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At least 577 records · Page 32

Identifying rhenium substitute candidate multiprincipal-element alloys from electronic structure and thermodynamic criteria

While rhenium has proven to be an ideal material in fast-cycling high-temperature applications such as rocket nozzles, its prohibitive cost limits its continued use and motivates a search for viable cost-effective substitutes. We show that a simple design principle that trades off average valence electron count and cost considerations proves helpful in identifying a promising pool of candidate substitute alloys: The Mo–Ru–Ta–W quaternary system. We demonstrate how this picture can be combined with a computational thermodynamic model of phase stability, based on high-throughput ab initio calculations, to further narrow down the search and deliver alloys that maintain rhenium’s desirable hcp crystal structure. Here, this thermodynamic model is validated with comparisons to known binary phase diagram sections and corroborated by experimental synthesis and structural characterization demonstrating multiprinciple-element hcp solid-solution samples selected from a promising composition range.

36 MATERIALS SCIENCE↗

Genome, transcriptome and secretome analyses of the antagonistic, yeast-like fungus Aureobasidium pullulans to identify potential biocontrol genes

Aureobasidium pullulans is an extremotolerant, cosmopolitan yeast-like fungus that successfully colonises vastly different ecological niches. The species is widely used in biotechnology and successfully applied as a commercial biocontrol agent against postharvest diseases and fireblight. However, the exact mechanisms that are responsible for its antagonistic activity against diverse plant pathogens are not known at the molecular level. Thus, it is difficult to optimise and improve the biocontrol applications of this species. As a foundation for elucidating biocontrol mechanisms, we have de novo assembled a high-quality reference genome of a strongly antagonistic A. pullulans strain, performed dual RNA-seq experiments, and analysed proteins secreted during the interaction with the plant pathogen Fusarium oxysporum. Based on the genome annotation, potential biocontrol genes were predicted to encode secreted hydrolases or to be part of secondary metabolite clusters (e.g., NRPS-like, NRPS, T1PKS, terpene, and β-lactone clusters). Transcriptome and secretome analyses defined a subset of 79 A. pullulans genes (among the 10,925 annotated genes) that were transcriptionally upregulated or exclusively detected at the protein level during the competition with F. oxysporum. These potential biocontrol genes comprised predicted secreted hydrolases such as glycosylases, esterases, and proteases, as well as genes encoding enzymes, which are predicted to be involved in the synthesis of secondary metabolites. This study highlights the value of a sequential approach starting with genome mining and consecutive transcriptome and secretome analyses in order to identify a limited number of potential target genes for detailed, functional analyses.

transcriptome↗

Peripheral Blood Transcript Signatures after Internal 131 I-mIBG Therapy in Relapsed and Refractory Neuroblastoma Patients Identifies Early and Late Biomarkers of Internal 131 I Exposures

131 I-metaiodobenzylguanidine ( 131 I-mIBG) is a targeted radiation therapy developed for the treatment of advanced neuroblastoma. We have previously shown that this patient cohort can be used to predict absorbed dose associated with early 131 I exposure, 72 h after treatment. We now expand these studies to identify gene expression differences associated with 131 I-mIBG exposure 15 days after treatment. Total RNA from peripheral blood lymphocytes was isolated from 288 whole blood samples representing 59 relapsed or refractory neuroblastoma patients before and after 131 I-mIBG treatment. We found that several transcripts predictive of early exposure returned to baseline levels by day 15, however, selected transcripts did not return to baseline. At 72 h, all 17 selected pathway-specific transcripts were differentially expressed. Transcripts CDKN1A (P < 0.000001), FDXR (P < 0.000001), DDB2 (P < 0.000001), and BBC3 (P < 0.000001) showed the highest up-regulation at 72 h after 131 I-mIBG exposure, with mean log2 fold changes of 2.55, 2.93, 1.86 and 1.85, respectively. At day 15 after 131I-mIBG, 11 of the 17 selected transcripts were differentially expressed, with XPC, STAT5B, PRKDC, MDM2, POLH, IGF1R, and SGK1 displaying significant up-regulation at 72 h and significant down-regulation at day 15. Interestingly, transcripts FDXR (P = 0.01), DDB2 (P = 0.03), BCL2 (P = 0.003), and SESN1 (P < 0.0003) maintained differential expression 15 days after 131 I-mIBG treatment. These results suggest that transcript levels for DNA repair, apoptosis, and ionizing radiation-induced cellular stress are still changing by 15 days after 131 I-mIBG treatment. Our studies showcase the use of biodosimetry gene expression panels as predictive biomarkers following early (72 h) and late (15 days) internal 131 I exposure. Our findings also demonstrate the utility of our transcript panel to differentiate exposed from non-exposed individuals up to 15 days after exposure from internal 131 I.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Identifying Direct SP -Converted Waves Constrains Local Induced Earthquake Depths

Abstract Seismicity in southern Kansas and northern Oklahoma in the past decade has been associated with fluid injections. In southcentral Kansas, the Wellington earthquake catalog is primarily composed of local, low-magnitude events. Approximately 22% of recorded earthquakes over a 2.5 yr period exhibit a seismic phase arriving between the direct P phase and direct S phase with particle motion similar to the P wave. This intermediate phase was identified as an S to P conversion (SP phase) occurring in the sedimentary rocks instead of the hypothesized basement to sedimentary section transition. We exploit the SP-converted phases to improve the depth accuracy of shallow earthquakes and to constrain VP/VS. The revised depth calculations further confirm that these local induced earthquakes are occurring in the shallow crystalline basement, below the sedimentary section in which fluids are injected.

Geochemistry & Geophysics↗

Identifying Downtime Sources in CEBAF SRF Linac Systems for Improving Its Reliability

In this work, we will present some recent analysis of the reliability statistics of the CEBAF SRF linac systems. Based on the data collected by the existing Down Time Manager (DTM), the year-to-year downtime evolution trend of linac zones over the six-year period from FY18 to FY23 is established. An in-depth downtime analysis at a resolution beyond the linac zone level was carried out by introducing a new system hierarchy, consisting of the SRF linac sub-systems and components. This new paradigm was implemented in a pilot downtime study over the two-month period of CEBAF operation from 9/13/23 to 11/13/23, enabling localization of the responsible sub-systems (SRF, HPRF, LLRF, Beamline vacuum, Cryogenics, etc.) and hardware components (cavity, tuner, RF coupler, etc.) in the CEBAF SRF linac systems. Pinpointing downtime sources over long operation periods at the sub-systems and component levels holds the key to improving the CEBAF SRF systems reliability. It also helps identify areas of SRF technology development needed by future high-power high-reliability CW SRF linacs.

Hryhorenko, Oleksandr↗

Use of a New Analytical Model To Match Production Data and Identify Opportunities To Maximize Well Productivity in the Tuscaloosa Marine Shale Reservoir

The low productivity of the oil wells in the Tuscaloosa Marine Shale (TMS) Trend, located in Louisiana and Mississippi, is a mystery. Production data from 55 wells in the TMS Trend were analyzed to identify possible means of enhancing well productivity. The -1/2 slope in a log–log plot for reservoir linear flow (RLF) was observed for some wells, but not all, and the -1 slope for boundary–dominated flow (BDF) has not been seen in the past 5 years of production. The behavior of the TMS wells is attributed to the ultralow permeability of the TMS matrix and the decline of fracture conductivity, both of which delay the BDF. On the basis of the concept of the distance of investigation, derived from the radius–of–investigation concept, the matrix permeability of the seven TMS wells was estimated to be between 34 and 65 nd, with an average of 51 nd. A new mathematical model for production decline of multifractured horizontal wells was developed taking into consideration the time–dependent fracture conductivity during the BDF period. This model fits TMS–well production data, with an average error of 6.12% and an R2 value of 0.96. Assuming constant matrix permeability, fitting the new model to the production data in the BDF period gives a rate of fracture–conductivity decline of between 0.20 and 0.74% per month. The new mathematical model reveals opportunities to optimize well completion to enhance well productivity in the TMS Trend. For a given amount of fracture proppant allocated to a well, the shortest possible fracture spacing should be used to maximize well productivity. If the number of fractures is fixed as a constraint of well completion design, well productivity is inversely proportional to the square root of fracture spacing (i.e., if the fracture spacing is shortened by fourfold, well productivity is expected to double). If the horizontal wellbore length is fixed as a constraint of well completion, well productivity is inversely proportional to fracture spacing (i.e., if the fracture spacing is shortened by 50%, well productivity is expected to double).

Engineering↗

Curvature Based Analysis to Identify and Categorize Trajectory Segments

Since the attacks carried out against the United States on September 11, 2001, which involved the commandeering of commercial aircraft, interest has increased in performing trajectory analysis of vehicle types not constrained by roadways or railways, i.e., aircraft and watercraft. Anomalous trajectories need to be automatically identified along with other trajectories of interest to flag them for further investigation. There is also interest in analyzing trajectories without a focus on anomaly detection. Various approaches to analyzing these trajectories have been undertaken with useful results to date. In this research, we seek to augment trajectory analysis by carrying out analysis of the trajectory curvature along with other parameters, including distance and total deflection (change in direction). At each point triplet in the ordered sequence of points, these parameters are computed. Adjacent point triplets with similar values are grouped together to form a higher level of semantic categorization. These categorizations are then analyzed to form a yet higher level of categorization which has more specific semantic meaning. This top level of categorization is then summarized for all trajectories under study, allowing for fast identification of trajectories with various semantic characteristics.

42 ENGINEERING↗

Identifying Suspect Instrument Intervals Using Midnight Noise Time Histories

With an intense work ethic, and high levels of devotion to their craft, seismologists continue to battle the elements and wrestle with complex logistics in their efforts to field instrumentation over the entire globe. We are fortunate to see data volumes accelerating in size, and quality continuing to improve; for example, universal timing issues can now be considered rare. However, instrument response issues are still common, and can hinder research that seeks to understand amplitudes of seismic wavefields. We are interested in using the accumulations of seismic data to develop models that will predict high frequency (0.2-20 Hz, and higher) signal amplitudes for explosion monitoring purposes over broad areas, which requires large data sets and extensive quality control. To identify response and station health issues, we have collected noise time histories for global seismic data, focusing on measurements near (but not restricted to) midnight to eliminate diurnal variations, and have manually determined time intervals that appear inconsistent with background behavior. We assign descriptive labels, but do not attempt to diagnose causes. We use these intervals to discard data. To date, we have examined 39,260 channels from 11,105 stations, heavily weighted toward IRIS holdings, dates through 2017 (depending on station, roughly the date we started our manual review), bands between 1 and 8 Hz, finding 24,733 anomalous time intervals. The great majority (90%) of these intervals appear to be shifts of constant offset, often bounded by times of known instrument changes, likely the result of poor documentation of response parameters at one of many stages between the field and the plotter. We hope these results can be of use to our colleagues, and would encourage community efforts to diagnose anomalous behavior, and fix poor responses. We also hope these results will support automation efforts, including application of supervised learning techniques.

47 OTHER INSTRUMENTATION↗

Machine learning to identify geologic factors associated with production in geothermal fields: A casestudy using 3D geologic data, Brady geothermal field, Nevada

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in the Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid flow pathways are relatively rare in the subsurface but are critical components of hydrothermal systems like Brady and many other types of fluid flow systems in fractured rock. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of fourteen 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate the macro-scale faults and a local step-over in the fault system preferentially occur along with production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate 1) the specific geologic controls on the Brady hydrothermal system and 2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

15 GEOTHERMAL ENERGY↗

Aging and Lifetimes Milestone 7777, G.C. #4: Assess the ability of MRI to identify chemical and mechanical heterogeneity in polymers at various length scales

Polysiloxane foams utilized for stockpile applications experience changes over time induced by exposure to thermal, radiative, and mechanical stresses. The interplay of these stresses can lead to chemical and/or physical changes in the polymer, such as compression set and material degradation over time. Understanding the mechanisms of material aging and identifying material degradation prior to failure are critical in maintaining the integrity of the nuclear stockpile. Our prior work using solid-state NMR spectroscopy has shown that we can observe signatures of thermal, radiative, and mechanical aging of silicone elastomers using differences in NMR parameters such as relaxation (T1 and T2). However, most of our measurements have been on bulk samples and have only provided an average aging behavior of each sample. Here, we have used magnetic resonance imaging (MRI) to provide relaxation-weighted images that are a metric of structural heterogeneity (cross-link density) in polymers across multiple length scales. In addition, the larger sample size that can be accommodated in animal MRI scanners has the potential to make the technique adaptable to detecting heterogeneities whole parts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

PV System Owner's Guide to Identifying, Assessing, and Addressing Weather Vulnerabilities, Risks, and Impacts

The report is designed to help federal facility managers, engineers, contractors, and consultants identify the most common PV system vulnerabilities that could lead from mild to catastrophic loss of PV systems during severe weather events. The guide helps users understand the type and severity of severe weather that can occur at any given location, and provides pre- and post-storm operations and maintenance (O&M) measures that can reduce the potential for damage to a PV system during a severe weather event and help speed the recovery of a PV system damaged during a storm. The guide emphasizes safety in responding to damage from severe weather events.

14 SOLAR ENERGY↗

Using Machine Learning to Identify Optimum Prospects for Offshore Geologic CO 2 Storage and Enhanced Oil Recovery in the Gulf of Mexico

The SECARB Offshore Partnership is a government-industry partnership focused on assembling the knowledge base required for secure, long-term, large-scale CO 2 subsea storage in saline formations and mature oil reservoirs. The SECARB Offshore Partnership is evaluating potential storage opportunities in the Central and Eastern planning areas of the outer continental shelf (OCS) of the Gulf of Mexico. The evaluation focuses on active and depleted oil and gas fields and potentially associated CO 2 -enhanced oil recovery (CO 2 -EOR), as well as, deep saline storage resources. As part of Subtask 3.0 (Offshore Storage Resources Characterization) and Subtask 4.0 (Risk Assessment, Simulation, and Modeling), Oklahoma State University (OSU) is developing a machine learning system that employs the SAS Institute’s Viya platform with visual data mining and machine learning to identify and characterize optimum prospects for enhanced oil recovery and stacked storage in saline reservoirs. This report summarizes this effort to date and discusses the many variables that can be used to characterize these storage objectives, as well as, the design vision of the Viya machine learning system.

02 PETROLEUM↗

Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.

As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly important. Uncertainty quantification (UQ) provides important information about the ability of a learned model to make sound predictions, often with respect to individual test cases. However, most UQ methods for ML are themselves data-driven and therefore susceptible to the same knowledge gaps as the models themselves. Specifically, UQ helps to identify points near decision boundaries where the models fit the data poorly, yet predictions can score as certain for points that are under-represented by the training data and thus out-of-distribution (OOD). One method for evaluating the quality of both ML models and their associated uncertainty estimates is out-of-distribution detection (OODD). We combine OODD with UQ to provide insights into the reliability of the individual predictions made by an ML model.

97 MATHEMATICS AND COMPUTING↗

Identifying Indicators of Sociopolitical Instability in Iran [Slides]

Introduction: Socio Political Instability - Defined as “the likelihood that a government will be overthrown by unconstitutional or violent means, including social unrest, violence, hostility, or terrorism." Global protests and demonstrations have increased 244% in the last decades. Better understanding of the drivers and motivations behind this increasing instability may help inform preventative or de-escalation measures. Identifying measurable indicators and drivers of sociopolitical unrest is the first step in accomplishing this task and may aid with event forecasting through modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Identifying Regions Favorable for Geothermal Heating and Cooling Storage

Space heating and cooling represents the single largest category of in building energy use for U.S. residential and commercial buildings, with heating representing 61% of residential and 46% commercial energy consumption. Building heating technologies are dominated by natural gas technologies, and are an important opportunity for building electrification to enable a transition to a low CO 2 energy system. FLXenabler study is a joint analysis effort among multiple analysis teams at NREL and focused on examining the role of geothermal heating and cooling (GHC) system with thermal energy storage (TES) providing flexibility. Utilizing information from ResStock the amount of energy consumption associated with heating and cooling by state was calculated. We applied adjusted load shapes to estimate the a maximum grid savings potential of using TES to address building space conditioning. Normalizing grid, fuel, and emissions impacts locations with higher favorability for further study in FLXenabler were identified.

15 GEOTHERMAL ENERGY↗

Identifying Northern Hemisphere Stratospheric and Surface Temperature Responses to the Mt. Pinatubo Eruption within E3SMv2-SPA

The Mt. Pinatubo eruption on 15 June 1991 is often associated with surface warming in the subsequent Northern Hemisphere winter. Employing E3SMv2 with prognostic aerosol modifications, we generated an ensemble of simulations initialized on 1 June 1991 to limit the intra-ensemble variability at the time of the eruption and a more traditional ensemble representing the full range of intra-ensemble variability. For each ensemble member we generated a paired counterfactual simulation with the Pinatub forcing removed allowing for isolation of the Pinatubo impact. In general, the limited variability ensemble has greater coherence in the Pinatubo impact across ensemble members which leads to more statistically robust signals compared to the full variability ensemble. Stratospheric warming patterns from Pinatubo were approximately zonally symmetric and confined between 30°S and 50°N. Isolating localized surface temperature impacts was more difficult, but the limited variability simulation did identify a preferential region of cooling between 20°S to 50°N.

54 ENVIRONMENTAL SCIENCES↗

Identifying the connections between grain growth and flux expulsion in low RRR niobium SRF cavities

The SRF community has shown that high temperature annealing can improve the flux expulsion of niobium cavities during cooldown. The required temperature will vary between cavities and different batches of material, typically around 800 C and up to 1000 C. However, for niobium with a low residual resistance ratio (RRR), even 1000 C is not enough to improve its poor flux expulsion. The purpose of this study is to observe the grain growth behavior of low RRR niobium coupons subjected to high temperature annealing to identify the mechanism for improving flux expulsion. We observe that low RRR material experiences less grain growth than high RRR when annealed at the same temperature. We search for the limitations to grain growth in low RRR material and develop a diagnostic based on grain structure to determine the appropriate recipe for good flux expulsion. The results of this study have the potential to unlock a new understanding on SRF materials and enable the next generation of high Q/high gradient surface treatments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Identifying Hot Spots and Hot Moments of Metabolic Activity in Salt Marsh Sediments through BONCAT-FISH Microscale Mapping (Final Technical Report)

Understanding the biogeography and timing of microbial metabolic activity is a key priority for microbial ecologists. With such knowledge, the cumulative biogeochemical contributions of microbial communities become more predictable, and our ability to both understand the effects of environmental change on microbial activity and build synthetic communities with desired functions will increase substantially. In this project, we advanced this broad, ambitious goal in two substantial ways: we developed a multiplexed Fluorescence In Situ Hybridization (FISH) approach that links microbial identity with metabolic function, and we established a novel Bio-Orthogonal Non-Canonical Amino acid Tagging (BONCAT) technique that can resolve the timing of anabolic activity, providing new resolution of when microbial constituents are growing. We deployed both of these techniques in multiple environmental settings to demonstrate their versatility. At the Little Sippewissett Salt Marsh, we combined a novel dual-BONCAT technique with fluorescence activated cell sorting to determine which population of cells was metabolically active during the day and which was active during the night. We found that Methylobacterium was active during daylight hours, potentially feeding on carbon-rich molecules released from plant roots. Sulfur-cycling microbes dominate the population active during the dark night-time hours. Overall, the work conducted under the auspices of this project developed two promising new techniques for identifying the “hot spots” and “hot moments” of microbial activity in complex communities with taxonomic and functional resolution. We focused largely on a salt marsh sediment context, but are confident that microbial ecologists seeking to understand the microbial role in biogeochemical cycles in a wide range of settings will find our newly developed techniques useful in future work.

54 ENVIRONMENTAL SCIENCES↗