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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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Antibody Recognition of CD4-Induced Open HIV-1 Env Trimers

Human immunodeficiency virus type 1 (HIV-1) envelope (Env), a heterotrimer of gp120-gp41 subunits, mediates fusion of the viral and host cell membranes after interactions with the host receptor CD4 and a coreceptor. CD4 binding induces rearrangements in Env trimer, resulting in a CD4-induced (CD4i) open Env conformation. Structural studies of antibodies isolated from infected donors have defined antibody-Env interactions, with one class of antibodies specifically recognizing the CD4i open Env conformation. In this study, we characterized a group of monoclonal antibodies isolated from HIV-1 infected donors (V2i MAbs) that displayed characteristics of CD4i antibodies. Binding experiments demonstrated that the V2i MAbs preferentially recognize CD4-bound open Env trimers. Structural characterizations of V2i MAb-Env-CD4 trimer complexes using single-particle cryo-electron microscopy showed recognition by V2i MAbs using different angles of approach to the gp120 V1V2 domain and the β2/β3 strands on a CD4i open conformation Env with no direct interactions of the MAbs with CD4. We also characterized CG10, a CD4i antibody that was raised in mice immunized with a gp120-CD4 complex, bound to an Env trimer plus CD4. CG10 exhibited characteristics similar to those of the V2i antibodies, i.e., recognition of the open Env conformation, but showed direct contacts to both CD4 and gp120. Structural comparisons of these and previously characterized CD4i antibody interactions with Env provide a suggested mechanism for how these antibodies are elicited during HIV-1 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Recognition Patterns of the C1/C2 Epitopes Involved in Fc-Mediated Response in HIV-1 Natural Infection and the RV114 Vaccine Trial

Antibodies (Abs) specific for CD4-induced envelope (Env) epitopes within constant region 1 and 2 (C1/C2) were induced in the RV144 vaccine trial, where antibody-dependent cellular cytotoxicity (ADCC) correlated with reduced risk of HIV-1 infection. We combined X-ray crystallography and fluorescence resonance energy transfer-fluorescence correlation spectroscopy to describe the molecular basis for epitopes of seven RV144 Abs and compared them to A32 and C11, C1/C2 Abs induced in HIV infection. Our data indicate that most vaccine Abs recognize the 7-stranded β-sandwich of gp120, a unique hybrid epitope bridging A32 and C11 binding sites. Although primarily directed at the 7-stranded β-sandwich, some accommodate the gp120 N terminus in C11-bound 8-stranded conformation and therefore recognize a broader range of CD4-triggered Env conformations. Our data also suggest that Abs of RV144 and RV305, the RV144 follow-up study, although likely initially induced by the ALVAC-HIV prime encoding full-length gp120, matured through boosting with truncated AIDSVAX gp120 variants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Botulinum Neurotoxin-Producing Bacteria. Isn’t It Time that We Called a Species a Species?

Botulinum neurotoxins (BoNTs) are produced by a diverse set of seven clostridial species, though alternate naming systems have developed over the last 100 years. Starting in the 1950s, a single-species taxonomy where any bacterium producing BoNT would be designated Clostridium botulinum was introduced. As the extreme diversity of these strains was recognized, a secondary system of taxonomic “groups” evolved. It became clear that these groups also had members that did not produce BoNT, and in some cases, they were given formal species names. Genomic analysis now clearly identifies species affiliations whether an isolate is toxigenic or not. It is clear that C. botulinum group nomenclature is no longer appropriate and that there are recognized species names for each clostridium. We advocate for the use of the scientific binomials and that the single-species group nomenclature be abandoned.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Hadrons and nuclei

In 2018, the USQCD Collaboration’s Executive Committee organized several subcommittees to recognize future opportunities and formulate possible goals for lattice field theory calculations in several physics areas. The conclusions of these studies, along with community input, are presented in seven white papers. Here, we discuss opportunities for lattice QCD calculations related to the structure and spectroscopy of hadrons and nuclei. An overview of recent lattice calculations of the structure of the proton and other hadrons is presented along with prospects for future extensions. Progress and prospects of hadronic spectroscopy and the study of resonances in the light, strange and heavy quark sectors are summarized. In conclusion recent advances in the study of light nuclei from lattice QCD are addressed and the scope of future investigations that are currently envisioned is outlined.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Editors’ Choice—Necessity to Avoid Titanium Oxide as Electrocatalyst Support in PEM Fuel Cells: A Membrane Durability Study

Pt/TiO 2 as electrocatalyst was found to significantly increase the rate of the membrane chemical degradation in a polymer electrolyte membrane (PEM) fuel cell. The increased degradation was proved to be due to TiO x (TiO 2 or Ti 4 O 7 ), which is widely recognized as a promising corrosion-resistant electrocatalyst support. The membrane degradation (thinning) appears to be preferentially in the side facing anode electrode. Migrated Ti species as a result of TiO x dissolution during fuel cell operation in an acidic environment was quantified by electron probe microanalysis (EPMA). Total fluorine inventory loss of the membrane strongly depends on the quantity of migrated Ti species (likely Ti 3+ and/or Ti 4+ ). The membrane chemical degradation induced by TiO x is proposed to be due to dissolved ionic Ti species reacting with H 2 O 2 through a Fenton reaction. TiO x is determined to be inviable as electrocatalyst support for PEM fuel cells, and future research is suggested to be directed toward alternative oxides with similar corrosion-resistance.

08 HYDROGEN↗
DOE OSTI2018

Altered Binding of Tumor Antigenic Peptides to MHC Class I Affects CD8 + T Cell–Effector Responses

T-cell priming occurs when a naïve T cell recognizes cognate peptide–MHC complexes on an activated antigen-presenting cell. The circumstances of this initial priming have ramifications on the fate of the newly primed T cell. Newly primed CD8 + T cells can embark onto different trajectories, with some becoming short-lived effector cells and others adopting a tissue resident or memory cell fate. To determine whether T-cell priming influences the quality of the effector T-cell response to tumors, here we used transnuclear CD8 + T cells that recognize the melanoma antigen TRP1 using TRP1 high or TRP1 low TCRs that differ in both affinity and fine specificity. From a series of altered peptide ligands, we identified a point mutation (K8) in a nonanchor residue that, when analyzed crystallographically and biophysically, destabilized the peptide interaction with the MHC binding groove. In vitro, the K8 peptide induced robust proliferation of both TRP1 high and TRP1 low CD8 + T cells but did not induce expression of PD-1. Cytokine production from K8-stimulated TRP1 cells was minimal, whereas cytotoxicity was increased. Upon transfer into B16 tumor–bearing mice, the reference peptide (TRP1-M9)- and K8-stimulated TRP1 cells were equally effective at controlling tumor growth but accomplished this through different mechanisms. TRP1-M9–stimulated cells produced more IFNγ, whereas K8-stimulated cells accumulated to higher numbers and were more cytotoxic. We, therefore, conclude that TCR recognition of weakly binding peptides during priming can skew the effector function of tumor-specific CD8 + T cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Trivalent mosaic or consensus HIV immunogens prime humoral and broader cellular immune responses in adults

Mosaic and consensus HIV-1 immunogens provide two distinct approaches to elicit greater breadth of coverage against globally circulating HIV-1 and have shown improved immunologic breadth in nonhuman primate models. This double-blind randomized trial enrolled 105 healthy HIV-uninfected adults who received 3 doses of either a trivalent global mosaic, a group M consensus (CON-S), or a natural clade B (Nat-B) gp160 env DNA vaccine followed by 2 doses of a heterologous modified vaccinia Ankara–vectored HIV-1 vaccine or placebo. We performed prespecified blinded immunogenicity analyses at day 70 and day 238 after the first immunization. T cell responses to vaccine antigens and 5 heterologous Env variants were fully mapped. Env-specific CD4 + T cell responses were induced in 71% of the mosaic vaccine recipients versus 48% of the CON-S recipients and 48% of the natural Env recipients. The mean number of T cell epitopes recognized was 2.5 (95% CI, 1.2–4.2) for mosaic recipients, 1.6 (95% CI, 0.82–2.6) for CON-S recipients, and 1.1 (95% CI, 0.62–1.71) for Nat-B recipients. Mean breadth was significantly greater in the mosaic group than in the Nat-B group using overall (P = 0.014), prime-matched (P = 0.002), heterologous (P = 0.046), and boost-matched (P = 0.009) measures. Overall T cell breadth was largely due to Env-specific CD4 + T cell responses. Priming with a mosaic antigen significantly increased the number of epitopes recognized by Env-specific T cells and enabled more, albeit still limited, cross-recognition of heterologous variants. Mosaic and consensus immunogens are promising approaches to address global diversity of HIV-1.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Limited Role of Absolute Humidity in Intraurban Heat Variability

Abstract Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks. Significance Statement Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

Meteorology & Atmospheric Sciences↗
DOE OSTI2026

Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

AI/ML↗
DOE OSTI2026

Data for Resourceful and Economical Designing of Fermentation Medium for Lab and Commercial Strains of Yeast from Alternative Feedstock: Transgenic Oilcane

Sugarcane plant engineered to accumulate lipids in its vegetative tissue is being developed as a new bioenergy crop. The new crop would be a source of juice, oil, and cellulosic sugars. However, limited tolerance of industrially recognized yeasts towards inhibitors generated during the processing of lignocellulosic biomass to produce fermentable sugars is a major challenge in developing scalable processes for second-generation drop-in fuel production. To this end, hydrolysates generated from engineered sugarcane—‘oilcane’ bagasse contain added phenolics and fatty acids that further restrict the growth of fermenting microorganisms and necessitate nutrient supplementation and/or detoxification of hydrolysate which makes the fermentation process expensive. Herein, we propose a resourceful and economical approach for growing lab and commercial strains of S. cerevisiae on unrefined cellulosic sugars aerobically and fermentatively. An equal ratio of hydrolysate and juice was found optimum for growth and fermentation by lab and commercial strains of Saccharomyces cerevisiae engineered for xylose fermentation. The industrial strain grew and fermented efficiently under low aeration conditions having an ethanol titer, yield, specific and volumetric productivities of 46.96 ± 0.19 g/l, 0.51 ± 0.00 g/g, 0.27 ± 0.02 g/g.h and 1.95 ± 0.01 g/l.h, respectively, while the lab strain grew better under higher aeration conditions having the ethanol titer, yield, specific and volumetric productivities of 24.93 ± 0.09, 0.27 ± 0.00 g/g, 0.17 ± 0.00 g/g.h and 1.04 ± 0.00 g/l.h, respectively. Acclimation of cultures in a blended medium significantly improved the performance of the yeast strains. The addition of transgenic oilcane juice, which is inedible and rich in amino acids, to the hydrolysate averted the need for expensive nutrient supplementation and detoxification steps of hydrolysate. The approach provides an economical solution to reduce the cost of fermentation at an industrial scale for second-generation drop-in fuel production.

Biomass Analytics↗
DOE OSTI2023

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A polypeptide model for toxic aberrant proteins induced by aminoglycoside antibiotics

Aminoglycoside antibiotics interfere with the selection of cognate tRNAs during translation, resulting in the synthesis of aberrant proteins that are the ultimate cause of cell death. However, the toxic potential of aberrant proteins and how they avoid degradation by the cell’s protein quality control (QC) machinery are not understood. Here we report that levels of the heat shock (HS) transcription factor σ32 increased sharply following exposure of Escherichia coli to the aminoglycoside kanamycin (Kan), suggesting that at least some of the aberrant proteins synthesized in these cells were recognized as substrates by DnaK, a molecular chaperone that regulates the HS response, the major protein QC pathway in bacteria. To further investigate aberrant protein toxic potential and interaction with cell QC factors, we studied an acutely toxic 48-residue polypeptide (ARF48) that is encoded by an alternate reading frame in a plant cDNA. As occurred in cells exposed to Kan, σ32 levels were strongly elevated following ARF48 expression, suggesting that ARF48 was recognized as a substrate by DnaK. Paradoxically, an internal 10-residue region that was tightly bound by DnaK in vitro also was required for the ARF48 toxic effect. Despite the increased levels of σ32, levels of several HS proteins were unchanged following ARF48 expression, suggesting that the HS response had been aborted. Nucleoids were condensed and cell permeability increased rapidly following ARF48 expression, together suggesting that ARF48 disrupts DNA-membrane interactions that could be required for efficient gene expression. Our results are consistent with earlier studies showing that aberrant proteins induced by aminoglycoside antibiotics disrupt cell membrane integrity. Insights into the mechanism for this effect could be gained by further study of the ARF48 model system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The structural basis of mRNA recognition and binding by yeast pseudouridine synthase PUS1

The chemical modification of RNA bases represents a ubiquitous activity that spans all domains of life. Pseudouridylation is the most common RNA modification and is observed within tRNA, rRNA, ncRNA and mRNAs. Pseudouridine synthase or ‘PUS’ enzymes include those that rely on guide RNA molecules and others that function as ‘stand-alone’ enzymes. Among the latter, several have been shown to modify mRNA transcripts. Although recent studies have defined the structural requirements for RNA to act as a PUS target, the mechanisms by which PUS1 recognizes these target sequences in mRNA are not well understood. Here we describe the crystal structure of yeast PUS1 bound to an RNA target that we identified as being a hot spot for PUS1-interaction within a model mRNA at 2.4 Å resolution. The enzyme recognizes and binds both strands in a helical RNA duplex, and thus guides the RNA containing the target uridine to the active site for subsequent modification of the transcript. The study also allows us to show the divergence of related PUS1 enzymes and their corresponding RNA target specificities, and to speculate on the basis by which PUS1 binds and modifies mRNA or tRNA substrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Mapping the immunogenic landscape of near-native HIV-1 envelope trimers in non-human primates

The induction of broad and potent immunity by vaccines is the key focus of research efforts aimed at protecting against HIV-1 infection. Soluble native-like HIV-1 envelope glycoproteins have shown promise as vaccine candidates as they can induce potent autologous neutralizing responses in rabbits and non-human primates. In this study, monoclonal antibodies were isolated and characterized from rhesus macaques immunized with the BG505 SOSIP.664 trimer to better understand vaccine-induced antibody responses. Our studies reveal a diverse landscape of antibodies recognizing immunodominant strain-specific epitopes and non-neutralizing neo-epitopes. Additionally, we isolated a subset of mAbs against an epitope cluster at the gp120-gp41 interface that recognize the highly conserved fusion peptide and the glycan at position 88 and have characteristics akin to several human-derived broadly neutralizing antibodies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Probing the structure and function of the protease domain of botulinum neurotoxins using single-domain antibodies

Botulinum neurotoxins (BoNTs) are among the deadliest of bacterial toxins. BoNT serotype A and B in particular pose the most serious threat to humans because of their high potency and persistence. To date, there is no effective treatment for late post-exposure therapy of botulism patients. Here, we aim to develop single-domain variable heavy-chain (VHH) antibodies targeting the protease domains (also known as the light chain, LC) of BoNT/A and BoNT/B as antidotes for post-intoxication treatments. Using a combination of X-ray crystallography and biochemical assays, we investigated the structures and inhibition mechanisms of a dozen unique VHHs that recognize four and three non-overlapping epitopes on the LC of BoNT/A and BoNT/B, respectively. We show that the VHHs that inhibit the LC activity occupy the extended substrate-recognition exosites or the cleavage pocket of LC/A or LC/B and thus block substrate binding. Notably, we identified several VHHs that recognize highly conserved epitopes across BoNT/A or BoNT/B subtypes, suggesting that these VHHs exhibit broad subtype efficacy. Further, we identify two novel conformations of the full-length LC/A, that could aid future development of inhibitors against BoNT/A. Our studies lay the foundation for structure-based engineering of protein- or peptide-based BoNT inhibitors with enhanced potencies and cross-subtypes properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

MiNAA: Microbiome Network Alignment Algorithm

A microbial network is a mathematical representation of a microbial community where nodes represent microbes and edges represent interactions. It is well-recognized that microbes are among the main drivers of biological phenotypes in soil, plants, and animals alike, and thus, their study has vast implications for soil, plant and human health. In particular, recognizing the microbial, environmental, and agricultural factors that drive plant and soil phenotypes is crucial to comprehend processes connected to plant and soil health, to identify global practices of sustainable agriculture, as well as to predict plant and soil responses to environmental perturbations such as climate change. The adaptability of microbes to thrive in every environment poses challenges for scientists who try to understand their communities. Indeed, two microbial communities with the exact same players can interact differently depending on the environmental conditions. It is thus desirable to identify commonalities and differences on two microbial networks, hence the need for computational tools that can match (or align) them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗