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At least 991 records · Page 55

Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions

Large-scale and openly available material science databases are mainly composed of computer simulation results rather than experimental data. Some examples include the Materials Project, Open Quantum Materials Database, and Open Catalyst 2022. Unfortunately, building large-scale experimental databases remains challenging due to the difficulties in consolidating locally distributed datasets. In this work, focusing on the catalysis literature of CO 2 reduction reactions (CO 2 RRs), we present a machine learning (ML)-based protocol for selecting highly relevant papers and extracting important experimental data. First, we report a document embedding method (Doc2Vec) for collecting papers of greatest relevance to the specific target domain, which yielded 3154 CO 2 RR-related papers from six publishers. Next, we developed named entity recognition (NER) models to extract twelve entities related to material names (catalyst, electrolyte, etc.) and catalytic performance (Faradaic efficiency, current density, etc.). Further, among several tested models, the MatBERT-based approach achieved the highest accuracy, with an average F1-score of 90.4% and an F1-score of 95.2% in a boundary relaxation evaluation scheme. The accurate and accelerated NER-based data extraction from a large volume of catalysis literature enables temporal trend analyses of the CO 2 RR catalysts, products, and performances, revealing the potentially effective material space in CO 2 RRs. While this work demonstrates the effectiveness of our ML-based text mining methods for specifically CO 2 RR literature, the methods and approach are applicable to and may be used to accelerate the development of other catalytic chemical reactions.

36 MATERIALS SCIENCE↗

AmeriFlux CA-DB2 Delta Burns Bog 2

This is the AmeriFlux version of the carbon flux data for the site CA-DB2 Delta Burns Bog 2. Site Description - The Burns Bog Ecological Conservancy Area (BBECA) in the metro Vancouver area presents a unique opportunity for insight into greenhouse gas dynamics post restoration. The bog was significantly disturbed for peat mining, development and agriculture over the last century, however since 2005 there has been both active restoration and natural regeneration occurring within the bog. Hence areas of bog are at varying stages of ecological recovery. This site is a Pine-Sphagnum low shrub site characteristic of a later successional stage. Conversely, CA-DBB, another AmeriFlux site in BBECA, is a Beakrush-Sphagnum site characteristic of an earlier successional stage.

Knox, Sara↗

Impacts of a Prescribed Fire on Air Quality in Central New Mexico

A short-duration but high-impact air quality event occurred on 28 November 2018 along the Rio Grande Valley of New Mexico. This fire occurred outside the typical wildfire season, and greatly impacted the air quality in Socorro, NM, and the surroundings. Measurements were taken during the event using an aerosol light scattering technique (integrating nephelometer) and a particulate mass concentration monitor (DustTrak PM optical monitor). The instruments sampled the ambient air during the event on the campus of the New Mexico Institute of Mining and Technology in Socorro, New Mexico. The peak values on a 5-min basis of light scattering and the PM mass concentration reached 470 Mm -1 and 270 µg/m 3 , respectively. We examined the meteorological context of the event using local meteorological data and back trajectories using the NOAA HYSPLIT model to determine atmospheric transport and possible sources. Several fires, both prescribed and wildfires, occurred in the region including a prescribed burn at Bosque del Apache National Wildlife Refuge (17 km south-southeast of the receptor site). The data suggest that the prescribed burn at Bosque del Apache was the dominant contributor due to transport evidence and the event’s narrow spatiotemporal extent. The increasing importance of restoring ecosystem function using prescribed fire in wildland fire management will likely lead to more frequent air quality impacts and sets up policy tradeoffs that require a balance between these public goals. This study examines the evidence of the effects of a prescribed fire in a protected wildland area impacting the air quality in a nearby populated area.

54 ENVIRONMENTAL SCIENCES↗

Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

59 BASIC BIOLOGICAL SCIENCES↗

Discovering Blind Geothermal Systems in the Great Basin Region: An Integrated Geologic and Geophysical Approach for Establishing Geothermal Play Fairways: All Phases

Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada. These parameters were grouped into subsets and individually weighted to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis. After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley, deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.

15 GEOTHERMAL ENERGY↗

RARE EARTH AND CRITICAL ELEMENT RELATIONSHIPS TO COAL DEPOSIT LITHOLOGIES IN SOUTHWEST WYOMING AND NORTHWEST COLORADO

Rare earth elements (REE) and critical minerals (CM) are vital components in a wide variety of applications, not only in the renewable energy sector but also in military, telecommunication, and other civilian uses. The association of REE and CM with coal deposits is widely known but not thoroughly understood. While individual coal samples or beds themselves might not host significant concentrations of REE, recent studies indicate temporal relationships among some REE with coal beds and their immediately adjacent non-coal bounding strata, including interbeds. These strata most commonly include clays, carbonaceous shales, and ash beds, but other related strata are possible in different coal-bearing settings. Variations in REE and CM concentrations and their direct or indirect association with coal beds are not always predictable. This study presents recent findings from studying different associations between coal beds and adjacent rocks to identify possible relationships between REE-CM concentrations and the coals and adjacent rocks. For this study we collected and analyzed several dozen samples from the U.S. Geological Survey’s Core Research Center (CRC). Additionally, a small number of coal mine outcrops and their adjacent non-coal rocks were included. Most samples are of Cretaceous age. Previous work has shown a potential link between coals in the Upper Cretaceous Williams Fork Formation and REE occurrences in the Yampa ash bed in northwest Colorado. While data are uncommon and sparse, this possible link may suggest REE or CM enrichment from leaching or reworking of the ash bed. Trace element geochemistry, whole rock data, and microscopic examinations are used here to explore these relationships and guide future studies.

REE, CM, ash, volcanic ash, coal↗

Hyaloscypha finlandica Metabolome Repository

This repository provides the curated data tables, manuscript figure and table exports, dependency records, and workflow scripts supporting an integrated comparative genomics and untargeted LC-MS/MS metabolomics analysis of Hyaloscypha finlandica strain PMI 746, a root-associated dark septate endophyte of poplar. The repository includes genome-mining summaries from antiSMASH, FunBGCeX, BGC-Prophet, and BiG-SCAPE; processed metabolomics inputs; metabolite annotation evidence; statistical outputs; and publication-facing figures and tables. Raw LC-MS/MS spectra, full genome/protein downloads, and large generated tool outputs are referenced through public archive/accession records and are not stored in Git.

59 BASIC BIOLOGICAL SCIENCES↗

Digital color analysis of color-ratio composite LANDSAT scenes

A method is presented that can be used to calculate approximate Munsell coordinates of the colors produced by making a color composite from three registered images. Applied to the LANDSAT MSS data of the Goldfield, Nevada, area, this method permits precise and quantitative definition of the limonitic areas originally observed in a LANDSAT color ratio composite. In addition, areas of transported limonite can be discriminated from the limonite in the hydrothermally altered areas of the Goldfield mining district. From the analysis, the numerical distinction between limonitic and nonlimonitic ground is generally less than 3% using the LANDSAT bands and as much as 8% in ratios of LANDSAT MSS bands.

Raines, G. L.↗

Decorrelation distance of snow in the Colorado River Basin

The problem of estimating areal averages from point measurement has been extensively studied by mining engineers and hydrologists. Its application to satellite measurements has recently been introduced. The semivariaogram has been used in many geostatistical applications to estimate spatial structures of observed properties, such as mineral distributions. An examination is made of snow variations in Colorado from daily snow data collected in 11 SNOTEL stations. The associated semivariogram is estimated. The objective is to estimate the spatial structure of the snow field so that the point data can be used for comparison with, and validation for, satellite measurements.

Chang, A. T. C.↗

Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of an potential upcoming adverse event. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with a carefully designed Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture to predict different type of adverse events for any given flights and identify their precursors with little to no post-processing.Results obtained show that the MHCNN-RNN is able to accurately forecast high speed and high path angle events during the approach, and that it is also capable of determining the aircraft’s parameters that are correlated to these events. These parameters can be considered precursors to the events.

multiple instance learning↗

National Energy Water Treatment & Speciation (NEWTS): A Water & Critical Mineral Database and Dashboard

The scarcity of water resources, the need for beneficial water reuse, and the challenges of wastewater treatment are becoming increasingly pressing in economic, social, and environmental domains. Addressing these concerns requires effective treatment strategies to manage wastewater streams and tackle environmental and economic issues. Furthermore, the recovery of critical minerals from the waste streams associated with energy production holds the promise of offsetting treatment costs and securing local sources of valuable minerals. However, relevant data on these waste streams are dispersed and challenging to locate. The process of ingesting such data into modeling software often involves multiple steps, requiring data restructuring to meet software-input requirements. The non-standardized reporting of water data makes data aggregation and reformatting a time-consuming process. Additionally, essential attributes necessary for modeling water treatment and mineral scale formation are frequently missing. Moreover, data gaps vary depending on the region of interest. Consequently, there is a pressing need for high-quality energy-water composition data that can be easily imported into water chemistry modeling software. To address this need, the National Energy Technology Laboratory has created the National Energy Water Treatment and Speciation (NEWTS) Database and Dashboard—a free online tool catering to community leaders and water researchers. NEWTS facilitates a comprehensive understanding of the composition of energy-related wastewater streams in the United States. The datasets provide detailed concentrations and speciation of major and minor aqueous compounds in energy-related wastewater streams, including power plant leachate, acid mine drainage, brackish water, and oil and gas produced water across the United States. Many of the aqueous species are critical minerals (Li, REEs) in high demand to modernize the world’s energy infrastructure. Many of the datasets also contain volumetric flow-rates needed to model the treatment and reuse scenarios in advanced aqueous chemistry software programs. The NEWTS Database and Dashboard offer public access to hitherto challenging-to-access datasets, presented in a standardized format that is tailored for easy input into aqueous chemistry modeling software. By performing the work needed to transform dispersed, disparate data sources into unified, model-ready datasets, NEWTS serves as an essential resource in advancing water treatment research and sustainable water resource management.

produced water management↗

Quantitative lithologic mapping in spectral ratio feature space - Volcanic, sedimentary and metamorphic terrains

The nature of spectral lithologic mapping is studied utilizing ratios centered around the wavelength means of TM imagery. Laboratory-derived spectra are analyzed to determine the two-dimensional relationships and distributions visible in spectral ratio feature space. The spectral distributions of various rocks and minerals in ratio feature space are found to be controlled by several spectrally dominant molecules. Three study areas were examined: Rawhide Mining District, Nevada; Manzano Mountains, New Mexico; and the Sevilleta Long Term Ecological Research site in New Mexico. It is shown that, in the comparison of two ratio plots of laboratory reflectance spectra, i.e., 0.66/0.485 micron versus 1.65/2.22 microns with those derived from TM data, several molecules spectrally dominate the reflectance characteristic of surface lithologic units. Utilizing the above ratio combination, two areas are successfully mapped based on their distribution in spectral ratio feature space.

Campos-Marquetti, Raul, Jr.↗

The Stability of Nd Hydroxyl Complexes at Near Neutral to Alkaline pH from 25 to 75 °C: Implications for Monazite Solubility in Hydrothermal Aqueous Fluids

Speciation of rare earth elements (REE) in aqueous fluids controls their mobilization during fluid-rock interaction. Thermodynamic modeling provides important insights into the factors controlling REE mobility and fractionation in aqueous fluids, in which aqueous REE hydroxyl complexes are particularly important across a broad pH range. However, the stability of these REE hydroxyl complexes remains poorly determined due to limited experimental data above ambient temperature. In this work, UV–vis spectrophotometry experiments were conducted to determine the cumulative hydrolysis constants for neodymium (Nd) from 25 to 75 °C in near-neutral to alkaline solutions. The color indicator m -cresol purple was used to determine in situ pH. Alkaline NaOH-bearing aqueous solutions were doped with varying initial Nd concentrations (0 to ∼0.155 mmol/kg), which resulted in the release of protons (H + ) and pH decrease during hydrolysis of Nd 3+ according to Nd 3+ + n H 2 O = Nd(OH) n 3– n + H + . The average OH – ligand number coordinated to Nd 3+ was found to increase from 1.0 to 1.6 at 25 °C and from 1.2 to 2.8 at 75 °C over a pH range from 6.3 to 9.0. The measured speciation shows an increased predominance of Nd(OH) 3 0 over the Nd(OH) 2 + and Nd(OH) 2+ species with increased temperature and pH. The increasing cumulative formation constants (log*β° n , n = 1 to 3) retrieved from 25 to 75 °C differ by 0.1 to 1.9 logarithmic units in comparison to existing thermodynamic databases. These updated thermodynamic data have important implications for geochemical modeling of the speciation of Nd hydroxyl complexes and the solubility of monazite as a function of pH.

58 GEOSCIENCES↗

Rare earth metals from secondary sources: Review of potential supply from waste and byproducts

Current concerns about lack of diversity in supply of critical metals have spurred research into utilizing domestic sources, particularly from waste streams. Sustainability strategies like urban mining, industrial symbiosis, and the circular economy suggest avenues to realize new supplies of critical metals. In this work we explore the resource and economic potential for extracting rare earth elements (REEs) from industry byproducts (e.g. coal combustion products, red mud) and secondary sources (e.g. waste electronics and light bulbs). Combining materials flow analysis and characterization data, we find that while REE concentrations in waste and byproduct streams are mostly much lower than current REE ores, some secondary sources are richer than ores in high value REEs such as scandium. The quantities of REEs contained in secondary sources could meet current global demand even with low extraction yield rates. Phosphogypsum, coal ash and red mud from aluminum production stand out as promising candidates for recovery due to high concentrations of valuable REEs and sufficient quantities to potentially meet demand. Processes to extract REEs from secondary sources are under development, it is not clear yet which will be profitable at scale and which can be achieved at least environmental impact. This work provides high level guidance on the potential of secondary sources by characterizing quality (concentrations of different rare earths) and quantity (mass of rare earths in global scale wastes and byproducts). This significant first step helps clarify directions for policy and research and development investments.

42 ENGINEERING↗

Interpretation of Pennsylvania agricultural land use from ERTS-1 data

The author has identified the following significant results. To study the complex agricultural patterns in Pennsylvania, a portion of an ERTS scene was selected for detailed analysis. Various photographic products were made and were found to be only of limited value. This necessitated the digital processing of the ERTS data. Using an unsupervised classification procedure, it was possible to delineate the following categories: (1) forest land with a northern aspect, (2) forest land with a southern aspect, (3) valley trees, (4) wheat, (5) corn, (6) alfalfa, grass, pasture, (7) disturbed land, (8) builtup land, (9) strip mines, and (10) water. These land use categories were delineated at a scale of approximately 1:20,000 on the line printer output. Land use delineations were also made using the General Electric IMAGE 100 interactive analysis system.

Mcmurtry, G. J.↗

Evaluation of geophysical properties of the lunar regolith for the design of precursor scientific missions for the space exploration initiative

The following topics are addressed: (1) the frequency of encountering boulders that represent hazards to lunar operations; (2) the ease of lunar soil excavation; (3) the use of explosives in excavation operation; (4) the trafficability of the regolith; (5) problems encountered in mining (probably strip mining) of the regolith; (6) the stable angle(s) of repose in excavation of the regolith; (7) the layering to be encountered in the subsurface; (8) knowledge of the regolith site and the possibility of its general application to any site on the lunar surface; (9) the data needed to characterize a site for a lunar base; (10) the influence of regolith properties on the design of geophysical experiments from the lunar base; and (11) terrestrial analogues for the geophysical properties of the lunar regolith.

Morgan, Paul↗

T-FSM: A Scalable Distributed Task-Based System for Frequent Subgraph Pattern Mining from a Big Graph

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel and distributed systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, poor scale-out capability, and possibly inaccurate results. In this article, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. The execution engine of T-FSM supports both intra-machine parallelism and inter-machine parallelism. For intra-machine parallelism, T-FSM adopts a novel task-based execution model to ensure high multithreading concurrency, bounded memory consumption, and effective load balancing. For inter-machine parallelism, T-FSM ensures good scale-out performance with a lightweight pattern rebalancing approach that reduces workload skewness of pattern evaluations among machines. To avoid recomputing the contexts for migrated patterns, we design a novel context cache table to support concurrent and asynchronous requesting and caching of remote context data, which can timely evict and garbage collect used pattern contexts that are no longer needed to keep memory consumption bounded. Extensive experiments show that T-FSM is orders of magnitude faster than existing state-of-the-art parallel systems (more than 10×, 51×, 131×, 55× speedup over ScaleMine, DistGraph, Pangolin and Peregrine, respectively) and distributed systems (more than 42× and 88× over ScaleMine and DistGraph, respectively) for frequent subgraph pattern mining, and it scales out satisfactorily to 512 CPU cores on the Polaris supercomputer at Argonne National Laboratory.

97 MATHEMATICS AND COMPUTING↗

Precision Neutrino Oscillation Physics with the Daya Bay and DUNE Experiments

The team supported by this grant made significant contributions to the final results of the Daya Bay Reactor Antineutrino Experiment. This experiment utilized eight identically designed antineutrino detectors positioned at varying distances from six 2.9 GW th nuclear reactors to precisely measure the oscillation parameters that govern antineutrino disappearance at short (<2 km) baselines. Our group played a leading role in the calibration and data quality efforts, both of which have been crucial for all final results. Additionally, we co-led the development of an independent measurement of the neutrino mixing angle θ 13 and the atmospheric mass splitting using a sample of antineutrinos identified via neutron capture on hydrogen. Lastly, we laid the groundwork for a search for seasonal modulation in Daya Bay’s measured muon flux using the final dataset, a result expected to be published soon. Simultaneously, our team ramped up its participation in the Deep Underground Neutrino Experiment (DUNE). This experiment will employ a powerful neutrino beam from Fermilab in Illinois directed to the Homestake mine in South Dakota to address some of the most pressing questions in neutrino physics, including the ordering of neutrino masses and whether neutrinos violate the CP symmetry. Our work focused on the development of the pixelated and modularized Liquid Argon Time-Projection Chamber technology that is being prepared for DUNE’s Near Detector. Our group took responsibility for the development, testing, and maintenance of the firmware for the control boards of the detector’s charge readout system and played an active role in analyzing data produced by the very first fully integrated prototypes.

2x2 Demonstrator↗