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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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All sources NASA NTRS DOE OSTI

DOE OSTI2022

INGENIOUS - Great Basin Regional Dataset Compilation

This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while reducing the exploration and development risks for all geothermal resources. These datasets will be used in INGENIOUS as input features for predicting geothermal favorability throughout the Great Basin study area. Datasets consist of shapefiles, geotiffs, tabular spreadsheets, and metadata that describe: 2-meter temperature probe surveys, quaternary faults and volcanic features, geodetic shear and dilation models, heat flow, magnetotellurics (conductance), magnetics, gravity, paleogeothermal features (such as sinter and tufa deposits), seismicity, spring and well temperatures, spring and well aqueous geochemistry analyses, thermal conductivity, and fault slip and dilation tendency. For additional project information, see the INGENIOUS project site linked in the submission. Terms of use: These datasets are provided "as is", and the contributors assume no responsibility for any errors or omissions. The user assumes the entire risk associated with their use of these data and bears all responsibility in determining whether these data are fit for their intended use. These datasets may be redistributed with attribution (see citation information below). Please refer to the license information on this page for full licensing terms and conditions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗

DOE OSTI2025

Exploring Geothermal Potential of Great Basin Sub-Regions

exploration↗

DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗

DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Hybrid and Inorganic Vacancy-Ordered Double Perovskites A 2 WCl 6

We report hybrid and all-inorganic, vacancy-ordered double perovskites of d 2 W 4+ with the formula A 2 WCl 6 (A = CH 3 NH 3 + , Rb + , and Cs + ). These compounds, which are reddish in color, can be distinguished from structurally similar compounds obtained by hydrothermal methods on the basis of structure, spectroscopic, and magnetic properties. The latter are green and incorporate oxygen, with the actual formula Cs 2 WO x Cl 6–x and distinct optical absorption and emission behavior. Furthermore, the local-moment magnetism of the pure-red d 2 compounds reported here does not correspond to the appropriate Kotani model, suggesting as-yet undiscovered physics in these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Discovery of a Hybrid System for Photocatalytic CO 2 Reduction via Attachment of a Molecular Cobalt-Quaterpyridine Complex to a Crystalline Carbon Nitride

While recent reports have demonstrated the attachment of molecular catalysts to amorphous, graphitic carbon nitrides (g-CN) for light-driven CO 2 reduction, approaches to the utilization of crystalline carbon nitrides have remained undiscovered. Herein, a functional hybrid photocatalyst system has been found using a crystalline carbon nitride semiconductor, poly(triazine imide) lithium chloride (PTI-LiCl), with a surface-attached CoCl 2 (qpy-Ph-COOH) catalyst for CO 2 reduction. The molecular catalyst attaches to PTI-LiCl at concentrations from 0.10 to 4.30 wt % and exhibits ∼96% selectivity for CO production in a CO 2 -saturated, aqueous 0.5 M KHCO 3 solution. Optimal loadings were found to be within 0.42–1.04 wt % with rates between 1,400 and 1,550 μmol CO/g·h at an irradiance of 172 mW/cm 2 (λ = 390 nm) and apparent quantum yields of ∼2%. This optimized loading is postulated to represent a balance between maximal turnover frequency (TOF; 300+ h –1 ) and excess catalyst that can limit excited-electron lifetimes, as probed via transient absorption spectroscopy. An increase in the incident irradiance yields a concomitant increase in the TOFs and CO rates only for the higher catalyst loadings, reaching up to 2,149 μmol CO/g·h with a more efficient use of the catalyst surface capacity. The lower catalyst loadings, by comparison, already function at maximal TOFs. Higher surface loadings are also found to help mitigate deactivation of the molecular catalysts during extended catalytic testing (>24 h) owing to the greater net surface capacity for CO 2 reduction, thus representing an effective strategy to extend lifetime. The hybrid particles can be deposited onto an FTO substrate to yield ∼60% Faradaic efficiency for photoelectrochemical CO production at −1.2 V vs Ag/AgCl bias. In conclusion, these results demonstrate the synergistic combination of a crystalline carbon nitride with a molecular catalyst that achieves among the highest known rates in carbon-nitride systems for the light-driven CO 2 reduction to CO in aqueous solution with >95% selectivity.

CO2 reduction↗

DOE OSTI2022

Identifying candidate structured RNAs in CRISPR operons

Noncoding RNAs with secondary structures play important roles in CRISPR-Cas systems. Many of these structures likely remain undiscovered. We used a large-scale comparative genomics approach to predict 156 novel candidate structured RNAs from 36,111 CRISPR-Cas systems. A number of these were found to overlap with coding genes, including palindromic candidates that overlapped with a variety of Cas genes in type I and III systems. Among these 156 candidates, we identified 46 new models of CRISPR direct repeats and 1 tracrRNA. This tracrRNA model occasionally overlapped with predicted cas9 coding regions, emphasizing the importance of expanding our search windows for novel structure RNAs in coding regions. We also demonstrated that the antirepeat sequence in this tracrRNA model can be used to accurately assign thousands of predicted CRISPR arrays to type II-C systems. This study highlights the importance of unbiased identification of candidate structured RNAs across CRISPR-Cas systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗

DOE OSTI2024

Hidden transport phenomena in an ultraclean correlated metal

Advancements in materials synthesis have been key to unveil the quantum nature of electronic properties in solids by providing experimental reference points for a correct theoretical description. Here, we report hidden transport phenomena emerging in the ultraclean limit of the archetypical correlated electron system SrVO 3 . The low temperature, low magnetic field transport was found to be dominated by anisotropic scattering, whereas, at high temperature, we find a yet undiscovered phase that exhibits clear deviations from the expected Landau Fermi liquid, which is reminiscent of strange-metal physics in materials on the verge of a Mott transition. Further, the high sample purity enabled accessing the high magnetic field transport regime at low temperature, which revealed an anomalously high Hall coefficient. Taken with the strong anisotropic scattering, this presents a more complex picture of SrVO 3 that deviates from a simple Landau Fermi liquid. These hidden transport anomalies observed in the ultraclean limit prompt a theoretical reexamination of this canonical correlated electron system beyond the Landau Fermi liquid paradigm, and more generally serves as an experimental basis to refine theoretical methods to capture such nontrivial experimental consequences emerging in correlated electron systems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Engineering a new tripartite split-ccGFP system from Corynactis californica for detecting protein–protein interactions

Protein-protein interactions (PPIs) are critical to a range of biological processes and, consequently, aberrant interactions are implicated in many disorders. The study of the complex networks of PPIs promises to elucidate undiscovered roles in cellular processes and the mechanisms of disease. To accomplish this, tools to effectively sense PPIs are necessary. Effective PPI sensors must rapidly detect interactions in real-time with high sensitivity without perturbing the proteins of interest (POIs) under study. Split fluorescent proteins have previously been used to successfully monitor PPIs, in part due to the small size of the tags. Here, we developed an optimized tripartite split GFP system based on Corynactis californica GFP (ccGFP) to detect PPIs in vitro. In this sensor system, ccGFP fragments ccGFP10 and ccGFP11 are tagged to two POIs. PPIs can then be detected via fluorescence by complementation to the third fragment, ccGFP1-9, which reconstitutes functional ccGFP. The optimized ccGFP system shows improved detection kinetics and pH and temperature stability compared to a previous system. We then validated the sensor by monitoring PPIs in two model systems: attractive/repulsive coiled-coils and rapamycin-inducible FRB/FKBP heterodimerization. Finally, we developed an anti-tripartite ccGFP single-chain variable fragment (scFv), which could enable versatile detection of identified protein-protein complexes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Metal Halide Scaffolded Assemblies of Organic Molecules with Enhanced Emission and Room Temperature Phosphorescence

Ionically bonded organic metal halide hybrids have emerged as versatile multicomponent material systems exhibiting unique and useful properties. The unlimited combinations of organic cations and metal halides lead to the tremendous structural diversity of this class of materials, which could unlock many undiscovered properties of both organic cations and metal halides. Here we report the synthesis and characterization of a series benzoquinolinium (BZQ) metal halides with a general formula (BZQ)Pb 2 X 5 (X = Cl, Br), in which metal halides form a unique two-dimensional (2D) structure. These BZQ metal halides are found to exhibit enhanced photoluminescence and stability as compared to the pristine BZQ halides, due to the scaffolding effects of 2D metal halides. Optical characterizations and theoretical calculations reveal that BZQ + cations are responsible for the emissions in these hybrid materials. Changing the halide from Cl to Br introduces heavy atom effects, resulting in yellow room temperature phosphorescence (RTP) from BZQ + cations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Reconnection-driven energy cascade in magnetohydrodynamic turbulence

Magnetohydrodynamic turbulence regulates the transfer of energy from large to small scales in many astrophysical systems, including the solar atmosphere. We perform three-dimensional magnetohydrodynamic simulations with unprecedentedly large magnetic Reynolds number to reveal how rapid reconnection of magnetic field lines changes the classical paradigm of the turbulent energy cascade. By breaking elongated current sheets into chains of small magnetic flux ropes (or plasmoids), magnetic reconnection leads to a previously undiscovered range of energy cascade, where the rate of energy transfer is controlled by the growth rate of the plasmoids. As a consequence, the turbulent energy spectra steepen and attain a spectral index of -2.2 that is accompanied by changes in the anisotropy of turbulence eddies. The omnipresence of plasmoids and their consequences on, for example, solar coronal heating, can be further explored with current and future spacecraft and telescopes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Giant Outer Transiting Exoplanet Mass (GOT ‘EM) Survey. II. Discovery of a Failed Hot Jupiter on a 2.7 Yr, Highly Eccentric Orbit

Radial velocity (RV) surveys have discovered giant exoplanets on au-scale orbits with a broad distribution of eccentricities. Those with the most eccentric orbits are valuable laboratories for testing theories of high-eccentricity migration. However, few such exoplanets transit their host stars, thus removing the ability to apply constraints on formation from their bulk internal compositions. We report the discovery of Kepler-1704 b, a transiting 4.15 M {sub J} giant planet on a 988.88 day orbit with an extreme eccentricity of 0.921{sub -0.015}{sup +0.010}. Our decade-long RV baseline from the Keck I telescope allows us to measure the orbit and bulk heavy-element composition of Kepler-1704 b and place limits on the existence of undiscovered companions. A failed hot Jupiter, Kepler-1704 b was likely excited to high eccentricity by scattering events that possibly began during its gas accretion phase. Its final periastron distance was too large to allow for tidal circularization, so now it orbits its host from distances spanning 0.16–3.9 au. The maximum difference in planetary equilibrium temperature resulting from this elongated orbit is over 700 K. A simulation of the thermal phase curve of Kepler-1704 b during periastron passage demonstrates that it is a remarkable target for atmospheric characterization from the James Webb Space Telescope, which could potentially also measure the planet’s rotational period as the hot spot from periastron rotates in and out of view. Continued characterization of the Kepler-1704 system promises to refine theories explaining the formation of hot Jupiters and cool giant planets like those in the solar system.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

54 ENVIRONMENTAL SCIENCES↗