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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 109 records · Page 6

Tuning Organic Semiconductor Packing and Morphology through Non-equilibrium Solution Processing (Final Report)

Organic semiconductors (OSCs) are a promising candidate to produce low-cost, large area, and flexible electronics. There has previously been successful development of organic field effect transistors (OFETs), photovoltaics (OPV), and bioelectronics using OSCs. Solution processing of these materials allows for the fabrication of large-area devices in the kinetic crystallization regime. The charge transport capabilities have been shown to depend on the morphology and molecular packing of the OSC within thin films. Our hypothesis is that solution processing conditions may significantly impact OSC morphology and as a result the charge transport. Therefore, this work has focused on obtaining a fundamental understanding of the morphology and molecular packing of OSCs for optimal device performance. Through our work, we have gained a better understanding of how these structural conditions were influenced by the solution processing conditions. Our studies have led to a more systematic understanding of the various parameters that impact OSC morphology. Solution processing leads to non-equilibrium films, thus allowing for the formation of diverse morphologies that are inaccessible by other fabrication methods. Previously, our group has focused on tuning the morphology of small-molecular OSCs [53, 55-57]. However, little work had been devoted to polymer OSCs. There was a lack of detailed studies characterizing the solution-state of polymer OSCs in terms of their conformation, degree of entanglement, polymer aggregation, and chain relaxation dynamics. The solution state properties are also likely to be influenced by the rigidity and molecular weight of the polymer OSCs investigated. Thus, we have focused our attention on gaining a better insight of polymer OSC films and solution processing methods. Through this proposal, our approach is to investigate the correlation between solution-state properties and the morphology of the resulting polymer OSC films. We worked three specific aims: investigate the effects of 1) polymer OSC solution-state properties on final film morphology; 2) molecular additives on the solution-state and final film properties; 3) controlled pre-aggregation in the solution-state in the final film morphology. More rigid and planar polymer backbones should promote interchain charge transport and more efficient interchain hopping between polymer chains. Through tailoring the polymer backbone, polymer sidechains, and molecular weight, we expected the altered solution-state properties to affect the final film morphology. In addition, reducing the entanglements and promoting chain alignment will likely prevent charge carrier trapping through conformation disorders. We thus studied different mechanisms, such as molecular additives, to reduce entanglements in solution. Devices fabricated from these solutions were expected to have improved charge transport abilities. In addition to tailoring the solution-state characteristics of the polymer OSCs, we investigated the effect of solution processing methods on the molecular packing and morphology of polymer OSC films. Two main solution processing techniques, e.g., spin-coating and solution shearing, were employed to fabricate OFETs. Spin coating was employed to prepare OSC films, which produce isotropic films. This technique creates several parameters to tune such as spin coating speed, acceleration, and time. Additionally, our research group developed the solution shearing method, which consists of the solution initially sandwiched between two plates. By sliding the top plate, the solution front is exposed and drying begins. This technique allows for the creation of aligned large crystalline domains. Solution shearing consists of different processing parameters which can affect the final morphology, such as shearing speed, substrate temperature and temperature gradient, distance between both plates, and the tilt angle of the top plate. Due to the complexity of the polymer systems investigated, numerous techniques were employed to characterize the polymer OSC solution state and films. All the materials were characterized using the DOE supported synchrotron X-ray scattering facilities at the Stanford Synchrotron Radiation Lightsource (SSRL). Using grazing incidence X-ray scattering (GIXS) and Near Edge X-ray Absorption Fine Structure (NEXAFS) techniques, the crystalline structure and molecular orientations of the thin films were measured. Optical absorption (UV-Vis) spectroscopy was employed to determine the aggregation state in solution and films of the polymer systems. Polarized UV-Vis also allowed for the determination of the relative degree of polymer chain alignment for solution sheared films. Additionally, various other techniques were used to investigate other properties within the film, such as atomic force microscopy (AFM) and solution rheology. Finally, the device performance is quantified through the fabrication and characterization of OFETs, which will highlight the effects of morphology on charge transport.

36 MATERIALS SCIENCE↗

Optimizing district energy systems under uncertainty: Insights from a case study from Washington D.C., USA

This study investigates solutions for delivering affordable heating and cooling to a brownfield site, focusing on a case study in Washington, DC. Moving towards more diverse and resilient energy systems, we identify the optimal portfolio for a district energy system with diverse energy sources to meet the area’s energy demands. Our methodological approach integrates two detailed models: one calculating building-level energy demand and the other optimizing district energy technology choices based on their demand profiles, accounting for uncertainties in energy prices, policies, and other parameters. The results provide an economic comparison of district and individual supply options at the building level, emphasizing the flexibility district systems can offer to the electricity sector. District energy systems demonstrate cost-stabilization benefits amidst volatile energy prices and external uncertainties. For heating, district systems yield significant cost savings compared to individual solutions, driven by fuel flexibility and the use of local renewable energy sources. For cooling, district systems also show advantages, though individual systems may remain more cost-effective for smaller buildings. Additionally, district systems exhibit considerable flexibility on the heating side, as evidenced by variations in electricity consumption. We recommend future research to explore the relationship between the economics of district energy systems, particularly at the building level, and their flexibility potential for the electricity sector across diverse geographic contexts to reduce overall grid costs and promote grid reliability. This includes areas with distinct zoning laws, municipal priorities, utility structures, and funding mechanisms, such as the United States, and regions like Europe with pronounced electricity price volatility.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Exposure pathways and bioaccumulation of per- and polyfluoroalkyl substances in freshwater aquatic ecosystems: Key considerations

We report that due to the bioaccumulative behavior, toxicity, and recalcitrance to degradation, per- and polyfluoroalkyl substances (PFAS) are a focus for many researchers investigating freshwater aquatic ecosystems. PFAS are a diverse set of chemicals that accumulate and transport quite differently in the environment depending on the length of their fluoroalkyl chains and their functional groups. This diversity in PFAS chemical characteristics combined with varying environmental factors also impact the bioaccumulation of these compounds in different organisms. In this review, we evaluate environmental factors (such as organic carbon, proteins, lipids, and dissolved cations) as well as PFAS characteristics (head group, chain-length, and concentration) that contribute to the significant variation seen in the literature of bioaccumulation metrics reported for organisms in aquatic ecosystems. Of the factors evaluated, it was found that PFAS concentration, dissolved organic matter, sediment organic matter, and biotransformation of precursor PFAS tended to significantly impact reported bioaccumulation metrics the most. Based on this review, it is highly suggested that future studies provide sufficient details of important environmental factors, specific organism traits/ behavior, and PFAS concentrations/compounds when reporting on bioaccumulation metrics to further fill data gaps and improve our understanding of PFAS in aquatic ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between them of 0.50 kcal/mol. The correlation between packages is very good, with the lowest Spearman’s, Pearson’s and Kendall’s tau correlation coefficients being 0.92, 0.91, and 0.76, respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mining for Metal–Organic Systems: Chemistry Frontiers of Th-, U-, and Zr-Materials

The conceptual framework presented in this Perspective overviews the design principles of innovative thorium-based materials that could address urgent needs of the medicinal, nuclear energy, and waste remediation sectors from the lens of zirconium and uranium analogs. We survey the intersections of Zr, Th, and U chemistry with a focus on how the intrinsic behavior of each metal translates to broader material properties, including, but not limited to, structural and topological diversity, preferential metal–ligand binding, and reactivity. On the example of several classes of materials, including organometallic complexes, polyoxometalates, and the primary focus of this Perspective, metal–organic frameworks (MOFs), the design principles that govern the preparation of Zr-, Th-, and U-compounds, including oxophilicity, variation in oxidation states, and stable coordination environments have been considered. Further, we highlight how the impact of the mentioned variables may shift throughout the progression from discrete molecular systems to extended structures. We discuss the common assumption that zirconium-organic materials are typically considered a close analog of thorium-based congeners in areas such as material design and preparation. Through consideration of fundamental chemistry principles, we shed light on the relationships between Zr-, Th-, and U-based materials and highlight how a critical analysis of their distinct properties can be used to target a desired material performance. Finally, we provide a detailed understanding of Th-based materials chemistry by anchoring their fundamental properties between two well-studied reference points, zirconium- and uranium-containing analogs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Uncovering the linear boron environment in Na 3 BP 2 through solid-state 11 B NMR spectroscopy

Boron-based compounds exhibit a wide range of structural diversity, with potential applications spanning organic and inorganic chemistry. Herein, we focus on the characterization of the linear boron-phosphorus unit P═B═P in Na 3 BP 2 using solid-state nuclear magnetic resonance (ssNMR) spectroscopy and density functional theory (DFT) calculations. High-resolution 11 B ssNMR spectra were recorded at two fields, and key parameters such as chemical shift anisotropy (CSA), quadrupolar coupling constants (C Q ), and electric field gradient (EFG) tensors were extracted. The 11 B NMR results revealed a distinct chemical environment for the two-coordinate boron atom, with a CSA span (Ω) of 280 ppm and a C Q of 3.0 MHz. These values were further validated through periodic plane-wave DFT calculations, which showed good agreement with experimental results. The obtained spectral parameters are compared to other linear boron units, such as the BO 2 motif, providing a broader context for understanding boron coordination in inorganic compounds. This work expands the body of NMR knowledge on boron-containing materials, particularly for linear boron motifs. The findings contribute to the growing field of boron chemistry and its potential applications in advanced materials.

Porter, Andrew P. [Ames Laboratory (AMES), Ames, I↗

Scalable Circuit Cutting and Scheduling in a Resource-constrained and Distributed Quantum System

Despite quantum computing's rapid development, current systems remain limited in practical applications due to their limited qubit count and quality. Various technologies, such as superconducting, trapped ions, and neutral atom quantum computing technologies are progressing towards a fault tolerant era, however they all face a diverse set of challenges in scalability and control. Recent efforts have focused on multi-node quantum systems that connect multiple smaller quantum devices to execute larger circuits. Future demonstrations hope to use quantum channels to couple systems, however current demonstrations can leverage classical communication with circuit cutting techniques. This involves cutting large circuits into smaller subcircuits and reconstructing them post-execution. However, existing cutting methods are hindered by lengthy search times as the number of qubits and gates increases. Additionally, they often fail to effectively utilize the resources of various worker configurations in a multi-node system. To address these challenges, we introduce FitCut, a novel approach that transforms quantum circuits into weighted graphs and utilizes a community-based, bottom-up approach to cut circuits according to resource constraints, e.g., qubit counts, on each worker. FitCut also includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms the Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 3.88 times on the worker side, achieving a system-wide improvement of 2.86 times.

Kan, Shuwen [Fordham University]↗

Integrating microbial communities into algal biotechnology: a pathway to enhanced commercialization

Microalgae are increasingly recognized for their potential in wastewater treatment and the sustainable production of feedstock for fuel, feed, food, and other bioproducts. Like conventional agricultural systems, algal cultivation involves complex microbial communities. However, despite their pivotal role in cultivation outcomes, especially at the commodity-scale, the critical interactions between microalgae and their microbiomes are often overlooked. Here we synthesize current knowledge on the taxonomic diversity, ecological roles, and biotechnological potential of algal microbiomes, with a focus on their interactions with algal hosts through nutrient exchange, growth modulation, pathogen defense, and environmental conditioning. We also examine how environmental factors such as nutrient availability, salinity, and temperature influence these interactions. Advances in microbiome engineering, including synthetic biology and ecological approaches, offer opportunities to enhance beneficial algal-microbiome interactions, thereby improving growth, resilience, and yield. These advancements could lead to more sustainable and economically viable microalgae cultivation, with far-reaching implications for environmental management and biotechnological innovation. By addressing key economic and environmental barriers, microbiome engineering holds transformative potential to revolutionize large-scale algae cultivation and provide sustainable solutions to global challenges.

Microbiology↗

Assessment of Potential Future Demands for Hydrogen in the United States

H2@Scale is a U.S. Department of Energy (DOE) initiative that brings together stakeholders to advance the affordable production, transport, storage, and utilization of hydrogen (H2) as an energy carrier to increase revenue opportunities in multiple energy sectors. The focus of the current work is to characterize the growth potential of diverse hydrogen industries in the United States, given research and development (R&D) advancements in hydrogen technologies. Current and emerging hydrogen production technologies utilize diverse energy sources, including natural gas (NG) reforming, as well as renewable and nuclear power for low-temperature and high-temperature water splitting. The produced hydrogen also enables emerging domestic industries that value conventional and renewable hydrogen as an energy carrier for intermediate and end use. The success of H2@Scale (Figure ES.1) depends not only on hydrogen demand from growing existing markets such as petroleum refining and ammonia (NH 3 ) production, but also on the development of new markets such as metals refining, synthetic fuel (synfuel) and chemical production, biofuels, light-duty (LD) and heavy-duty (HD) hydrogen fuel cell (FC) electric vehicles (FCEVs), and injection into NG pipelines, all of which can significantly increase hydrogen demand relative to current levels of approximately10 million metric tons [MMT] annually. This study focused primarily on five demand sectors — synfuels, upgrading of oil/biomass, NH 3 /fertilizer, metals refining, and hydrogen vehicles (transportation) — along with gas infrastructure. For each sector, a hydrogen demand potential was quantified, along with a “threshold price.” The hydrogen demand potential reflects a practical amount of hydrogen that could be used in that sector, barring economic considerations. The threshold price reflects the price at which the consumer would utilize hydrogen in lieu of an alternative that could supply the same performance.

08 HYDROGEN↗

Dynamics of Three-Dimensional Shock-Wave/Boundary-Layer Interactions

Advances in measuring and understanding separated, nominally two-dimensional (2D) shock-wave/turbulent-boundary-layer interactions (STBLI) have triggered recent campaigns focused on three-dimensional (3D) STBLI, which display far greater configuration diversity. Nonetheless, unifying properties emerge for semi-infinite interactions, taking the form of conical asymptotic behavior where shock-generator specifics become insignificant. The contrast between 2D and 3D separation is substantial; the skewed vortical structure of 3D STBLI reflects the essentially 2D influence of the boundary layer on the 3D character of the swept shock. As with 2D STBLI, conical interactions engender prominent spectral content below that of the turbulent boundary layer. However, the uniform separation length scale, which is crucial to normalizing the lowest-frequency dynamics in 2D STBLI, is absent. Comparatively, the spectra of 3D STBLI are more representative of the mid-frequency, convective, shear-layer dynamics in 2D, while phenomena associated with 2D separation-shock breathing are muted. Asymptotic behavior breaks down in many regions important to 3D-STBLI dynamics, occurring in a configuration-dependent manner. Aspects of inceptive regions near shock generators and symmetry planes are reviewed. Focused efforts toward 3D modal and nonmodalanalyses, moving-shock/boundary-layer interactions, fluid/structure interactions, and flow control are suggested as directions for future work.

42 ENGINEERING↗

Utilizing a Virtual Sodium-Cooled Fast Reactor Digital Twin to Aid in Diversion Pathway Analysis for International Safeguards Applications

We report digital twin technology has the potential to improve the effectiveness of international safeguards inspectors by providing a tool which can: first, perform an accurate diversion path analysis, identify their indicators, and required sensors to detect them; and second, monitor facilities in real-time using critical data streams that benefit from this safeguards-by-design approach. Safeguards inspectors are required to visit facilities and verify the nuclear material to ensure no diversion has taken place and detect misuse of the facility; however, this analysis and verification effort is time consuming, and with limited funding it is imperative that time spent at a nuclear facility is focused on key areas. A virtual digital twin of three prototypic sodium fast reactors was developed, where diversion and misuse scenarios were explored to determine how a digital twin could provide inspectors with an understanding of how proliferation may occur and where the most likely areas for proliferation would be. For each of the three reactors, an optimization algorithm was able to find core designs which would be difficult to detect via sensors alone; however, the use of a machine learning adapter provided by the digital twin was able to show general trends in where proliferation as likely to take place.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait , a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Karaoz, Ulas↗

Preface to the Special Issue on Signal Processing, Data Acquisition and Front-end Systems for Radiation Detection and Measurements

This special issue of the Journal of Signal Processing Systems focuses on advancement of signal processing and data-acquisition for radiation detection and measurements. The seven articles included cover diverse topics within the scope of radiation detection ranging from understanding of fundamental fission process, instrumentation, and application of radiation interaction (e.g., tomography and imaging). The articles focus on signal processing for fission spectrometer, neutron and gamma-ray computed tomography and artificial intelligence methods, dynamic neutron imaging system, pulse-pileup correction in x-ray spectroscopy and calibration methods in radiation measurement.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Diversity in the soil virosphere: to infinity and beyond?

Viruses are key members of Earth’s microbiomes, shaping microbial community composition and metabolism. Here, we describe recent advances in 'soil viromics', that is, virus-focused metagenome and metatranscriptome analyses that offer unprecedented windows into the soil virosphere. Given the emerging picture of high soil viral activity, diversity, and dynamics over short spatiotemporal scales, we then outline key eco-evolutionary processes that we hypothesize are the major diversity drivers for soil viruses. We argue that a community effort is needed to establish a 'global soil virosphere atlas' that can be used to address the roles of viruses in soil microbiomes and terrestrial biogeochemical cycles across spatiotemporal scales.

59 BASIC BIOLOGICAL SCIENCES↗

Direct observation of negative differential resistance in WS2 homojunction

Vertical tunneling junctions showing negative differential resistance (NDR) are realized in WS2 homojunction devices. Mono-/multilayered single crystalline WS2 is grown using chemical vapor deposition. NDR is observed through resonant tunneling in Au/bi-layer WS2/Au and Pt/few-layered WS2/Au tunneling junctions by back-gating at room temperature. While two-dimensional materials have been a central focus of materials research during the past decade, exploiting novel properties in diverse layers of these materials is emerging with new designs for electronic devices. Our results pave the way for novel resonant tunneling devices presenting a route to fabricate homojunction WS2 with simple fabrication techniques.

Hsu, Bo (ORCID:0000000201714211)↗

Visualizing Temporal Topic Embeddings with a Compass

—Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

Cluster analysis↗

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Abstract Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux‐based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long‐term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long‐term monitoring programs.

54 ENVIRONMENTAL SCIENCES↗

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗