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At least 55 records · Page 3

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.

15 GEOTHERMAL ENERGY

Modeled Electricity Demand Profiles for Electric Airport Ground Support Equipment in the United States

Electric airport ground support equipment (eGSE) hourly annual (8760) load datasets for the top 50 U.S. airports (by enplanements), as described in Liu et al. (2025). Please cite as: Liu, Bo, Kevin Robby, Jayaraj Rane, Adway Das, Kara Podkaminer, and Brennan Borlaug. 2025. Hourly Load Profile Dataset for Electric Airport Ground Support Equipment in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-92139. https://www.nlr.gov/docs/fy25osti/92139.pdf

24 POWER TRANSMISSION AND DISTRIBUTION

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.

accessibility

Modifikation eines Cu‐Pd‐Schaufelrad‐Metall‐Organischen Gerüsts für die selektive CO 2 ‐Elektroreduktion

Abstract Die Optimierung der Bindungsenergie zwischen dem Intermediat und dem aktiven Zentrum ist ein entscheidender Faktor, um die katalytische Produktselektivität und Aktivität bei der elektrochemischen Kohlendioxidreduktion (CO 2 RR) zu steuern. Es ist bekannt, dass Kupferatome als aktive Zentren CO 2 zu Kohlenwasserstoffen und Sauerstoffverbindungen reduzieren, jedoch unter schlechter Produktselektivität leiden, da mehrere Intermediate nur moderate Bindungsenergien aufweisen. Hier berichten wir über eine Ionenaustauschstrategie zur Konstruktion von Cu−Pd‐Schaufelrad‐Dimeren innerhalb von Cu‐basierten metallorganischen Gerüsten (MOFs), [Cu 3‐x Pdx(BTC) 2 ] (BTC=1,3,5‐Benzoltricarbonsäure), ohne die strukturellen Eigenschaften des MOFs zu verändern. Im Vergleich zum reinen Cu‐MOF ([Cu 3 (BTC) 2 ], HKUST‐1) verlagert der Cu−Pd‐MOF die Produkte der CO 2 ‐Elektroreduktion von einer Vielzahl chemischer Spezies hin zu einer selektiven CO‐Erzeugung. Eine in situ‐Röntgenabsorptions‐Feinstrukturanalyse der Oxidationsstufe des Katalysators und der lokalen Geometrie, kombiniert mit theoretischen Berechnungen, zeigt, dass die Einfügung von Pd in die Knotenpunkte der Cu‐Schaufelradstruktur des MOFs die Adsorption des Schlüsselintermediats COOH* am Cu‐Zentrum fördert. Dies ermöglicht CO‐selektive katalytische Mechanismen und verbessert somit unser Verständnis über das Zusammenspiel von Struktur und Aktivität bei der elektrochemischen CO 2 ‐Reduktion unter Verwendung molekularer Katalysatoren.

Zhang, Ruirui

Semiconductor-compatible topological digital alloys

Recently, GeSn alloys have attracted much interest for direct-gap infrared photonics and as potential topological materials which are compatible with the semiconductor industry. However, for photonics, the high-Sn content required leads to low detectivity, associated with poor material quality, and the (>35 %) Sn required for topological properties have been out of reach experimentally. Here, we demonstrate that by patterning the Sn distribution within Ge, the electronic properties have a far greater tunability than is possible with the random alloy. For the GeSn δ-digital alloy (DA) formed by confining Sn atoms in atomic layer(s) along the [111] direction of Ge, we show that ∼ 10 % Sn can lead to a triple-point semimetal. These findings are understood in terms of Sn ordering causing spatial separation of Sn and Ge band edges, leading to band inversion. This mechanism can also lead to a weak topological insulator, Weyl semimetal, and enables tunable direct bandgaps down to 2 meV, covering the entire infrared range. This DA induced topological properties are also identified in compound semiconductors, such as InAs 1-x Sb x , showing the general applicability of the DA design for realizing topological properties on conventional semiconductor platforms. Furthermore, our findings not only point to a new class of currently unexplored topological systems accessible by epitaxy, but also establish the promise of low-Sn GeSn DAs for application as infrared laser diodes and photodetectors in Si photonic integrated circuits and infrared image sensors.

3D topological semimetal

Numerically exact configuration interaction at quadrillion-determinant scale

The combinatorial growth of configuration interaction (CI) has long limited this formally exact quantum chemistry method to only the smallest molecules. Here, we report a numerically exact CI calculation exceeding one quadrillion (10 15 ) determinants, made possible by a lossless categorical compression strategy within the small-tensor-product distributed active space (STP-DAS) framework. This approach overcomes the traditional memory bottlenecks of CI by a numerically exact compression of the wavefunction representation and reformulating the most computationally demanding matrix–vector operations. Using this method, we performed a fully relativistic CI calculation of the ground state of HBrTe with over 10 15 complex-valued determinants in just 34.5 h on 1000 computing nodes—the largest CI calculation ever reported. We further achieved fast computation for systems with hundreds of billions of determinants on only a few compute nodes. Extensive benchmarks confirm that the method retains full numerical exactness while cutting memory and computational cost by orders of magnitude. Compared to previous state-of-the-art CI calculations, this work achieves a 1000 times increase in CI space, a 10 6 -fold increase in floating-point operations performed, and a 10 6 -fold improvement in computational speed.

Computational chemistry

Beyond leading twist: 𝜌 meson decay constants and distribution amplitudes in a self-consistent light-front quark model

In this study, we present a comprehensive analysis of decay constants and chiral-even and chiral-odd distribution amplitudes (DAs) up to twist 4 for the 𝜌 meson in the standard light-front quark model (LFQM) based on the Bakamjian-Thomas (BT) construction. For the 𝜌 meson, which possesses both longitudinal (ℎ=0) and transverse (ℎ =±1) polarizations, two types of decay constants, 𝑓$^{∥}_{𝜌}$ and 𝑓$^{⊥}_{𝜌}$, arises accordingly. We demonstrate that these decay constants can be self-consistently extracted from both local (𝑧𝜇 =0) and nonlocal (𝑧𝜇 ≠0) matrix elements ⟨0⁢|$\overline{𝑞}$(𝑧)⁢Γ⁢𝑞⁡(−𝑧)|⁢𝜌⁡(𝑃,ℎ)⟩, with Γ=(𝛾 𝜇 ,𝜎 𝜇⁢𝜈 ,𝛾 𝜇 ⁢𝛾 5 ,𝟏), in a manner independent of current components, polarizations, and reference frames. In particular, we emphasize the role of nonlocal matrix elements involving axial-vector and scalar currents, where mixing between 𝑓$^{∥}_{𝜌}$ and 𝑓$^{⊥}_{𝜌}$ occurs. We show that this mixing is consistently resolved through the BT construction, ensuring the proper extraction of these decay constants.

Electroweak interaction

Lattice QCD calculation of 𝑥-dependent meson distribution amplitudes at physical pion mass with threshold logarithm resummation

We present a lattice quantum chromodynamics (QCD) calculation of the 𝑥-dependent pion and kaon distribution amplitudes (DA) in the framework of large momentum effective theory. This calculation is performed on a fine lattice of 𝑎 = 0.076 fm at physical pion mass, with the pion boosted to 1.8 GeV and kaon boosted to 2.3 GeV. We renormalize the matrix elements in the hybrid scheme and match to Math output error with a subtraction of the leading renormalon in the Wilson-line mass. The perturbative matching is improved by resumming the large logarithms related to the small quark and gluon momenta in the soft-gluon limit. After resummation, we demonstrate that we are able to calculate a range of 𝑥 ∈[𝑥 0 ,1 − 𝑥 0 ] with 𝑥 0 = 0.25 for pion and 𝑥 0 = 0.2 for kaon with theoretical systematic errors under control. The kaon DA is shown to be slighted skewed, and narrower than pion DA. Although the 𝑥-dependence cannot be direct calculated beyond these ranges, we estimate higher moments of the pion and kaon DAs by complementing our calculation with short-distance factorization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Continuous surface-to-distributed acoustic sensor snapshots explain reactivation of individual natural fractures during an unconventional reservoir stimulation

ABSTRACT Fiber-optic sensing technologies allow petroleum engineering teams to detect hydraulic fracture interaction with boreholes during unconventional reservoir stimulation. In combination with high-repeatability seismic sources, the same distributed acoustic sensors (DASs) enable vertical seismic profiling (VSP) of the fracture evolution away from the boreholes. We discovered clear signatures of seismic scattering on activated fractures during nine days of continuous seismic monitoring of the fracturing stages at the Austin Chalk/Eagle Ford Field Laboratory. The present study applies a novel approach for quantitative analysis of the scattering events in terms of the evolution of the geometry and elastic stiffness of individual fractures. Our characterization strategy sequentially refines the fracture models: from a stack of 1D soft layers to 3D rectangular inclusions. First, we estimate the number of fracture locations and reflectivity using a modified sparse-spike deconvolution of the stacked VSP traces. The fracture set consists of five fractures spaced by 15–30 m with a reflectivity of approximately 1%. Then, we develop a scattering integral method to refine these estimates along with an inversion of the fracture top and bottom for each monitoring vintage. We find that, initially, some of the fractures are located above the monitoring fiber with the height of approximately 100 m. Then we integrate the seismic interpretation with the low-frequency DAS and pressure and microseismic monitoring to reconstruct the activation process of the fractures. Most likely, some of the natural fractures slowly grew downward to the monitoring fiber as a result of fluid injections in the stimulated well. This led to bright strain anomalies but did not trigger seismicity. The top of the fractures remained almost constant and were limited by a lithologic boundary/stress barrier. To our knowledge, this is the first time VSP data enabled tracking of the fracture evolution with such high spatial and temporal resolution, which was previously only available for crosswell surveys and at a much smaller scale.

Glubokovskikh, Stanislav

Complementary Subsurface Characterization Methods to Develop a Geologic Model for the EGS Collab Experiment, Sanford Underground Research Facility

The EGS (Enhanced Geothermal Systems) Collab project was performed within the Sanford Underground Research Facility (SURF) with a goal of understanding processes and evaluation of models related to hydraulic stimulation of rock at depth. The present work deals with the development of Testbed 2 where experiments were conducted at a depth of 1.25 km and were located within a well-characterized testbed in a metamorphic, amphibolite host rock. A total of eleven boreholes varying in length between 10.6 m and 81.2 m were continuously cored to develop the testbed. In addition to the continuous coring of the amphibolite host rock, geophysical instrumentation supporting electrical resistivity tomography (ERT), microearthquake (MEQ) detection, and optical fiber providing distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) were installed in the monitoring boreholes, all of which produced a comprehensive complementary suite of characterization and monitoring technologies. Pre-stimulation characterization of the groundwater conditions identified only two hydraulically significant fractures, neither of which transects the central portion of the testbed. Flow and pressure monitoring indicated low preexisting pore pressure conditions likely affected by the mine openings.

15 GEOTHERMAL ENERGY

Complementary Subsurface Characterization Methods to Develop a Geologic Model for the EGS Collab Experiment, Sanford Underground Research Facility

The EGS (Enhanced Geothermal Systems) Collab project was performed within the Sanford Underground Research Facility (SURF) with a goal of understanding processes and evaluation of models related to hydraulic simulation of rock at depth. The present work deals with the development of Testbed 2 where experiments were conducted at a depth of 1.25 km and were located within a well-characterized testbed in a metamorphic, amphibolite host rock. A total of eleven boreholes varying in length between 10.6 m and 81.2 m were continuously cored to develop the testbed. In addition to the continuous coring of the amphibolite host rock, geophysical instrumentation supporing electrical resisivity tomography (ERT), microearthquake (MEQ) detection, and optical fiber providing distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) were installed in the monitoring boreholes, all of which produced a comprehensive complementary suite of characterization and monitoring technologies.

15 GEOTHERMAL ENERGY

Quantum Enhanced Fiber Sensing for Oil and Gas Applications (Final Technical Report)

The major goal of the project was to develop a quantum sensing approach compatible with current infrastructure in the oil and gas industry that could provide a quantum enhancement, with a focus towards pipeline leak detection. The proposed approach to achieving this goal consisted of the use of quantum states of light to enhance the sensitivity of fiber optic sensors beyond the classical limit. To address the limitations of existing commercial leak detection, the project focused on the study of two technologies: distributed acoustic sensors (DAS) and fiber Bragg grating (FBG)-based sensors. The initial phase of the project centered around the identification and mitigation of dominant sources of classical noise to enable operation at the classical shot noise limit (SNL). The second phase focused on the use of quantum resources to obtain sensitivities beyond the classical SNL. The final phase sought to combine quantum noise reduction techniques with advanced dynamical classical noise cancelation approaches.

02 PETROLEUM

Distributed optical fibre sensing in physical oceanography: emergence and future prospects

Distributed optical fibre sensing (DOFS) is a technology that enables continuous, real-time measurements of a range of environmental parameters along the length of a fibre optic cable. In this article, we review the recently emerged applications of DOFS techniques in physical oceanography and offer a perspective on the technology's potential for future growth within the field. The introduction of DOFS to physical oceanography is contextualised with a brief history of the technology, which spun off primarily from the use of seafloor-laid optical fibres for telecommunications purposes. The key components and underpinning physics of a DOFS system are outlined and, on their basis, the suite of variables that are observable with DOFS are discussed. The implementation factors of DOFS, which include trade-offs between measurement accuracy and spatio-temporal resolutions and ranges, are also examined. The physical oceanographic applications of DOFS to date are then illustrated with case examples of four distinct DOFS techniques: distributed temperature sensing (DTS), which can provide ocean temperature observations; distributed static strain sensing (DSS) and distributed acoustic sensing (DAS), which are sensitive to temperature, cable strain and strain-associated variables, such as pressure and ocean velocity; and ultra-long-range observations of ocean currents with optical interferometry. The forthcoming prospects of DOFS in physical oceanography are considered, and are proposed to include new fibre optic-based approaches to sense ocean salinity and measure through the water column. We conclude with reflections on the future role of DOFS within the Global Ocean Observing System, and highlight the opportunities provided by the existing world-wide network of seafloor-laid optical fibres.

Naveira Garabato, Alberto C. [Univ. of Southampton

Hydraulic Conductivity Measurements, Utqiagvik (Barrow), Alaska, 2014

Six individual ice cores were collected from the Barrow Environmental Observatory in Barrow, Alaska, in May of 2013 as part of the Next Generation Ecosystem Experiment (NGEE). Each core was drilled at a different location to varying depths. After drilling, the cores were stored in coolers packed with dry ice and flown to Lawrence Berkeley National Laboratory (LBNL) in Berkeley, CA. 3-dimensional images of the cores were constructed using medical X-ray computed tomography (CT) scanner at 120kV. Hydraulic conductivity samples were extracted from these cores at LBNL Richmond Field Station in Richmond, CA, in February 2014 by cutting 5 to 8 inch segments using a chop saw. Samples were packed individually and stored at -20C freezing temperatures to minimize any changes in structure or loss of ice content prior to analysis. Hydraulic conductivity was determined through falling head tests using a permeameter [ELE International, Model #: K-770B] (Appendix A). Samples were placed in a latex membrane via a membrane stretcher while frozen. Use of a membrane stretcher made the membranes easier to secure and minimized contact with the sample. A clear polycarbonate sleeve, fabricated with a stainless steel ring at the bottom to keep the sleeve from floating, was placed around the sample inside the permeameter to minimize deformation during analysis. The permeameter was filled with water and 1.0 PSI of air was applied for confining pressure during sample defrost. Outflow valves were left open to allow for incremental thawing and samples were left to thaw for approximately 12 hours. After approximately 12 hours of thaw, initial falling head tests were performed. When the flow was significantly too fast or too slow, the analysis was stopped and the burette size was adjusted accordingly (i.e. a larger diameter burette was used for flows that were faster than desired or a smaller diameter burette was used for flows that were slower than desired). Two to four measurements were collected on each sample and collection stopped when the applied head load exceeded 25% change from the original load. Analyses were performed between 2 to 3 times for each sample. The final hydraulic conductivity calculations were computed using methodology of Das et al., 1985.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES

RNA-Puzzles Round V: blind predictions of 23 RNA structures

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson–Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.

59 BASIC BIOLOGICAL SCIENCES

Absorption of Fermionic Dark Matter in the PICO-60 C 3 F 8 Bubble Chamber

Fermionic dark matter absorption on nuclear targets via neutral current interactions is explored using a nonrelativistic effective field theory framework. An analysis of data from the PICO-60 C 3 ⁢F 8 bubble chamber sets leading constraints on spin-independent absorption for dark matter masses below 23 MeV/𝑐 2 and establishes the first limits on spin-dependent absorptive interactions. In conclusion, these results demonstrate the sensitivity of bubble chambers to low-mass dark matter and underscore the importance of absorption searches in expanding the parameter space of direct detection experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS