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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Elucidating Cycling Rate-Dependent Electrochemical Strains in Sodium Iron Phosphate Cathodes for Na-ion Batteries

Battery electrodes materials undergo significant mechanical instabilities which affects their longevity and exert rate-limitations during the cycling process. In this study, we investigate the rate-dependent mechanical response of sodium iron phosphate (NaFePO4, NFP) cathodes during Na intercalation via galvanostatic cycling at different rates by employing digital image correlation, electrochemical methods, and mathematical model. The mechanical behaviour of the electrode shows strong dependance on the applied scan rate. At slower rates, electrode shows asymmetrical strain generation between anodic and cathodic cycles, which is attributed to the formation of cathode-electrolyte interface layers. The electrode undergoes smaller strain generation when cycled at slower rates when the same amount of Na ions is removed or inserted into the electrode. A mathematical model was developed to predict strain evolution in the composite electrode as well as the concentration profile of the Na ions in the electrode particles. Rate-dependent and time dependent factors on the strain generation in the electrode are attributed to the capacity-dependent intercalation strains, rate-dependent mismatch strains, and time-dependent irreversible strains. The combination of in situ strain measurements with the analytical model provided new insight into the electrochemically induced mechanical deformations in Na-ion cathode electrodes.

Ozdogru, Bertan↗
DOE OSTI2016

The nature of nano-sized precipitates in ferritic/martensitic steel P92 produced by thermomechanical treatment

Thermomechanical treatment (TMT) can effectively improve the mechanical properties of high-Cr ferritic/martensitic (F/M) steels, which has been mainly attributed to a dense dispersion of nano-sized precipitates. Precipitate phases in high-Cr F/M steels produced by TMT require further investigations. Precipitates in commercial F/M steel P92 produced by a TMT process, warm-rolled at 650 °C plus tempered at 650 °C for 1 h, were investigated by transmission electron microscopy. Nano-sized precipitates with a high number density in the steel after the TMT were found to be Cr-rich M{sub 2}(C,N) carbonitride, rather than MX or M{sub 23}C{sub 6} phase. The M{sub 2}(C,N) carbonitride has a hexagonal lattice with the lattice parameters about a/c = 0.299/0.463 nm. These M{sub 2}(C,N) carbonitrides with a typical composition of (Cr{sub 0.85}V{sub 0.06}Fe{sub 0.06}Mo{sub 0.03}){sub 2}(C,N) have an average diameter smaller than 30 nm, and mainly distribute on dislocations and at the boundaries of equiaxed ferrite grains in the TMT steel. The TMT process inhibits the precipitation of M{sub 23}C{sub 6} and M{sub 5}C{sub 2} phases. Enhanced creep properties of the P92 steel after the TMT, as reported previously, were considered to be mainly attributed to plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides produced by the TMT rather than to MX and M{sub 23}C{sub 6} precipitates. - Graphical abstract: TEM micrographs of precipitates on extraction carbon replicas prepared from ferritic/martensitic (F/M) steel P92. (a) After conventional heat treatment, normalized at 1050 °C for 30 min plus tempered at 765 °C for 1 h. (b) After a thermomechanical treatment (TMT), warm-rolled at 650 °C plus tempered at 650 °C for 1 h. Nano-sized precipitates with a high number density in the steel produced by the TMT were found to be Cr-rich M{sub 2}(C,N) carbonitride, rather than MX or M{sub 23}C{sub 6} phase. The TMT process inhibits the precipitation of M{sub 23}C{sub 6} and M{sub 5}C{sub 2} phases. Enhanced creep properties of the steel by the TMT are mainly attributed to plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides, rather than to MX and M{sub 23}C{sub 6} precipitates. It is suggested that plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides produced by the TMT are more helpful to improve the creep properties of the steel than M{sub 23}C{sub 6} plus MX precipitates obtained by the conventional heat treatment. - Highlights: •Carbon replicas were used for precisely identifying nano-sized phases in steel produced by TMT. •Densely nano-sized precipitates in P92 steel after TMT were found to be Cr-rich M{sub 2}(C,N) phase. •Fine precipitates produced by TMT can be M{sub 2}X phase instead of MX and/or M{sub 23}C{sub 6} phase. •Enhanced creep properties of P92 steel by TMT is attributed to fine M{sub 2}X, rather than to MX and M{sub 23}C{sub 6}. •TMT involving a tempering at 650 °C inhibits the formation of M{sub 23}C{sub 6}/M{sub 5}C{sub 2} phases in high-Cr steels.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Effects of size reduction on microstructural, optical, vibrational, magnetic and photocatalytic properties of ZnO nanocrystals

Highlights: • ZnO nanocrystals of different sizes 4–19 nm, thus different S/V ratios synthesized. • Lattice strain and lattice volume increase with size reduction. • Raman spectra show enhanced surface effects with phonon mode disappearance. • ESR shows exchange interaction; VSM shows surface originated ferromagnetism. • Photocatalysis is less affected with size reduction and surface enhancement. - Abstract: ZnO nanocrystals were synthesized in the size range 4 nm–19 nm by chemical co-precipitation method and extensively characterized by various techniques. Size control was achieved by changing the concentration of ME that acted as the capping agent. Lattice strain and lattice volume were found to increase with the decrease in crystallite size that could be attributed to an enhanced influence of surface atoms. Raman spectroscopy provided evidence of enhancement of surface influence and reduction of crystallinity with the observation of decrease in E{sub 2}{sup high} phonon mode intensity with a decrease in crystallite size. Electron spin resonance spectra revealed the presence of exchange interaction as well as presence of lattice disorder. Vibrating sample magnetometry results showed that M-H curves exhibit hysteresis loops which confirmed the ferromagnetic nature of the nanocrystals and it could be attributed to the interaction between localized electron spins resulting from oxygen vacancies at the surface. Blue shift of the UV–Visible absorption spectra was observed with decrease in crystallite size, which could be attributed to quantum confinement effect. Room temperature photoluminescence spectra were recorded under UV light excitation, which showed emission of different colours (light violet, violet, blue and green) in the visible region, attributable to the presence of various types of defects such as zinc interstitial, zinc vacancy and oxygen vacancy in the ZnO nanocrystals. Photodegradation of aqueous methylene blue by the ZnO nanocrystals was performed under simulated sunlight irradiation. There was a very small increase in efficiency of photodegradation with size reduction, which showed that there was no significant change in surface defects with size reduction in the nanocrystals despite the surface enhancement. A mechanism for photocatalysis involving defect states has been proposed.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

A framework for assessment of predictive capability maturity and its application in nuclear thermal hydraulics

Here this work presents a formalized and computerized framework for the assessment of decision regarding the adequacy of a simulation tool for a nuclear reactor application. The decision regarding a code’s adequacy for an application is dependent on the assessment of different attributes that govern verification, validation, and uncertainty quantification of the code. In this work, the focus is on code validation. Therefore, the framework is developed and illustrated from the perspective of decision regarding the validation assessment of a code. Code validation assessment is performed based on the validation test results, data applicability, and process quality assurance factors. The process quality assurance factors warrant the trustworthiness of the evidence and help in checking people and process compliance with respect to the standard requirements. The proposed framework is developed using an argument modeling technique called Goal Structuring Notation (GSN). Goal structuring notation facilitates structural knowledge representation, information abstraction, evidence incorporation, and provides a skeletal structure for quantitative maturity assessment. The decision schema for the development of the decision model is based on the Predictive Capability Maturity Model (PCMM) and Analytic Hierarchy Process (AHP) and formalized using Goal structuring notation. Each decision attribute is formulated as a claim, where the degree of validity of the claim (attribute’s assessment) is expressed using different maturity levels. The GSN representation of the decision model is transformed into a confidence network to provide evidence-based quantitative maturity assessment using the Bayesian network. A metric based on the expected utility of maturity levels, called expected distance metric, is proposed to measure the distance between target maturity and achieved maturity on a scale of zero to one. Expected distance metric helps in comparing the assessment of different attributes and identification of major areas of concern in terms of modeling capability, data needs, and quality of assessment process. The practical application of the framework is demonstrated by a case study on validation assessment of a thermal-hydraulic code for a challenge problem called Departure from Nucleate Boiling (DNB).

42 ENGINEERING↗
DOE OSTI2021

Measurement and analysis of GPU-accelerated applications with HPCToolkit

To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. Here, we illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

This study discovers various geothermal prospects in the Great Basin, USA based on shallow groundwater chemical (geochemical) data. The geochemical data are expected to include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical data in the Great Basin at 14,341 locations including 18 attributes. Next, a non-negative matrix factorization with customized k-means clustering is applied to the geochemical data matrix that automatically finds three hidden geothermal signatures representing modestly, moderately, and highly confident geothermal prospects. The algorithm also evaluated the probability of occurrence of these types of resources through the studied region. There is a consistency between regional geothermal prospectivity as estimated by our ML methodology and the traditional play fairway analysis conducted over a portion of the study area. We also identify the dominant data attributes associated with each signature. Finally, our ML analyses allow us to reconstruct attributes from sparse into continuous over the study domain. The predicted continuous attributes can be used for future detailed geothermal explorations in the Great Basin.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Preface: Chemical forensics

Introduction: This virtual special issue stems from 36 presentations given at the Fourth Chemical Forensics Symposium held 3 - 4 April 2017 as part of the 253rd American Chemical Society (ACS) National Meeting & Exposition in San Francisco, CA, USA. Since 2011, the ACS Division of Analytical Chemistry has hosted symposia that review developments and topics arising in chemical forensics. Chemical forensics is a developing scientific discipline that aims to attribute a chemical (or mixture) of interest to its source by the analysis of the chemical itself or associated material constituents to address investigative, legal or intelligence questions. “Source” refers to how, where and when a chemical was handled or produced. Given past and recent attacks with chemical warfare agents (CWAs) and other toxic chemicals there is a strong need for research and development in chemical forensics for the purpose of finding those responsible for chemical attacks. Hence, many of the papers in this issue deal with the source attribution of toxic chemicals that can be used as weapons. However, given the needs, principles and methods that are shared across different application areas of material provenance or authentication, papers that deal with the source attribution of other illicitly used chemicals and materials are included.

Chemical Forensic↗
DOE OSTI2024

Ultrafast Exciton Dynamics of CH 3 NH 3 PbBr 3 Perovskite Nanoclusters

Exciton dynamics of perovskite nanoclusters has been investigated for the first time using femtosecond transient absorption (TA) and time-resolved photoluminescence (TRPL) spectroscopy. The TA results show two photoinduced absorption signals at 420 and 461 nm and a photoinduced bleach (PB) signal at 448 nm. The analysis of the PB recovery kinetic decay and kinetic model uncovered multiple processes contributing to electron-hole recombination. The fast component (~8 ps) is attributed to vibrational relaxation within the initial excited state, and the medium component (~60 ps) is attributed to shallow carrier trapping. The slow component is attributed to deep carrier trapping from the initial conduction band edge (~666 ps) and the shallow trap state (~40 ps). The TRPL reveals longer time dynamics, with modeled lifetimes of 6.6 and 93 ns attributed to recombination through the deep trap state and direct band edge recombination, respectively. In conclusion, the significant role of exciton trapping processes in the dynamics indicates that these highly confined nanoclusters have defect-rich surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Atmospheric and Long-term Aging Effects on the Electrical Properties of Variable Thickness WSe 2 Transistors

Atmospheric and long-term aging effects on electrical properties of WSe 2 transistors with various thicknesses are examined. While countless published studies report electrical properties of transition-metal dichalcogenide materials, many are not attentive to testing environment or to age of samples, which we have found significantly impacts results. Our as-fabricated exfoliated WSe 2 pristine devices are predominantly n-type, which is attributed to selenium vacancies. Transfer characteristics of as-fabricated devices measured in air then vacuum reveal physisorbed atmospheric molecules significantly reduced n-type conduction in air. First-principles calculations suggest this short-term reversible atmospheric effect can be attributed primarily to physisorbed H 2 O on pristine WSe 2 , which is easily removed from the pristine surface in vacuum due to the low adsorption energy. Devices aged in air for over 300 h demonstrate irreversibly increased p-type conduction and decreased n-type conduction. Moreover, they develop an extended time constant for recovery of the atmospheric adsorbents effect. Short-term atmospheric aging (up to approximately 900 h) is attributed to O 2 and H 2 O molecules physisorbed to selenium vacancies where electron transfer from the bulk and adsorbed binding energies are higher than the H 2 O-pristine WSe2. The residual/permanent aging component is attributed to electron trapping molecular O 2 and isoelectronic O chemisorption at selenium vacancies, which also passivates the near-conduction band gap state, p-doping the material, with very high binding energy. All effects reflected have the expected thickness dependence, namely, thinner devices are more sensitive to atmospheric and long-term aging effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

Cheng, Yanyan↗
DOE OSTI2016

Magnetic iron oxide and manganese-doped iron oxide nanoparticles for the collection of alpha-emitting radionuclides from aqueous solutions

Magnetic nanoparticles are well known to possess chemically active surfaces and high surface areas that can be employed to extract a range of ions from aqueous solutions. Additionally, their paramagnetic property provides a convenient means for bulk collection of the material from solution after the targeted ions have been adsorbed. Herein, two nanoscale amphoteric metal oxides, each possessing useful magnetic attributes, were evaluated for their ability to collect both naturally occurring radioactive isotopes (polonium (Po), radium (Ra), and uranium (U)) as well as the transuranic element americium (Am) from a suite of naturally occurring aqueous matrices. The nanomaterials include commercially available paramagnetic magnetite (Fe3O4) and magnetite that was modified to incorporate manganese (Mn) into the crystal structure. The chemical stability of these nanomaterials was evaluated in Hanford Site, WA ground water between the natural pH (~8) and pH 1 (acidified with HCl). Whereas the magnetite was observed to have good stability over the pH range, the Mn-doped material was observed to leach Mn at low pH. The materials were evaluated in parallel to characterize their uptake performance of the aforementioned alpha-emitting radionuclide spikes from Hanford Site ground water across a range of pH (from ~8 down to 2). In addition, radiotracer uptake experiments were performed on Columbia River water, seawater, and human urine at their natural pH and at pH 2. Despite the observed leaching of Mn from the Mn-doped nanomaterial in the lower pH range, it exhibited generally superior analyte extraction performance compared to the magnetite, and analyte uptake was observed across a broader pH range. The uptake behavior of the various radiotracers on these two materials at different pH levels can generally be explained by the amphoteric nature of the nanoparticle surfaces. Finally, the rate of sorption of the radiotracers on the two materials in unacidified groundwater was evaluated. The uptake curves generally indicate that equilibrium is obtained within a few minutes, which is attributed to the high surface areas of the nanomaterials and the high level of dispersion in the liquids. Overall, the results indicate that these nanomaterials may have the potential to be employed for a range of applications to extract radionuclides from aqueous solutions. These applications may include analytical chemistry, waste water treatment and remediation, mining, and in vitro radiobioassay.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Computational design of CO-tolerant Pt 3 M anode electrocatalysts for proton-exchange membrane fuel cells

CO is a typical contaminant in hydrogen fuel produced via steam reforming. Development of a CO-tolerant Pt-based anode for the proton-exchange membrane fuel cells (PEMFC) is of great interest to avoid catalyst deactivation caused by strong CO adsorption on Pt. Pt–Ru, Pt–Mo, and Pt 3 Sn are three known Pt-based bimetallic CO-tolerant anode materials. The enhanced CO-tolerance of these alloys has been generally attributed to a bifunctional effect enabling oxidative CO removal at low applied potential and a ligand effect between the two metals which weakens CO bonding and reduces CO surface coverage. In this study, we use density functional theory to calculate surface adsorption states under reaction conditions for Pt-based alloys. Our calculations provide a molecular-level understanding of the enhanced CO-tolerance of Pt alloys induced by the second metal. We find that Mo and Sn dopants promote CO-tolerance by reducing CO surface coverage and enabling CO oxidative removal at low applied potential. In contrast, Ru is not able to promote CO electro-oxidation at the low applied potential of the operating conditions of a PEMFC anode. The CO-tolerance induced by Ru is attributed to the reduction of CO coverage on Pt sites. Based on these mechanisms, we carried out a computational screening of Pt 3 M electrocatalysts for CO-tolerant PEMFC anodes. A number of promising candidates have been identified for experimental examination.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Outer membrane vesicles catabolize lignin-derived aromatic compounds in Pseudomonas putida KT2440

Lignin is an abundant and recalcitrant component of plant cell walls. While lignin degradation in nature is typically attributed to fungi, growing evidence suggests that bacteria also catabolize this complex biopolymer. However, the spatiotemporal mechanisms for lignin catabolism remain unclear. Improved understanding of this biological process would aid in our collective knowledge of both carbon cycling and microbial strategies to valorize lignin to value-added compounds. Here, we examine lignin modifications and the exoproteome of three aromatic–catabolic bacteria: Pseudomonas putida KT2440, Rhodoccocus jostii RHA1, and Amycolatopsis sp. ATCC 39116. P. putida cultivation in lignin-rich media is characterized by an abundant exoproteome that is dynamically and selectively packaged into outer membrane vesicles (OMVs). Interestingly, many enzymes known to exhibit activity toward lignin-derived aromatic compounds are enriched in OMVs from early to late stationary phase, corresponding to the shift from bioavailable carbon to oligomeric lignin as a carbon source. In vivo and in vitro experiments demonstrate that enzymes contained in the OMVs are active and catabolize aromatic compounds. Taken together, this work supports OMV-mediated catabolism of lignin-derived aromatic compounds as an extracellular strategy for nutrient acquisition by soil bacteria and suggests that OMVs could pote

biological funneling↗
DOE OSTI2024

Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.

Tayal, Kshitij↗
DOE OSTI2021

How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework

Abstract This work explores the dependability tradeoffs provided by the most common types of central power plants in the United States. Historically, the electricity sector has lacked consensus on how reliability , resilience , and vulnerability differ and how those metrics change depending on the power plant fleet composition. We propose distinct definitions for these metrics and an analytical framework to evaluate power plant fleet dependability. Using data analysis and literature review, we identify fifteen dependability attributes across which we rank eleven power plant types relative to natural gas combined-cycle (NGCC) plants. We use NGCC as the benchmark because it is common to many locations and is of relatively recent vintage. The framework shows that each power plant type has unique dependability benefits and drawbacks. We provide examples of how researchers may use the framework to evaluate grid dependability qualitatively under different scenarios. We find that assuming all attributes that contribute to grid dependability are equally important and additive, electric grid dependability is best supported when power plant fleets include a mixture of power generation technologies. Then, we discuss scenario characteristics that could alter the prioritization and relationships of attributes. We also find that if current capacity installation trends continue to favor low- and zero-carbon power plants, US power grids may benefit from increased resilience and reduced vulnerability at the cost of decreased reliability. We conclude by recommending methods for adapting the framework and quantifying relationships between attributes in individual scenarios.

Ramirez-Meyers, K. (ORCID:0000000291216952)↗
DOE OSTI2023

Laboratory investigation of hydraulic fracturing in granitic rocks using active and passive seismic monitoring

SUMMARY Knowledge of the fracturing processes can be important for the optimization of pressurized fluid injection operations in the deep underground rock mass. Active and passive seismic monitoring techniques have been used in the field for tracking or mapping the propagating hydraulic fracture. Although both these monitoring techniques provide valuable information about the generated fracture network, it is difficult for either technique to comprehensibly identify the different processes associated with hydraulic fracturing. The combined active and passive monitoring has the potential for better characterization of the complex hydraulic fracturing phenomena. In this study, laboratory hydraulic fracturing experiments with combined active and passive seismic monitoring were conducted on true triaxially loaded Barre granite cubes with different fluid injection rates. The seismic inelastic fracturing was detected by 16 passive acoustic emission sensors, where 3678 and 2370 seismic source events were detected for the high and low injection rate experiments, respectively. For active monitoring, strong variations in the attributes of signals were observed which were transmitted through four source–receiver pairs, placed both perpendicular and parallel to the generated hydraulic fracture. Positive velocity changes were observed for active sensor pairs with ray paths passing through the generated hydraulic fracture indicating fluid permeation, whereas isolated dry deformation was characterized by a slight but permanent velocity decrease. Compared to velocity, the energy of the active signals was 1–2 orders of magnitude more sensitive to different hydraulic fracturing processes. However, the sensitivity and signatures of the active signal attributes were found to be dependent on the frequency range and direction of ray path with respect to the location of the generated fracture network. Using the coupled evaluation of the active and passive signals we were able to systematically identify various hydraulic fracturing processes including: (1) aseismic deformation, (2) fracture initiation and fluid permeation, (3) pressure build-up, (4) fracture propagation and (5) pressure release and leak-off. The results of this study showed that combining the respective advantages of active and passive seismic techniques and using both of them to monitor the failure processes can facilitate a more comprehensive understanding and better control of the hydraulic stimulations in subsurface operations.

Geochemistry & Geophysics↗