Exploring Electric Sector Evolution and Cutting-Edge Reliability Technologies: Cooperative Power
Exploring Electric Sector Evolution and Cutting-Edge Reliability Technologies: Cooperative Power
24 - POWER TRANSMISSION AND DISTRIBUTION↗
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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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Innovative Interactive 3D Slice Visualization Tool: Bridging Accessibility, Insight, and Immersive Analysis for Complex Data Exploration
Innovative Interactive 3D Slice Visualization Tool: Bridging Accessibility, Insight, and Immersive Analysis for Complex Data Exploration
36 - MATERIALS SCIENCE↗
SEAFORML (Smart Exploration and Analysis For Optimal and Robust Machine Learning)
The poster discusses data analysis of the WAVgraph database and applied machine learning methods for it. The database is a long-term project that seeks to be a comprehensive repository of information on cyber threats and is updated regularly. It was previously unanalyzed and unexplored. The goal was to learn more about it and its contents in order to have a better understanding and enable better use. The data analysis and discovery enabled further exploration through natural language processing, similarity, and clustering methods. The poster shows some of the insights from the analysis and explains the methods used for the machine learning applications.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Exploration of Real Time Inference for MI-RR Deblending on GPU/TPU Systems
The Fermilab Main Injector (MI) and Recycler Ring (RR) share a common beam loss monitor (BLM) system, making loss events difficult to attribute to their source machine when beam is present in both simultaneously. The Real-time Edge AI for Distributed Systems (READS) project addresses this by deblending BLM readings in real time using machine learning (ML). The current FPGA based implementation meets the sub-3 ms latency requirement but carries a resource intensive hls4ml development cycle, motivating exploration of GPU based deployment. This paper characterizes inference latency on an NVIDIA Jetson Orin Nano and introduces a packet organization scheme for assembling synchronized event frames from seven distributed BLM DAQ streams. Using a Python based DAQ simulation with injected timing jitter in place of unavailable live beam data, the pipeline achieved an average end to end latency of 0.456 ms (σ = 0.122 ms) across 167,000 test frames, comfortably meeting the timing constraint. Early outliers were attributed to TensorRT warm-up rather than steady state limitations, suggesting GPU based inference is a viable alternative to the existing FPGA implementation.
Yu, Kellen [Fermilab; Cornell U.]↗
Machine-learning-based efficient parameter space exploration for energy storage systems
Not Available
Bayesian optimization↗
Exploring the Mineral Composition, Structure, and Function of a Freshwater Bivalve Adhesive
Abstract Etheria elliptica is a freshwater oyster that produces an adhesive that hardens underwater, allowing them to attach to other oysters, rocks, wood, and other natural substrates, producing complex oyster bed communities. Despite the importance of the adhesive in oyster bed formation, little is known about its chemical composition, structure, or mechanical properties. Through a combination of scanning electron microscopy (SEM), x‐ray photoemission electron microscopy (X‐PEEM), infrared (IR) spectroscopy, microhardness testing, and nanoindentation, it is found that the oyster adhesive consists entirely of aragonite nanoparticles that clump together into crystallites of varied shape, size, and orientation. The crystallite clumping density varies from the exterior of the adhesive to the interior, with the adhesive closest to the prismatic layer generally being denser. With this variation in density comes a variation in material properties with the exterior adhesive layer being softer and more flexible and the interior adhesive layer being harder and less elastic, or more similar to the prismatic and nacre shell structures produced by the E. elliptica . The resultant material exhibits varied materials properties, despite similar mineral composition.
Chemistry↗
In Situ Exploration of Dipole Field Effects on Weak Hysteresis in 3D/2D Perovskites
This research delves into the effects of 2D layers on the functionality of 3D perovskite using lock-in amplifier-based in situ surface photovoltage (SPV) and its phase spectroscopy, with an emphasis on elucidating the connection between the tuning of dipole moments and the photocurrent hysteresis. Conventionally, the SPV of a perovskite/hole transport layer is observed to diminish as positive bias escalates. However, this trend is reversed in the case of 3D perovskite samples, where an augmentation in SPV is noted under positive bias. Notably, 3D/2D perovskite structures initially show a decrease, then an increase in SPV as bias intensifies, a phenomenon more pronounced with larger dipole moments in 2D. However, there is no linear relationship between the dipole moment and the hysteresis factor. Furthermore, using in situ light-chopping-frequency-modulated SPV and Kelvin Probe Force Microscopy, it is revealed that the dipole fields of 2D layers can hinder ion migration. This leads to efficient hole transfer and minimal photocurrent hysteresis in 3D/2D perovskites, providing strong evidence for the underlying cause of hysteresis. Additionally, these findings suggest intricate interplays among the external electric field, interface dipole moments, and surface photovoltaics, offering significant insights into perovskite optoelectronics.
3D/2D perovskite↗
Exploring the Impact of Active Site Structure on the Conversion of Methane to Methanol in Cu‐Exchanged Zeolites
Abstract In the past, Cu‐oxo or ‐hydroxy clusters hosted in zeolites have been suggested to enable the selective conversion of methane to methanol, but the impact of the active site's stoichiometry and structure on methanol production is still poorly understood. Herein, we apply theoretical modeling in conjunction with experiments to study the impact of these two factors on partial methane oxidation in the Cu‐exchanged zeolite SSZ‐13. Phase diagrams developed from first‐principles suggest that Cu‐hydroxy or Cu‐oxo dimers are stabilized when O 2 or N 2 O are used to activate the catalyst, respectively. We confirm these predictions experimentally and determine that in a stepwise conversion process, Cu‐oxo dimers can convert twice as much methane to methanol compared to Cu‐hydroxyl dimers. Our theoretical models rationalize how Cu‐di‐oxo dimers can convert up to two methane molecules to methanol, while Cu‐di‐hydroxyl dimers can convert only one methane molecule to methanol per catalytic cycle. These findings imply that in Cu clusters, at least one oxo group or two hydroxyl groups are needed to convert one methane molecule to methanol per cycle. This simple structure–activity relationship allows to intuitively understand the potential of small oxygenated or hydroxylated transition metal clusters to convert methane to methanol.
Chemistry↗
Should I stay or should I flow? An exploration of phase‐separated metallosupramolecular liquid crystal polymers
Abstract Dynamic liquid crystalline polymers (dLCPs) incorporate both liquid crystalline mesogens and dynamic bonds into a single polymeric material. These dual functionalities impart order‐dependent thermo‐responsive mechano‐optical properties and enhanced reprocessability/programmability enabling their use as soft actuators, adaptive adhesives, and damping materials. While many previous works studying dynamic LCPs utilize dynamic covalent bonds, metallosupramolecular bonds provide a modular platform where a series of materials can be accessed from a single polymeric feedstock through the variation of the metal ion used. A series of dLCPs were prepared by the addition of metal salts to a telechelic 2,6‐bisbenzimidazolylpyridine (Bip) ligand endcapped LCP to form metallosupramolecular liquid crystal polymers (MSLCPs). The resulting MSLCPs were found to phase separate into hard and soft phases which aids in their mechanical robustness. Variations of the metal salts used to access these materials allowed for control of the thermomechanical, viscoelastic, and adhesive properties with relaxations that can be tailored independently of the mesogenic transition. This work demonstrates that by accessing phase separation through the incorporation of metallosupramolecular moieties, highly processable yet robust MSLCP materials can be realized. This class of materials opens the door to LCPs with bulk flow behavior that can also be utilized as multi‐level adhesives.
Chemistry↗
Accelerating the Structure Exploration of Diverse Bi–Pt Nanoclusters via Physics‐Informed Machine Learning Potential and Particle Swarm Optimization
Bimetallic Bi–Pt nanoclusters exhibit diverse structural motifs, including core-shell, Janus, and mixed alloy configurations, due to the unique bonding characteristics between Bi and Pt atoms. Using density functional theory refinements from ChIMES physically machine-learned potential and CALYPSO particle swarm optimization global searches, 34 Bi20-Pt20 nanoclusters are systematically classified. The results reveal that Bi atoms predominantly occupy surface sites, driven by charge transfer effects. Cohesive energy trends alone prove insufficient for structure differentiation, necessitating a data-driven approach employing principal component analysis and K-means clustering. Furthermore, vibrational, electronic, and infrared spectral analyses provide additional insights into structure-property relationships. The findings offer an original framework for the automated classification and analysis of bimetallic nanoclusters, enhancing the understanding of their stability and functional properties.
bimetallic nanoparticles↗
Ab initio exploration of low–lying electronic states of linear and bent MNX + (M = Ca, Sr, Ba, Ra; X = O, S, Se, Te, Po) and their origins
High-level multireference and coupled cluster quantum calculations were employed to analyze low-lying electronic states of linear-MNX + and side-bonded-M[NX] + (M = Ca, Sr, Ba, Ra; X = O, S, Se, Te, Po) species. Their full potential energy curves (PECs), dissociation energies (D e s), geometric parameters, excitation energies (T e s), and harmonic vibrational frequencies (ω e s) are reported. The first three chemically bound electronic states of MNX + and M[NX] + are 3 Σ – , 1 Δ, 1 Σ + and 3 A", 1 A', 1 A", respectively. The 3 Σ – , 1 Δ, 1 Σ + of MNX + originate from the M + ( 2 D) + NX( 2 Π) fragments, whereas the 3 A", 1 A', 1A" states of M[NX] + dissociate to M + ( 2 S) + NX( 2 Π) as a result of avoided crossings. The MNX + and M[NX] + are real minima on the potential energy surface and their interconversions are possible. The M 2+ NX – /M 2+ [NX] – ionic structure is an accurate representation for their low-lying electronic states. The D e s of MNX + species were found to depend on the dipole moment (μ) of the corresponding NX ligands and a linear relationship between these two parameters was observed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Exploring the Photogenerated Charge Transfer Mechanism in Cu 2 O@MoS 2 Heterojunction Photocatalyst Using Transient Absorption Spectroscopy
Both heterojunction and core–shell photocatalysts have demonstrated promising performance in photocatalytic CO 2 conversions to fuels. However, fundamental knowledge of heterojunctions in core–shell structures is highly desired to facilitate the design of future photocatalysts. By combining advanced experimental characterizations and density functional theory (DFT) calculations, the role of the Cu 2 O@MoS 2 heterojunction in photocatalytic CO 2 conversions to fuels was investigated. We discovered that the charge dynamics and electron transfer properties of Cu2O@MoS 2 photocatalysts are altered by the heterojunction and Cu 2 O underlayer due to the electron transfer from Cu 2 O to MoS 2 and the change in CO 2 adsorption strength on the hybrid catalyst surface. Consequently, more electrons can travel to the surrounding liquid environment to be consumed by CO 2 reduction. This study provides experimental and theoretical investigations of the fundamental mechanisms of heterojunction core–shell photocatalysts.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework
Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine-learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum.
59 BASIC BIOLOGICAL SCIENCES↗
Exploring small-angle emissions in charm quark jets in proton-proton collisions at $\sqrt{s}=5.02$ TeV
A measurement of the angular structure of inclusive jets and those containing a prompt D 0 meson in proton-proton collisions at the LHC at a center-of-mass energy of 5.02 TeV is presented. The data corresponding to an integrated luminosity of 301 pb −1 were collected by the CMS experiment in 2017. Two jet grooming algorithms, late-k T and soft drop, are used to study the intrajet radiation pattern using iterative Cambridge-Aachen declustering. The splitting-angle distributions of jets with transverse momentum (p T ) of around 100 GeV, obtained with these two algorithms, show that there is a shift of the distribution for jets containing a prompt D 0 meson with respect to inclusive jets. The suppression of emissions at small angles observed in the late-k T grooming approach is consistent with the dead-cone effect, whereas the similar suppression for splittings selected with the soft-drop algorithm appears to be induced by gluon splitting to charm quark-antiquark pairs at large angles. The measured distributions are corrected to the particle level and can be used to constrain model predictions for the substructure of high-p T charm quark jets.
Charm Physics↗
Exploring the holographic entropy cone via reinforcement learning
We develop a reinforcement learning algorithm to study the holographic entropy cone. Given a target entropy vector, our algorithm searches for a graph realization whose min-cut entropies match the target vector. If the target vector does not admit such a graph realization, it must lie outside the cone, in which case the algorithm finds a graph whose corresponding entropy vector most nearly approximates the target and allows us to probe the location of the facets. For the N = 3 cone, we confirm that our algorithm successfully rediscovers monogamy of mutual information beginning with a target vector outside the holographic entropy cone. We then apply the algorithm to the N = 6 cone, analyzing the 6 mystery extreme rays of the subadditivity cone from [1] that satisfy all known holographic entropy inequalities yet lacked graph realizations. We found realizations for 3 of them, proving they are genuine extreme rays of the holographic entropy cone, while providing evidence that the remaining 3 are not realizable, implying unknown holographic inequalities exist for N = 6.
AdS-CFT correspondence↗
Exploring the role of judgement and shared situation awareness when working with AI recommender systems
Abstract AI-advised Decision Making is a form of human-autonomy teaming in which an AI recommender system suggests a solution to a human operator, who is responsible for the final decision. This work seeks to examine the importance of judgement and shared situation awareness between humans and automated agents when interacting together in the form of a recommender systems. We propose manipulating both human judgement and shared situation awareness by providing the human decision maker with relevant information that the automated agent (AI), in the form of a recommender system, uses to generate possible courses of action. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of relevant information the participant had, the assessment technique of the proposed solution, and the reliability of the recommender system. Findings indicate that this technique of supporting the human’s judgement and establishing a shared situation awareness is effective in (1) boosting the human decision maker’s situation awareness and task performance, (2) calibrating their trust in AI teammates, and (3) reducing overreliance on an AI partner. Additionally, participants were able to pinpoint the limitations and boundaries of the AI partner’s capabilities. They were able to discern situations where the AI’s recommendations could be trusted versus instances when they should not rely on the AI’s advice. This work proposes and validates a way to provide model-agnostic transparency into recommender systems that can support the human decision maker and lead to improved team performance.
Srivastava, Divya↗
Exploring Fission–Fusion Synergies to Accelerate Compatibility Understanding
To address the significant commercial interest in fusion energy, it will be necessary to accelerate the compatibility research associated with liquid breeders including Li, eutectic Pb–Li and LiF-BeF 2 (FLiBe) molten salt. Particularly for FLiBe, compatibility understanding is limited especially for fusion relevant materials such as reduced activation ferritic-martensitic steels, SiC and V alloys. The historical knowledge associated with molten salt reactors (MSRs) and recent work to commercialize MSRs can benefit fusion research. Recent experimental and modeling work has improved understanding and this knowledge can be applied to fusion relevant materials. For liquid metals (LMs), the comparisons to Li and Pb–Li are less direct but nevertheless can help guide the pathway toward commercialization. For Pb–Li, Al-rich coatings have been shown to inhibit dissolution and potentially increase operating temperatures. For commercialization, the experience with sensors and on-line cleanup can help guide future developments. Thus, it is worth considering the potential for fission-related research with LMs and molten salts to help accelerate fusion research.
36 MATERIALS SCIENCE↗
Exploring urban typologies using comprehensive analysis of transportation dynamics
Abstract As urban areas continue to expand and develop, categorizing cities into typologies offers a valuable framework for understanding metropolitan dynamics and fostering inter-city collaboration. However, existing typologies related to urban mobility have limitations, failing to consider cities within a single large urban region and often overlooking crucial dimensions such as trip demand and traffic flow. In this paper, we introduce a transportation-focused characterization for cities within a large urban region, specifically the San Francisco Bay Area, California. We incorporate over 40 metrics across five transportation dimensions: trip demand, road network, multi-modal network, traffic flow, and land use. Specifically, for the trip demand dimension, we include metrics capturing residents’ trip characteristics, such as mode share, intra-city trips, and inter-city trips. Additionally, we analyze the purpose of trips entering the city to gain a deeper understanding of incoming trip patterns. In the traffic flow dimension, we examine metrics like vehicle miles traveled, delay, and congestion to assess the traffic conditions on the street network. These, combined with other dimensions, provide a comprehensive view of a city’s transportation dynamics. Using unsupervised machine learning clustering methods, we identified eight distinct typologies for the Bay Area: Live Work Cities; Job and Activity Magnet Cities; Anchor Cities; Multi-modal Cities; Hyper-connected Cities; Low-density Residential Cities; Medium-density Residential Cities; and Mixed-use Residential Cities. Our findings show that many clusters are strongly influenced by trip demand and traffic flow metrics. Finally, we examine the practicality of this typology and its potential to guide collaborative transportation management strategies. The typologies provide a foundation for dialogue among Bay Area cities, focusing on evaluating shared characteristics and leveraging successes or challenges to develop unified strategies for transportation management.
Kuncheria, Anu↗