Data for EMSL Project 60921 from March 2025
Explore the source record for details and available documents.
Toyoda, Jason [Environmental Molecular Sciences La↗
SEARCH · Engineering Papers
Results for “Oregon State University”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Data for EMSL Project 60921 from March 2025
Explore the source record for details and available documents.
Toyoda, Jason [Environmental Molecular Sciences La↗
Data for EMSL Project 60921 from March 2025
Explore the source record for details and available documents.
Toyoda, Jason [Environmental Molecular Sciences La↗
Characterization of Build Parameters and Microstructure in Low Heat Input WAAM of Ni-Based Superalloy Haynes 282
Ni-based superalloy Haynes® 282® is being targeted for various applications in advanced power generation systems for its superior fabricability, weldability, and excellent high temperature creep and corrosion performance. This process optimization study aims to use a low heat-input, high deposition rate, controlled Gas Metal Arc Welding (GMAW) process, Cold Metal Transfer (CMT) by Fronius, attempting to achieve fully dense fabrication and possibly avoid the need for HIP. Twenty-one multilayer blocks (~25x100x40 mm3) were deposited to explore a large set of build parameters variations that focused on varying the travel speed from 14 to 42 inches per minute (ipm) and wire feed speed from 150 to 450 ipm. A strong correlation has been observed between arc energy – controlled primarily by travel and wire feed speed. Initial visual inspection, internal microstructural examination, and computed tomography (CT) have been used to determine the effects of built parameters on evolution of internal porosity and defects. Scanning electron microscopy techniques enabled structural and compositional imaging of heterogeneity and changes in microstructural properties.
additive manufacturing↗
A global comparison of surface and subsurface microbiomes reveals large-scale biodiversity gradients, and a marine-terrestrial divide
Subsurface environments are among Earth’s largest habitats for microbial life. Yet, until recently, we lacked adequate data to accurately differentiate between globally distributed marine and terrestrial surface and subsurface microbiomes. Here, we analyzed 478 archaeal and 964 bacterial metabarcoding datasets and 147 metagenomes from diverse and widely distributed environments. Microbial diversity is similar in marine and terrestrial microbiomes at local to global scales. However, community composition greatly differs between sea and land, corroborating a phylogenetic divide that mirrors patterns in plant and animal diversity. In contrast, community composition overlaps between surface to subsurface environments supporting a diversity continuum rather than a discrete subsurface biosphere. Differences in microbial life thus seem greater between land and sea than between surface and subsurface. Diversity of terrestrial microbiomes decreases with depth, while marine subsurface diversity and phylogenetic distance to cultured isolates rivals or exceeds that of surface environments. We identify distinct microbial community compositions but similar microbial diversity for Earth’s subsurface and surface environments.
54 ENVIRONMENTAL SCIENCES↗
Effects of co-adsorbed water on different bond cleavages involved in acetic acid decomposition on Pt (111)
Acetic acid decomposition on Pt (111) in the presence of co-adsorbed water is a good model system for oxygenate decomposition on Pt (111) in aqueous phase, with application in hydrogen production and biomass conversion. Here, in this study, we present a density functional theory (DFT) theory calculation of how co-adsorbed water affects different bond cleavages of acetic acid decomposition on Pt (111). The presence of co-adsorbed water generally enhances O$-$H bond cleavage while inhibiting OC$-$O and OC$-$OH bond cleavage. The influence of co-adsorbed water on C$-$H bond cleavage varies the most and depends on the nature of the transition state and how co-adsorbed water stabilizes the initial and final state. Although these trends are useful as general guidance, they are not sufficient to predict the effect on a complex reaction network such as acetic acid decomposition on Pt (111) which has several parallel reaction paths with similar energies. In the absence of co-adsorbed water, the two lowest energy pathways are decarboxylation (DCX) and decarbonylation (DCN) pathways through a common CH 2 COO intermediate, in which the DCX pathway (C$-$H bond cleavage of CH 2 COO) is more favorable than DCN pathway (OC$-$O bond cleavage of CH 2 COO). In the presence of co-adsorbed water, the energy difference between Csingle bondH bond cleavage and OC$-$O bond cleavage of CH 2 COO increases, suggesting an increase in the favorability of acetic acid decarboxylation (formation of carbon dioxide) over acetic acid decarbonylation (formation of carbon monoxide) on Pt (111).
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios
This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.
De Souza, Reubun [School of Electrical Engineering↗
Morphological Degradation of Oxygen Evolution Reaction-Electrocatalyzing Nickel Selenides at Industrially Relevant Current Densities
We investigated electrodeposited nanoparticulate nickel selenide (pre)catalysts that transform into nickel oxides/ oxyhydroxides under oxygen evolution reaction conditions in alkaline solutions. Previous studies of this transformation were conducted at lower current densities than those of industrial relevance (≥1 A cm −2 ). We used ultramicroelectrodes (UMEs) to achieve such current densities, benefiting from their small size, ensuring low absolute currents and low ohmic drop but high current densities. Morphological degradation of the catalyst material was only observed at current densities exceeding 1 A cm −2 but not for smaller ones. Using X-ray absorption, Xray photoemission spectroscopy, and X-ray diffraction, we confirmed that the degradation was accompanied by the literature-known transformation of nanoparticulate Ni 3 Se 2 (bulk)/NiSe (surface) into nickel oxyhydroxide. The transformation of the precatalyst goes along with a significant improvement in the charge transfer kinetics observed by decreasing Tafel slopes with ongoing experimental time extracted from cyclic voltammetry (CV) experiments and electrochemical impedance spectroscopy (EIS) in the high-frequency range. However, these kinetic improvements are accompanied by limitations in mass transport concluded from decreasing current responses at high overpotentials in CVs and increasing impedance in the low-frequency range of the EIS spectra after extended CV cycling. These mass transport limitations originated from morphological degradations at the UME exceeding 1 A cm −2 which we proved by applying identical location scanning electron microscopy. This has not been reported in studies that have been limited to lower current densities before. Our findings showcase how UMEs can be used to study (pre)catalysts (herein nickel selenides) under current densities of industrial relevance in the absence of ohmic drop-related ambiguities, combined with in-depth materials characterization studies, e.g., identical location microscopy and advanced spectroscopic methods. This approach enables direct evaluation and comparison of catalyst materials and thus demonstrates how to overcome long-standing limitations of electrocatalyst design and testing.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site
Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C 3 or C 4 ), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjusted R 2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjusted R 2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjusted R 2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies ( R 2 = 0.65 to <0.01), but R 2 was >0.80 for certain lineages and sites. The relationship between Vc max , a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients.
Pau, Stephanie [Department of Geography University↗
Temporal Study 2022-2024: Sample-Based Surface Water Dissolved Inorganic Carbon, Dissolved Organic Carbon, Total Nitrogen, Stable Isotopes, and Total Suspended Solids from across Multiple Watersheds in the Yakima River Basin, Washington, USA
This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry data generated from samples collected at bi-weekly or monthly intervals at six sites across the Yakima River Basin in Washington, USA. Sample and sensor data from previous years (2021-2022) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1898912 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054, respectively. Related sensor data from 2022-2024 will be published separately. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) dissolved inorganic carbon (DIC) and averages; (6) dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC) and averages; (7) total dissolved nitrogen (TN) and averages; (8) total suspended solids (TSS); (9) stable isotopes; (10) surface water sampling protocol; (11) sensor protocol; (12) methods codes; and (13) international generic sample number (IGSN) mapping file. All files are .csv or .pdf. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. For data and scripts associated with "Shifts in rain-snow partitioning drive faster water transit times in the US Pacific Northwest" (Butler et al., 2026), go to https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3025481
18-O↗
The Temperature Dependence of the Langmuir Adsorption Model for a Single-Site Metal–Organic Framework
The single-site Langmuir adsorption model, also known as the Langmuir isotherm equation, is one of the simplest possible descriptions of adsorption phenomena, and yet finds widespread applicability across a range of disciplines. In its simplest form, it is deployed to treat adsorption equilibria at constant temperature (i.e., along isotherms); however, at the heart of its derivation is a more general class of models that each incorporate an explicit temperature dependence, subject to assumptions about the spatial/translational degrees of freedom of the adsorbed species. In this work, measurements of the temperature dependence of supercritical adsorption of H 2 on a single-site metal-organic framework (MOF) are presented, and fitted using a range of Langmuir models with distinct treatments of degrees of freedom in the adsorbed phase. Surprisingly, all of the models can be used to adequately represent the measured data, despite yielding significantly different values for binding energy and the temperature dependence of the isosteric enthalpy of adsorption (i.e., the isosteric heat, qst). Furthermore, a critical finding is that the mean-temperature isosteric enthalpy of adsorption remains consistent across all models within experimental error, highlighting its reliability for evaluating adsorption thermodynamics.
08 HYDROGEN↗
Atmospheric Feedbacks Reverse the Sensitivity of Modeled Photosynthesis to Stomatal Function
Stomata mediate fluxes of carbon and water between terrestrial plants and the atmosphere. These fluxes are governed by stomatal function and can be modulated in many Earth system models by an empirical parameter within the calculation of stomatal conductance, the stomatal slope (𝑔 1𝑀 ). Intuitively, 𝑔 1𝑀 represents the marginal water cost of carbon, relating it to the emergent plant property of water use efficiency. Observations show that 𝑔 1𝑀 can range widely across and within plant types in varying environments, and this distribution of 𝑔 1𝑀 is not captured within Earth system models which represent each plant type with a single 𝑔 1𝑀 value. Here we examine how 𝑔 1𝑀 influences photosynthesis using coupled Earth system model simulations by perturbing 𝑔 1𝑀 to observed 5th and 95th percentiles for each plant type. We find that high 𝑔 1𝑀 reduces photosynthesis nearly everywhere, while low 𝑔 1𝑀 has regionally dependent responses. Under fixed atmospheric conditions, low 𝑔 1𝑀 increases photosynthesis in the Amazon and central North America but decreases photosynthesis in boreal Canada. These responses reverse when the atmosphere responds interactively due to spatially differing sensitivity to increases in temperature and vapor pressure deficit. Choice of 𝑔 1𝑀 also influences photosynthetic response to changes in atmospheric carbon dioxide (CO 2 ), with lower and higher 𝑔1𝑀 modifying total global response to elevated 2x preindustrial CO 2 by +6.4% and −9.6%, respectively. Our work demonstrates that atmospheric feedbacks are critical for determining the photosynthetic response to 𝑔 1𝑀 assumptions and some regions are particularly sensitive to choice of 𝑔 1𝑀 .
Liu, Amy X. [University of Washington, Seattle, WA↗
The Role of Surface Hydroxyls in Dehydration and Dehydrogenation of Formic Acid on Fe 3 O 4 (001)
Understanding the role of surface structure and hydroxylation in catalytic reactions on metal oxide surfaces is important for developing a mechanistic insight into the complex interface processes. Here, we investigate the reactivity of formic acid on reconstructed Fe 3 O 4 (001) using a combination of X-ray photoelectron spectroscopy, infrared reflection absorption spectroscopy, temperature-programmed reaction spectroscopy, low energy electron diffraction, and electronic structure calculations. We find that formic acid initially dissociates at low temperatures (< 80 K) into bidentate formate and a hydroxyl up to an initial dosed coverage of two HCOOH per Fe 3 O 4 (001) unit cell. At higher temperatures (> 450 K), formate largely decomposes along the dehydration pathway, producing CO and H 2 O, with dehydrogenation to CO 2 being a minority side reaction. As a first step, water formation leads to surface oxygen extraction via the Mars-van Krevelen mechanism. Computational studies reveal formate embedded in oxygen vacancies as a key intermediate in the CO formation mechanism. CO formation proceeds via two reaction pathways with desorption that peaks at 530 K on the hydroxyl-rich surface and 560 K on the hydroxyl-deficient surface. Atomic hydrogen coadsorption experiments and ab initio calculations reveal that the presence of surface hydroxyls reduces the CO formation barrier. Furthermore, these results highlight the complex interactions between substrate and intermediate species occurring during reactions on metal oxide surfaces.
Adsorption↗
Data for: Miscanthus × giganteus changes soil structure and increases maximum water holding capacity
The data provided include results from a comparative study evaluating the impact of Miscanthus × giganteus (miscanthus) versus maize on soil structural properties and maximum water holding capacity (MWHC) across two Iowa sites. The dataset includes MWHC values determined using the Funnel Filter Paper Drainage (FFPD) method, as well as additional measurements of MWHC following structural disruption of the soil to isolate the effect of aggregation. It also contains three-dimensional micro-computed tomography (microCT) data used to quantify total porosity and pore size distribution (PSD) of soil aggregates at a 5 µm resolution. All data are provided as raw replicate-level measurements, organized by site, crop, and depth, along with processed summary files in table form in CSV (.csv) format to support reproducibility and downstream analysis.
Misanthus x giganteus↗
Transcriptomic and functional analyses uncover a conserved effector driving genotype-dependent virulence in the Sphaerulina musiva-Populus trichocarpa interaction
The introduction of invasive microbes compromises the structure, biodiversity, and function of naïve ecosystems. Sphaerulina musiva, a hemibiotrophic pathogen that causes leaf spot and stem cankers in Populus species, exemplifies an invasive fungal pathogen spread by human activities. However, the genetic mechanisms of pathogenicity and virulence are poorly understood, impeding mitigation strategies. We utilized RNA sequencing to identify fungal effectors linked to stem canker formation, informing the development of future strategies for effective disease management. Our analysis revealed 70 genes differentially expressed at 2 weeks and 110 genes at 3 weeks between inoculated trees and controls. Notably, the gene with the highest expression at 2 weeks and the second highest at 3 weeks was homologous to Extracellular protein 2 (Ecp2). Complementary genome-wide association studies linked sequence polymorphisms in this locus to phenotypic variation in disease severity. Infiltration of S. musiva Ecp2 into Populus trichocarpa leaves induced necrosis in susceptible genotypes. Gene disruption using a CRISPR-Cas9 RNP system resulted in a genotype-dependent reduction of stem canker and disease severity. Tracing the evolutionary history of this effector across the fungal kingdom, we uncovered clade-specific gene-family expansions and orthologs in new species. These findings raise questions about the function and adaptive significance of these gene families in fungal lifestyles. Our study provides the first tractable target for breeding resistant poplar genotypes, addressing the challenges of managing S. musiva and uncovering mechanisms that drive its virulence, and provides deeper insights into the evolutionary dynamics of a conserved small-secreted protein with a diversity of functions.
Sondreli, Kelsey L [Oregon State University]↗
The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks
Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.
Nuclear data↗
Selective Oxidation and Cr Segregation in High-Entropy Oxide Thin Films
High-entropy oxides (HEOs) offer exceptional compositional flexibility and structural stability, making them promising materials for energy and catalytic applications. Here, in this study, we investigate Sr doping effects on B-site cation oxidation states, local composition, and structure in epitaxial La 1–x Sr x (Cr 0.2 Mn 0.2 Fe 0.2 Co 0.2 Ni 0.2 )O 3 thin films. X-ray spectroscopies reveal that Sr doping preferentially promotes Cr oxidation from Cr 3+ to Cr 6+ , partially oxidizes Co and Ni, while leaving Mn 4+ and Fe 3+ unchanged. Atomic-resolution scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy shows pronounced Cr segregation, with depletion at the interface and enrichment at the surface, along with partial amorphization in heavily Sr-doped samples. This segregation is likely driven by oxidation-induced migration of smaller, high-valence Cr cations during growth. These findings highlight the critical interplay between charge compensation, local strain, and compositional fluctuations in HEOs, indicating that precise control over growth conditions is critical for tuning their surface composition and electronic structure toward more robust electrocatalyst design.
36 MATERIALS SCIENCE↗
Deep Learning-Driven Control Optimization for Wave Energy Converters via Real-time Sea State Prediction
Explore the source record for details and available documents.
Alam Shifat, Tanvir [Oregon State University ]↗