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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Understanding and Overcoming Water-induced Interfacial Degradation in Si Modules (Final Technical Report)

Moisture ingress is an established issue for photovoltaic module durability. Durability studies probing moisture effects typically evaluate performance losses at the module level, attributing global power losses to the overall humidity condition of the test environment while leaving local module behavior unknown. This project successfully develops an in situ short-wave infrared probe of moisture content in PV modules with demonstrated detection limits better than 100 ppm in EVA-encapsulated Al-BSF and PERC architectures (66 µg/cm3) and applicability across the range of terrestrial conditions. Combining this optical moisture quantification method (water reflectometry detection, WaRD) with peel testing of interfaces, biased photoluminescence imaging of performance, and first principles computation, we correlate module moisture content and cell performance over the course of accelerated damp heat tests. By performing multi-level humidity and thermal accelerated testing over thousands of hours (0, 65 and 85% RH and 25, 65, and 85°C), we are able to attribute effects of individual environmental stresses and the dependence on module architecture. Using first-principles computational chemistry, we find that the decomposition of EVA to acetic acid (Norrish II) is thermodynamically preferred over a pathway to acetaldehyde (Norrish I), elucidating the underlying chemistry of encapsulant degradation. We also find it is likely that water segregates to the stable interfaces of the cell, especially the SiNx/EVA interface. We find that moisture is strongly correlated with reduction in adhesion at the interfaces of the cells, particularly at Ag fingers. We show uniquely that the peel strength required to delaminate the encapsulant from the front of the cell depends not only on the exposure to a high heat and humid environment, but also to the moisture content during the mechanical testing, indicating an important interaction between when mechanical stresses are faced in terms of the moisture content of the module and its durability. We find that the background sheet resistance (that is, the sheet resistance rise not attributable to finger interruptions or cracks) is substantially higher in the backsheet mini-modules vs glass-glass packages when humidity and temperature are faced. This increase in power loss due to the background resistance increase over time in damp heat in glass-backsheet modules resulted in ~3-4% larger decrease in relative PCE compared to glass-glass modules. In glass-backsheet modules, the effect of the moisture dose alone is comparable or greater than that of the combined temperature and water term, suggesting that glass-backsheet modules are overall more susceptible to moisture induced performance loss. Finally, we show that thin film PV modules are amenable to WaRD measurement, based on the ability to detect moisture in POE encapsulants or in the cell stack itself. The ease of WaRD measurement and its few requirements on the bill of materials should enable broad applicability of this technique for studying the effects of moisture in PV.

14 SOLAR ENERGY↗
DOE OSTI2022

High-Silicon Steel Strip by Single-Step Shear Deformation Processing

It is well known that Fe-Si alloys with Si content higher than in conventional electrical sheet steels (>3.2% Si) can make a significant impact in improving the efficiency of electrical motors if they are available in sheet/foil (strip) forms at suitable cost. While the magnetic and electrical attributes (e.g., resistivity, core loss) of these high-Si Fe alloys, of relevance to electrical motor core laminations, are known to be exceptional, the alloys have limited workability, making them difficult to produce consistently in sheet/foil (strip) forms. Current processing techniques such as rolling, while adequate for producing conventional electrical steel sheet, do have important disadvantages - large energy consumption and emissions, limitations in processing of low-workability alloys (e.g., high-Si content steels), large-scale plant infrastructure, and less than adequate capability to engineer sheet metals with specific microstructures (e.g., fine-grained) and crystallographic textures (e.g., shear textures). It is therefore of interest to have an alternative commercial process that can produce sheet/foil (strip) from high-Si Fe alloys and which can also overcome some of the deficiencies of current multistage strip processes. The goal of the present project was design and demonstration of a new energy-efficient pilot process for producing high-Si electrical steel strip of commercial widths and thickness, and with superior electrical and magnetic properties than current electrical steels (Fe-3.2% Si as benchmark). The applications domain for these steels is electrical motor core laminations. We have addressed this goal by accomplishment of the following specific objectives and tasks: a) Development of an Fe-4Si-4Cr alloy with electrical resistivity >80 μΩ-cm, induction flux density >1.48 T at 5000 A/m and core loss 35% lower than the benchmark 3.2% Si alloy. The alloy which meets DOE target specifications for motor core attributes was designed with the Si content controlled for the electrical properties and the Cr content tailored to meet material/process workability requirements. b) A unique machining-based deformation processing system was designed and scaled-up to produce strip of commercial width (25 mm to 150 mm) and thickness (up to 0.5 mm) from the Fe-4Si-4Cr alloy and other alloys of varied workability including copper, Al6061-T6 and naval brass. The key attributes of the machining-based strip production are deformation processing by concentrated simple-shear; single-step production of strip from ingot using compact machine infrastructure; strip surface finish of Ra 0.35 to 1 micrometer that is comparable/superior to that of rolled strip; discrete production of strip that can potentially be done at point of use; and controllability of strip mechanical/formability properties by deformation control. c) The electrical, magnetic, surface quality, mechanical, formability, and metallurgical properties/attributes of the machining-based strip were established by direct ASTM standard or equivalent measurement techniques. d) Punching characteristics of the strip in terms of load, edge quality and macro defects were similar to those of conventional 3.2% Si electrical steels. These punching characteristics are critical from a manufacturability perspective for motor/transformer core applications. e) A modeling framework for energy analysis of multistage rolling and the machining-based deformation processing has been established. Application of this modeling to the two strip-processes showed that the machining-based process requires significantly lower specific energy for processing, ~ 25% of that for rolling. The modeling framework can be adapted for a range of sheet-metal forming, bulk metal forming, and machining processes. It can be used to identify key parameters controlling process specific energy. f) A comparative analysis of advantages and disadvantages of machining-based processing against rolling for strip production. The single stage machining-based processing, with compact infrastructure, represents a new manufacturing paradigm for sheet and foil manufacturing that can potentially also be applied to advanced titanium, aluminum, copper and magnesium alloys. The goals and objectives were accomplished by a cross-disciplinary project team comprising of personnel from Purdue University; M4 Sciences LLC, a small-business focused on advanced manufacturing technology development; the Pacific Northwest National Labs; and tool manufacturers. The team is currently in advanced discussions with multiple entities for future process development for commercialization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

CLimate Impact: Determining Etiology thRough pAthways (CLDERA)

Climate impacts have broad economic, health, political, and national security ramifications. Societally relevant impacts are typically farther downstream, are the product of multiple interacting processes, and can arise over small regions and timeframes because their sources are short-term and localized. Short-term forcings (as can be seen in volcanic eruptions, climatic tipping points (e.g., the collapse of rainforests or the disappearance of sea ice), or in increasingly plausible climate interventions) fundamentally possess low signal-to-noise and could benefit from accounting for the multiple conditional processes through which a downstream impact arises. Under the Grand Challenge LDRD CLDERA (CLimate impacts: Discovering Etiology thRough pAthways), we have developed tools to enable downstream impact attribution from geographically and temporally localized source forcings in the climate. CLDERA developed methods that can distinguish how a localized source drives the climate system to respond with particular impacts. The how is embodied in pathways – the spatio-temporally evolving chain of physical processes that connects a source to a series of increasingly distant impacts. Novel analytic methods in pursuit of downstream impact attribution were developed and demonstrated on simulations and observations of the 1991 eruption of Mt. Pinatubo in the Philippines. As described within this report we have • developed stratospheric expertise and aerosol modeling capabilities in E3SM, • created original methods to detect and model pathways from source-to-impact, and • advanced climate attribution through novel methods, cases, and approaches. Further, CLDERA developed a tiered verification process consisting of controlled datasets to prototype, verify, and refine the original method development. CLDERA increased Sandia’s footprint in the climate analytics community and developed new climate collaborations whilst also creating a cadre of climate analysts at Sandia. The products from CLDERA have been extensive with a total of 9 journal articles published, 12 articles submitted and under review, and an additional 8 articles in preparation. We have produced 1750 simulated years and developed 9 code-bases. This report details these accomplishments and serves as a summary of the work completed during the CLDERA Grand Challenge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Likeness: a toolkit for connecting the social fabric of place to human dynamics

The ability to produce richly-attributed synthetic populations is key for understanding human dynamics, responding to emergencies, and preparing for future events, all while protecting individual privacy. The Likeness toolkit accomplishes these goals with a suite of Python packages: pymedm/pymedm\_legacy, livelike, and actlike. This production process is initialized in pymedm (or pymedm\_legacy) that utilizes census microdata records as the foundation on which disaggregated spatial allocation matrices are built. The next step, performed by livelike, is the generation of a fully autonomous agent population attributed with hundreds of demographic census variables. The agent population synthesized in livelike is then attributed with residential coordinates in actlike based on block assignment and, finally, allocated to an optimal daytime activity location via the street network. We present a case study in Knox County, Tennessee, synthesizing 30 populations of public K–12 school students \& teachers and allocating them to schools. Validation of our results shows they are highly promising by replicating reported school enrollment and teacher capacity with a high degree of fidelity.

Tuccillo, Joe↗
DOE OSTI2020

Progress in Understanding the Origins of Excellent Corrosion Resistance in Metallic Alloys: From Binary Polycrystalline Alloys to Metallic Glasses and High Entropy Alloys

Some of the factors responsible for good corrosion resistance of select polycrystalline and emerging alloys in chloride solutions are discussed with a goal of providing some perspectives on the current status and future directions. Traditional metallic glass alloys, single phase high entropy alloys (HEAs), early metallic glasses, and high entropy metallic glasses are all emerging corrosion-resistant alloys (CRAs) that utilize traditional strategies for improved corrosion resistance as well as take advantage of some other novel beneficial attributes. These materials enjoy many degrees of freedom as far as choice of both composition and structure, providing great flexibility in the pursuit of superior corrosion resistance. The new materials depart from classical solvent-solute type polycrystalline binary or ternary alloys. Thus, such emerging materials provide significant opportunities to achieve even greater improvements in corrosion resistance in harsh environments. Several examples of the unique corrosion properties of selected materials in the context of modern theories of corrosion are discussed herein. Discussion is restricted to solid-solution binary or ternary polycrystalline alloys, several metallic glass alloys, and single phase HEAs. A common feature of many CRAs is that composition and microstructure often affect both passivity and resistance to localized corrosion that can be divided into initiation, stabilization, and propagation stages. Enormous complexities in protective oxide structures and chemistries and the large number of combinatorial possibilities in newer materials such as HEAs preclude trial-and-error approaches and perhaps even combinatorial experimental design. Computational materials methodologies will be required in the search for new corrosion-resistant alloys in these material classes. The search must consider the best scientific insights available regarding how major and minor alloy additions, as well as various microstructural attributes, contribute to corrosion mitigation. Additional scientific insights, as they emerge, will enable choices beyond the reliance on high concentrations of alloying elements that are known to affect passivity breakdown and pit stabilization. A challenge is to connect the “basic attributes” of an alloy with its properties. The strength of this connection will likely require new scientific principles enabling deep multiphysics insights in order to link feature(s) such as composition and metallurgical phases to the desired corrosion properties. Application of data informatics will likely also play a role given the plethora of variables that are important in corrosion and the difficulty in assessing all relationships. Here, the opportunity exists to accelerate the design of emerging materials for high corrosion resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Light Absorption by Brown Carbon over the South-East Atlantic Ocean

Biomass burning emissions often contain brown carbon (BrC), which represents a large family of light-absorbing organics that is chemically complex and therefore difficult to estimate their absorption of incoming solar radiation, resulting in large uncertainties in the estimation of the global direct radiative effect of aerosols. Here we investigate the contribution of BrC to the total light absorption of biomass burning aerosols over the South-East Atlantic Ocean with different optical models utilizing a suite of airborne measurements from the ORACLES 2018 campaign by introducing an effective refractive index of black carbon (BC), m eBC = n eBC +ik eBC , that accounts for all possible absorbing components at 660 nm wavelength to facilitate the attribution of absorption at shorter wavelengths. Most values of the imaginary part of the refractive index, k eBC , were larger than those commonly used for BC from biomass burning emissions, suggesting contributions from absorbers beyond BC at 660 nm. The TEM-EDX single particle analysis further suggests that these long-wavelength absorbers might include iron oxides, as iron is found to be present only when large values of k eBC are derived. Using this effective BC refractive index, we find that the contribution of BrC to the total absorption at 470 nm (R BrC,470 ) ranges from ~5–15 %, with the organic aerosol mass absorption coefficient (MAC OA,470 ) at this wavelength ranging from 0.25 ± 0.34 m 2 g -1 to 0.43 ± 0.12 m 2 g -1 . The core-shell model yielded much higher estimates of MAC OA,470 and R BrC,470 than homogeneous mixing models, underscoring the importance of model treatment. Another key finding was that estimates of the BrC contribution at 470 nm from the commonly used AAE (absorption Ångström exponent) attribution method (< 5 %) are much lower than the BrC contribution estimates (R BrC,470 ) using our new methodology that accounts for contributions from both BrC and non-carbonaceous, long-wavelength absorbers, such as magnetite. Thus, it is recommended that application of any optical properties-based attribution method use absorption coefficients at the longest possible wavelength to minimize the influence of BrC at the long wavelength and to account for potential contributions from other absorbing materials.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Resilience Metrics Framework for Solar Photovoltaics

This presentation was given at the Photovoltaic Specialist Conference (PVSC) 54 in New Orleans, Louisiana. Photovoltaic (PV) systems are routinely exposed to extreme weather, including wind and hail storms. Historically, most systems have proven to be resilient to such events, but some storms have damaged PV systems, leading to physical and financial loss. Storm hardening measures and specific system attributes can reduce this risk. This work introduces a set of resilience metrics and a framework for quantifying, comparing, and predicting PV system resilience. The framework is divided into two parts: 1) predictive, attribute metrics based on site and component characteristics, and 2) impact metrics that assess post-storm performance. Metrics are weighted and aggregated, producing hazard-specific resilience scores. We derive damage functions from storm-impacted PV systems, establishing a baseline against which post-storm performance can be compared. This damage was widely variable across hail and wind intensities, and field hail damage was less than predicted by laboratory tests, suggesting that system features - in addition to storm conditions - influence damage likelihood. Finally, the metrics framework is demonstrated using three case studies of storm damaged PV systems. Although additional data are needed to create attribute specific damage functions and establish metric weights, this study presents a methodology for evaluating PV resilience and contributes new damage functions to the literature.

14 SOLAR ENERGY↗
DOE OSTI2020

Computer processing through distance-based quality score method in geospatial-temporal semantic graphs

A computer-implemented method of improving processing of overhead image data by a processor using a distance-based quality score in a geospatial-temporal semantic graph. An allowable range for each attribute in the subgraph search template is defined. For each match in a comparison, attribute values of each match element are compared against the preferred range and the allowable range to compute a corresponding distance of each match attribute from the subgraph search template. A corresponding overall match quality score is determined for each match from the subgraph search template, wherein determining the corresponding overall match qualities is performed using a corresponding required quality score and a corresponding optional quality score. All corresponding overall match quality scores are sorted into an ordered list and then displayed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

GeoRePORT Case Study Examples: Reporting Using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT)

The Geothermal Research Portfolio Optimization & Reporting Technique (GeoRePORT) was developed with funding from the United States Department of Energy's Geothermal Technologies Office (GTO) to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and non-scientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and, (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g. temperature, permeability, permitting), a site's project grade and readiness are evaluated on twelve independent attributes pertaining to geological, technical, or socio-economic feasibility. In this paper, we present case studies illustrating how GeoRePORT can be used to compare geological, technical, and socio-economic attributes between geothermal systems. The consistent and objective assessment protocols used in GeoRePORT allow for comparison of project attributes across unique locations and geological settings. GeoRePORT case studies outline the geological, socio-economic and technical features of four individual geothermal sites: Coso, Chena, Dixie Valley, and White Sands Missile Range. The case studies presented herein illustrate the usefulness of GeoRePORT in evaluating project risk/return, identifying gaps in reported data, evaluating R&D impact, and gathering insights on successes/failures as applicable to future projects.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2020

Using Graph Edit Distance for Noisy Subgraph Matching of Semantic Property Graphs

The subgraph matching problem is a fundamental problem in graph theory that is known to be NP-complete. In this study, performers were asked to develop algorithms to search for semantic property graphs that were subgraphs of a large knowledge graph. The templates provided contained structural information about the subgraphs and some attributes for each node and edge. There also exists a similarity measure between a set of attribute values that occurs on every node and edge. Algorithms performed well in the case where an exact match existed, but performers were also provided templates that had noise added such that there existed no match in the knowledge graph. Performers were asked to find the closest matches to those noisy subgraphs. To evaluate performance on this task, we developed a version of the graph edit distance algorithm to measure the cost of editing the template graph so that it is isomorphic in structure and attributes to the performer submission.

Ebsch, Christopher L.↗
DOE OSTI2024

Anomaly detection for cyber-physical systems

An anomaly detector is configured to construct cyber and/or physical features comprising information configured to characterize the cyber and/or physical state of a cyber-physical system. The physical features may be based on physical and/or physics-based relationships between a plurality of physical state attributes. A health of the cyber-physical system may be based on an error between estimates of one or more of the physical state attributes and measurements of the one or more physical state attributes. The relationships may be incorporated into machine learning membership functions used to classify cyber and/or physical behavior of the system.

Rieger, Craig G.↗
DOE OSTI2026

Are the U.S. Biorefineries Over the Hurdle of 2000 Ton Daily Throughput Yet?

The efficient utilization of lignocellulosic biomass for biofuel and biochemical production is hindered by material handling issues such as clogging and segregation among other challenges. Preprocessing methods such as drying, screening, and milling have improved conversion yield but have not sufficiently enhanced flowability, especially herbaceous biomass. The poor flowability of herbaceous biomass is rooted in some particle attributes that remain less altered by those methods, e.g., irregular particle shape, high roughness, and high compressibility, making it hard to scale up throughput to a key benchmark for a biorefinery – 2000 ton per day. Applying additional preprocessing methods like pelletization and torrefaction to drastically change those particle attributes can improve flow and handling but has not been comprehensively verified through test. The flowability of herbaceous biomass feedstock formats generated by three different preprocessing methods was recently assessed at Idaho National Laboratory’s Biomass Feedstock National User Facility: first, loose particles size reduced from as-received materials; second, pellets produced from an efficient densification process; and third, powders milled from torrefied pellets. Benchmarking tests including static angle of repose, basic flow energy measured in a powder rheometer, and discharge flow in an adjustable hopper, were conducted to evaluate those feedstock formats. Beyond the capacity of existing experimental apparatuses, a digital engineering approach involving flow simulations and AI models were used to identity the material attributes and processing parameters that have dominant influences on flow throughput. Techno-economic analysis focusing on hopper flow as a typical material handling operation was conducted for those feedstock formats. Perspectives will be discussed on whether the 2000-ton daily throughput for a biorefinery is achievable at an acceptable cost by using any of the tested preprocessing methods.

09 - BIOMASS FUELS↗
DOE OSTI2024

The Effect of Air Separations on Fast Pyrolysis Products for Forest Residue Feedstocks

This study investigates the intricate relationship between biomass preprocessing and pyrolysis product yields, employing the air classification technique for the treatment of loblolly pine residues with varying moisture content. A comprehensive exploration of the physicochemical properties of air-classified loblolly pine informs a sophisticated pyrolysis simulation model. Given the complex and multifaceted nature of biomass pyrolysis, operating across diverse temporal and spatial scales, a pyrolysis kinetics-based CFD–DEM simulation method is employed to predict product yields. Results showed that the elevated moisture content amplifies particle adhesiveness, necessitating augmented air velocities for effective separation, thereby influencing the efficiency of the separation process. While carbon and hydrogen contents exhibit relative stability across diverse moisture contents and blower frequencies, the oxygen content undergoes noticeable changes. For example, the oxygen contents were measured as 29.2 and 38.6 wt% in the light fraction of 30% moisture content sample at blower frequencies of 10 and 20 Hz, respectively. An intriguing finding emerges from pyrolysis simulation, indicating that a lower blower frequency in air classification moderately enhances bio-oil yield and significantly improves its quality, particularly in terms of water content. For instance, the water content in the bio-oil was about 1.5% and 10% in the heavy and light fractions, respectively from 10% moisture sample under 15 Hz blower frequency. In summary, a detailed understanding and strategic manipulation of critical material attributes in biomass through efficient fractionation techniques are imperative for advancing fast pyrolysis as a sustainable avenue for renewable energy and chemical production.

09 BIOMASS FUELS↗
DOE OSTI2021

On the Emergence of Human Influence on Surface Air Temperature Changes Over India

Human activities in terms of greenhouse gas (GHG) emissions and aerosols resulting from the combustion of fossil fuels have been shown to have affected the temperature of the Earth on global and continental scales. The surface air temperature (TAS) over India has also been observed to be increasing over the last 100 years. Understanding the underlying causes of regional climate change over India can help in developing appropriate mitigation and adaptation strategies. Differentiating signals of externally forced climate changes from the noise of natural internal variability generally becomes more difficult as spatial scale reduces. Therefore detecting and attributing the influence of external forcings such as GHGs and aerosols is harder at local and regional scales. In this study, we applied a detection and attribution (D&A) method to study annual and seasonal mean TAS over the Indian region. We found that the observed warming over India from 1906 to 2005 cannot be explained by natural climate variability alone. We found that the warming is largely driven by the increase in GHGs, and partially offset by regional anthropogenic emissions of aerosols. These results were confirmed for the shorter 1956–2005 period, but results were sensitive to the choice of observational data set. Furthermore, the changes cannot be explained by internal climate variability or natural external forcings alone, but are compatible with the responses to combined anthropogenic GHG and aerosol forcings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Little influence of Arctic amplification on mid-latitude climate

Observations and model simulations show enhanced warming in the Arctic under increasing greenhouse gases, a phenomenon known as the Arctic amplification (AA), that is likely caused by sea-ice loss. AA reduces meridional temperature gradients linked to circulation, thus mid-latitude weather and climate changes have been attributed to AA, often on the basis of regression analysis and atmospheric simulations. However, other modelling studies show only a weak link. This inconsistency may result from deficiencies in separating the effects of AA from those of natural variability or background warming. In this work, using coupled model simulations with and without AA, we show that cold-season precipitation, snowfall and circulation changes over northern mid-latitudes come mostly from background warming. AA and sea-ice loss increase precipitation and snowfall above ~60° N and reduce meridional temperature gradients above ~45° N in the lower–mid troposphere. However, minimal impact on the mean climate is seen below ~60° N, with weak reduction in zonal wind over 50°–70° N and 150–700 hPa, mainly over the North Atlantic and northern central Asia. These results suggest that the climatic impacts of AA are probably small outside the high latitudes, thus caution is needed in attributing mid-latitude changes to AA and sea-ice loss on the basis of statistical analyses that cannot distinguish the impact of AA from other correlated changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.

54 ENVIRONMENTAL SCIENCES↗