([Ln{sub 2}(2,5-tdc){sub 3}(dmso){sub 2}]·H{sub 2}O){sub n} (Ln=Ho (1); Gd (2); Eu (3); 2,5-tdc=2,5-thiophenedicarboxylate anion; dmso=dimethylsulfoxide), and their complete characterization, including single crystal X-ray diffraction, FTIR spectroscopy and thermogravimetric analysis are reported. In especial, photophysical properties of Eu(III) complex have been studied in detail via both theoretical and experimental approaches. Crystal structure of (1) reveals that each lanthanide ion is seven-coordinated by oxygen atoms in an overall distorted capped trigonal – prismatic geometry. The 2,5-tdc{sup 2−} ligands connect four Ln(III) centers, adopting (κ{sup 1}–κ{sup 1})–(κ{sup 1}–κ{sup 1})–μ{sub 4} coordination mode, generating an 8-connected uninodal 3D network. In addition, theoretical studies for Eu(III) complex were performed using the Sparkle model for lanthanide complexes. - Graphical abstract: Three new metal-organic frameworks of lanthanides (LnMOFs) ([Ln{sub 2}(2,5-tdc){sub 3}(dmso){sub 2}]·H{sub 2}O){sub n} (Ln=Ho (1); Gd (2); Eu (3); 2,5-tdc=2,5-thiophenedicarboxylate anion; dmso=dimethylsulfoxide), were synthesized and their complete characterization, including single crystal X-ray diffraction, FTIR spectroscopy and thermogravimetric analysis are reported. In especial, photophysical properties of Eu(III) complex have been studied in detail via both theoretical and experimental approaches. - Highlights: • Three new LnMOFs were synthesized and fully characterized. • Ho{sup 3+}, Gd{sup 3+} and Eu{sup 3+} complexes photoluminescence properties were investigated. • Theoretical approaches for Eu{sup 3+} complex luminescence has been performed. • An energy level diagram is used to establish the ligand-to-metal energy transfer. • These metal−organic frameworks can act as light conversion molecular devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Dinitrogen Binding and Functionalization from a Low-Coordinate Alkynyliron Complex

Alkynyl complexes of low-coordinate transition metals offer a sterically open environment and interesting bonding opportunities. Here, in this work, we explore the capacity of iron(I) alkynyl complexes to bind N 2 and isolate a N 2 complex including its X-ray crystal structure. Silylation of the N 2 complex gives an isolable, formally iron(IV) complex with a disilylhydrazido(2−) ligand, but natural bond orbital analysis indicates that an iron(II) formulation is preferable. The structure of this compound is similar to an earlier reported phenyl complex in which phenyl migration forms a new N–C bond, but the alkynyl group does not migrate. DFT calculations are used to test the possible reasons why the alkynyl is resistant to migration, and these show that the large Fe–C bond energy in the alkynyl complex is a factor that could contribute to the lack of migration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2011

Transcriptome Analysis of Manganese-deficient Chlamydomonas reinhardtii Provides Insight on the Chlorophyll Biosynthesis Pathway

The biosynthesis of chlorophyll and other tetrapyrroles is a vital but poorly understood process. Recent genomic advances with the unicellular green algae Chlamydomonas reinhardtii have created opportunity to more closely examine the mechanisms of the chlorophyll biosynthesis pathway via transcriptome analysis. Manganese is a nutrient of interest for complex reactions because of its multiple stable oxidation states and role in molecular oxygen coordination. C. reinhardtii was cultured in Manganese-deplete Tris-acetate-phosphate (TAP) media for 24 hours and used to create cDNA libraries for sequencing using Illumina TruSeq technology. Transcriptome analysis provided intriguing insight on possible regulatory mechanisms in the pathway. Evidence supports similarities of GTR (Glutamyl-tRNA synthase) to its Chlorella vulgaris homolog in terms of Mn requirements. Data was also suggestive of Mn-related compensatory up-regulation for pathway proteins CHLH1 (Manganese Chelatase), GUN4 (Magnesium chelatase activating protein), and POR1 (Light-dependent protochlorophyllide reductase). Intriguingly, data suggests possible reciprocal expression of oxygen dependent CPX1 (coproporphyrinogen III oxidase) and oxygen independent CPX2. Further analysis using RT-PCR could provide compelling evidence for several novel regulatory mechanisms in the chlorophyll biosynthesis pathway.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Combining Spectroscopic Ellipsometry with Transmission Spectroscopy to Derive Accurate Optical Constants for Organic Materials

A method for deriving the optical constants of organic powdered materials in the mid-infrared spectral range is introduced using variable angle spectroscopic ellipsometry and transmission spectroscopy. The approach uses pressed pellets of the powder and is applied to organic solids, which have both strong and weak infrared absorption features. Many powders have significant voids and do not press into smooth, homogenous pellets. To account for pellet non-idealities and to accurately measure both n and k, three different forms of pellets were pressed and measured: A pure analyte pellet, a mixed analyte/KBr pellet with a large analyte percentage, and a KBr transmission pellet with only a small analyte percentage. Using all three pellets in a multi-sample analysis involving both ellipsometric and transmission data, the complex refractive index (n/k) values can be derived for many organic compounds. This method is illustrated to calculate the optical constants for anhydrous lactose from 6000-400 cm-1. The transmission measurements improve the spectral fitting of weak absorption features, and the multi-sample analysis enables a better determination of the significant void space that is present in the pure pellet, leading to lower values for both n and k if not properly accounted for in the multi-oscillator model used to fit the ellipsometric data.

Optical constants, refractive index, extinction co↗

DOE OSTI2014

Structural Analysis of a Viral Ovarian Tumor Domain Protease from the Crimean-Congo Hemorrhagic Fever Virus in Complex with Covalently Bonded Ubiquitin

Crimean-Congo hemorrhagic fever (CCHF) virus is a tick-borne, negative-sense, single-stranded RNA [ssRNA(-)] nairovirus that produces fever, prostration, and severe hemorrhages in humans. With fatality rates for CCHF ranging up to 70% based on several factors, CCHF is considered a dangerous emerging disease. Originally identified in the former Soviet Union and the Congo, CCHF has rapidly spread across large sections of Europe, Asia, and Africa. Recent reports have identified a viral homologue of the ovarian tumor protease superfamily (vOTU) within its L protein. This protease has subsequently been implicated in downregulation of the type I interferon immune response through cleavage of posttranslational modifying proteins ubiquitin (Ub) and the Ub-like interferon-simulated gene 15 (ISG15). Additionally, homologues of vOTU have been suggested to perform similar roles in the positive-sense, single-stranded RNA [ssRNA(+)] arteriviruses. By utilizing X-ray crystallographic techniques, the structure of vOTU covalently bound to ubiquitin propylamine, a suicide substrate of the enzyme, was elucidated to 1.7 {angstrom}, revealing unique structural elements that define this new subclass of the OTU superfamily. In addition, kinetic studies were carried out with aminomethylcoumarin (AMC) conjugates of monomeric Ub, ISG15, and NEDD8 (neural precursor cell expressed, developmentally downregulated 8) substrates in order to provide quantitative insights into vOTU's preference for Ub and Ub-like substrates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Transcriptome analysis of Aspergillus niger xlnR and xkiA mutants grown on corn Stover and soybean hulls reveals a highly complex regulatory network

BACKGROUND: Enzymatic plant biomass degradation by fungi is a highly complex process and one of the leading challenges in developing a biobased economy. Some industrial fungi (e.g. Aspergillus niger) have a long history of use with respect to plant biomass degradation and for that reason have become 'model' species for this topic. A. niger is a major industrial enzyme producer that has a broad ability to degrade plant based polysaccharides. A. niger wild-type, the (hemi-)cellulolytic regulator (xlnR) and xylulokinase (xkiA1) mutant strains were grown on a monocot (corn stover, CS) and dicot (soybean hulls, SBH) substrate. The xkiA1 mutant is unable to utilize the pentoses D-xylose and L-arabinose and the polysaccharide xylan, and was previously shown to accumulate inducers for the (hemi-)cellulolytic transcriptional activator XlnR and the arabinanolytic transcriptional activator AraR in the presence of pentoses, resulting in overexpression of their target genes. The xlnR mutant has reduced growth on xylan and down-regulation of its target genes. The mutants therefore have a similar phenotype on xylan, but an opposite transcriptional effect. D-xylose and L-arabinose are the most abundant monosaccharides after D-glucose in nearly all plant-derived biomass materials. In this study we evaluated the effect of the xlnR and xkiA1 mutation during growth on two pentose-rich substrates by transcriptome analysis. RESULTS: Particular attention was given to CAZymes, metabolic pathways and transcription factors related to the plant biomass degradation. Genes coding for the main enzymes involved in plant biomass degradation were down-regulated at the beginning of the growth on CS and SBH. However, at a later time point, significant differences were found in the expression profiles of both mutants on CS compared to SBH. CONCLUSION: This study demonstrates the high complexity of the plant biomass degradation process by fungi, by showing that mutant strains with fairly straightforward phenotypes on pure mono- and polysaccharides, have much less clear-cut phenotypes and transcriptomes on crude plant biomass.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Deletion of Type I glutamine synthetase deregulates nitrogen metabolism and increases ethanol production in Clostridium thermocellum

Clostridium thermocellum rapidly deconstructs cellulose and ferments resulting hydrolysis products into ethanol and other products, and is thus a promising platform organism for the development of cellulosic biofuel production via consolidated bioprocessing. And while recent metabolic engineering strategies have targeted eliminating canonical fermentation products (acetate, lactate, formate, and H 2 ), C. thermocellum also secretes amino acids, which has limited ethanol yields in engineered strains to approximately 70% of the theoretical maximum. To decrease amino acid secretion, we attempted to reduce ammonium assimilation by deleting the Type I glutamine synthetase (glnA) in C. thermocellum. Deletion of glnA reduced levels of secreted valine and total amino acids by 53% and 44% respectively, and increased ethanol yields by 53%. RNA-seq analysis revealed that genes encoding the RNF-complex were more highly expressed in ΔglnA and may have a role in improving NADH-availability for ethanol production. While a significant up-regulation of genes involved in nitrogen assimilation and urea uptake suggested that deletion of glnA induces a nitrogen starvation response, metabolomic analysis showed an increase in intracellular glutamine and α-ketoglutarate levels indicative of nitrogen-rich conditions. Here, we propose that deletion of glnA causes deregulation of nitrogen metabolism, leading to overexpression of nitrogen metabolism genes and, in turn, elevated glutamine/α-ketoglutarate levels. Here we demonstrate that perturbation of nitrogen assimilation is a promising strategy to redirect flux from the production of nitrogenous compounds toward biofuels in C. thermocellum.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Self-Taught Waveform Synthesis and Analysis in the Amplify-and-Forward Relay Channel

Wireless communications plays a pivotal role in multiple complex domains such as tactical networks or space communications. Traditional physical (PHY) layer protocols for digital communications contain chains of signal processing blocks that have been mathematically optimized to transmit information bits efficiently over noisy channels. Unfortunately, the ongoing advancement of hardware and software design, and algorithm development, makes it difficult for some domains to keep up with the constant change in modern communication systems. It has been shown previously that combining deep learning with digital modulation (deepmod) allows a system to learn communications on its own rather than requiring human-invented protocols. This is particularly attractive to space communications where updating PHY layer technologies may be prohibitively complex or expensive. A link using deepmod is able to learn both waveform synthesis (transmit) and analysis (receive) that is self-taught. When deepmod is first initiated it has no knowledge of the channel medium but quickly learns to communicate by synthesizing waveforms that can be successfully decoded at the other end of the link. This is accomplished by a custom deep neural network especially suited for this particular task of learning. In this current work, we show that deepmod learns in both traditional point-to-point channels as well as the more abstract multi-hop amplify-and-forward relay channel. In the experimental results, even though no direct link between transmitter and receiver exists, deepmod-enabled nodes still create latent information bearing waveforms that can be used for communications.

Anderson, Adam L.↗

DOE OSTI2025

Batch Test Analysis of Potential Treatment Technologies for Cyanide Groundwater Remediation

In addressing groundwater contamination, several remediation technologies are widely employed, including ion exchange (IX) resins and other adsorbents in pump-and-treat (P&T) facilities. However, many sites do not have the complex nature of the Hanford Site – a legacy production site that is part of the U.S. Department of Energy nuclear weapons complex. The Hanford Site covers an area of nearly 1,500 km 2 and has various inorganic and organic contaminants. One contaminant of concern in the Hanford Site’s Central Plateau is cyanide, particularly in the recalcitrant form of ferrocyanide. While ferrocyanide is often considered a stable complex, it can release free cyanide under certain conditions and presents challenges when testing for cyanide as a total cyanide analysis cannot differentiate between complexed and free cyanide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Bacterial amyloid curli acts as a carrier for DNA to elicit an autoimmune response via TLR2 and TLR9

Bacterial biofilms are associated with numerous human infections. The predominant protein expressed in enteric biofilms is the amyloid curli, which forms highly immunogenic complexes with DNA. Infection with curli-expressing bacteria or systemic exposure to purified curli-DNA complexes triggers autoimmunity via the generation of type I interferons (IFNs) and anti-double-stranded DNA antibodies. Here, we show that DNA complexed with amyloid curli powerfully stimulates Toll-like receptor 9 (TLR9) through a two-step mechanism. First, the cross beta-sheet structure of curli is bound by cell-surface Toll-like receptor 2 (TLR2), enabling internalization of the complex into endosomes. After internalization, the curli-DNA immune complex binds strongly to endosomal TLR9, inducing production of type I IFNs. Analysis of wild-type and TLR2-deficient macrophages showed that TLR2 is the major receptor that drives the internalization of curli-DNA complexes. Suppression of TLR2 internalization via endocytosis inhibitors led to a significant decrease in Ifnβ expression. Confocal microscopy analysis confirmed that the TLR2-bound curli was required for shuttling of DNA to endosomal TLR9. Structural analysis using small-angle X-ray scattering revealed that incorporation of DNA into curli fibrils resulted in the formation of ordered curli-DNA immune complexes. Curli organizes parallel, double-stranded DNA rods at an inter-DNA spacing that matches up well with the steric size of TLR9. We also found that production of anti-double-stranded DNA autoantibodies in response to curli-DNA was attenuated in TLR2- and TLR9-deficient mice and in mice deficient in both TLR2 and TLR9 compared to wild-type mice, suggesting that both innate immune receptors are critical for shaping the autoimmune adaptive immune response. We also detected significantly lower levels of interferon-stimulated gene expression in response to purified curli-DNA in TLR2 and TLR9 deficient mice compared to wild-type mice, confirming that TLR2 and TLR9 are required for the induction of type I IFNs. Finally, we showed that curli-DNA complexes, but not cellulose, were responsible elicitation of the immune responses to bacterial biofilms. This study defines the series of events that lead to the severe pro-autoimmune effects of amyloid-expressing bacteria and suggest a mechanism by which amyloid curli acts as a carrier to break immune tolerance to DNA, leading to the activation of TLR9, production of type I IFNs, and subsequent production of autoantibodies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Effect of Radiotherapy Planning Complexity on Survival of Elderly Patients With Unresected Localized Lung Cancer

Purpose: To evaluate whether complex radiotherapy (RT) planning was associated with improved outcomes in a cohort of elderly patients with unresected Stage I-II non-small-cell lung cancer (NSCLC). Methods and Materials: Using the Surveillance, Epidemiology, and End Results registry linked to Medicare claims, we identified 1998 patients aged >65 years with histologically confirmed, unresected stage I-II NSCLC. Patients were classified into an intermediate or complex RT planning group using Medicare physician codes. To address potential selection bias, we used propensity score modeling. Survival of patients who received intermediate and complex simulation was compared using Cox regression models adjusting for propensity scores and in a stratified and matched analysis according to propensity scores. Results: Overall, 25% of patients received complex RT planning. Complex RT planning was associated with better overall (hazard ratio 0.84; 95% confidence interval, 0.75-0.95) and lung cancer-specific (hazard ratio 0.81; 95% confidence interval, 0.71-0.93) survival after controlling for propensity scores. Similarly, stratified and matched analyses showed better overall and lung cancer-specific survival of patients treated with complex RT planning. Conclusions: The use of complex RT planning is associated with improved survival among elderly patients with unresected Stage I-II NSCLC. These findings should be validated in prospective randomized controlled trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. Yet, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. Here, we present an application of an unsupervised ML method (called NTF k ) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which is important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction A+B→C , where species A and B react to form species C. The reactive-diffusion model input parameters that control mixing include properties of the velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product C. The introduced ML analysis allowed us to identify additive temporal and spatial features that characterize mixing behavior. The application of the proposed NTF k approach is not limited to reactive-mixing. NTFk can be readily applied to any observed or simulated datasets that can be represented as tensors (multi-dimensional arrays) and have separable latent signatures or features.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Calculation of Heating Values for the High Flux Isotope Reactor

Calculating the amount of energy released by a fission reaction (fission Q value) and the heating rate distribution in a nuclear reactor is an important part of the safety analysis. However, these calculations can become very complex. One of the codes that can be used for this type of analyses is the Monte Carlo transport code MCNP5. Currently it is impossible to calculate the Q value and heating rate disposition for delayed beta and delayed gamma particles directly from MCNP5. The purpose of this paper is to outline a rigorous method for indirectly calculating the Q values and heating rates in the High Flux Isotope Reactor (HFIR), based on previous similar studies carried out for very high-temperature reactor configurations. This method has been applied in this study to calculate heating rates for the beginning of cycle (BOC) and end-of-cycle (EOC) states of HFIR. In addition, the BOC results obtained for HFIR are compared with corresponding results for the Advanced Test Reactor. The fission Q value for HFIR was calculated as 200.2 MeV for the BOC and 201.3 MeV for the EOC. It was also determined that 95.1% and 95.4% of the heat was deposited within the HFIR fuel plates for the BOC and EOC models, respectively. This methodology can also be used for heating rate calculations for HFIR experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Structural motifs in heteroleptic copper and cadmium selenites

Cu(II) cations in the presence of diamine ligands such as tetramethylethylendiamine (TMEDA) react with Na2SeO3 to produce the heteroleptic selenite copper compound of formula [Cu-3(TMEDA)(3)(SeO3)(2)(H2O)(4)] (NO3)(2). In the cationic moiety two copper atoms are linked by two selenite anions through the O-Se-O bridge. One of the two bridging selenite anion coordinates to a third copper atom through the third oxygen atom. Reaction of Cd(CH3COO)(2)center dot 2H(2)O with Na2SeO3 in the presence of ligand with softer character such as thiourea [(NH2)(2)CS; tu] or selenourea [(NH2)(2)CSe; tu] in aqueous solution afforded the complex [{Cd(tu)(2)SeO3}(2)], whose structure has been elucidated by X-ray diffraction analysis. It is the first reported example of a molecular cadmium selenite complex. Two cadmium cations stabilized by the coordination of four ancillary thiourea soft ligands, are linked together by two bridging selenite ions through O and O-Se-O bridges. A comparison between the coordination mode of the selenite ion in those cadmium and copper complexes and in inorganic extended CdSeO3 and hydrated CuSeO3 phases is also reported. (C) 2017 Elsevier B.V. All rights reserved.

cadmium↗

DOE OSTI2017

Acceleration of metal–ligand complexation kinetics by electrospray ionization

Electrospray ionization mass spectrometry (ESI-MS) is a valuable and frequently used analytical technique across nearly all branches of chemistry, and has recently seen increasing use in the study of metal–ligand solution equilibria. Despite its prevalence, the approach by which ESI produces gas-phase ions from solutions containing metal–ligand complexes is not fully understood, with recent reports showing significant changes to solution equilibria during ESI analysis. This study examines perturbations to the formation kinetics of metal–ligand complexes during the ESI process, showing how quickly new equilibria – reflective of the ionization process and not solution – can be established. Electrospray ionization mass spectrometry (ESI-MS) and ion mobility spectrometry (IMS) are used to examine the well studied Lu-DOTA (1,4,7,10-tetraazacyclododecane- N , N ', N ", N '"-tetraacetic acid) complexation reaction, with collision cross section modeling based on density functional theory (DFT) optimized structures used to aid in the interpretation of the ion mobility results. The electrospray process was discovered to significantly accelerate the formation kinetics, increasing the formation rate constant by more than an order of magnitude over its previously determined solution-phase value.

Kelley, Morgan P.↗

DOE OSTI2012

Calculation of heating values for the high flux isotope reactor

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

The 200 Gbps Challenge: Imagining HL-LHC analysis facilities

The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a "200 Gbps Challenge", aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes - including software, services, and facilities - built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today's facilities, setting the stage for the next exercise at twice the scale.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗