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Results for “Correlation analysis”

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At least 379 records · Page 21

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Relative effect of anthropogenic warming and natural climate variability to changes in Compound drought and heatwaves

Compound drought and heatwave (CDHW) events can be influenced by large scale teleconnections and anthropogenic warming, leading to severe socio-economic impacts across various climate regions. Here, the relative influence of six different teleconnection patterns and anthropogenic global warming on the global CDHW occurrences is quantified systematically using the instrumental data period, 1982–2016. The results from the study suggest a substantial increase in the CDHW events (1–5 events per year) across various parts of the globe at the beginning of 21st century (2000–2016). A Bayesian approach is implemented to identify the most vulnerable climate regions based on the degree of susceptibility of heatwaves (DSHW) towards drought. As such, top ten most vulnerable regions are selected based on the DSHW magnitude, and a partial correlation analysis is performed to select the natural and anthropogenic drivers of CDHW in those regions, separately. A logistic regression model is then used to determine significant changes in the odds of CDHW due to changes in the selected drivers that suggest a significantly positive, and multiplicative effect of anthropogenic global warming in the top ten most vulnerable climate regions. Finally, the same logistic regression model, integrated with an analytical framework, is applied to determine the relative influence of anthropogenic global warming on the changes in odds of CDHW for the future, 1.5 °C and 2 °C warming limits. Finally, the results suggest that relative to the 2 °C global warming, constraining to the 1.5 °C global warming limit may conduce about 17-fold reduction in the odds of CDHW in the most vulnerable climate region, East Asia, 5–8-fold reduction in Western North America, Northern Australia, Central North America, Central Europe, South Asia, and the Mediterranean region, and 3–4-fold reduction in Northeastern Brazil, Eastern North America, and West Asia.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Thermal cycling-driven microstructural changes of eutectic Al–Si phase change materials in SS304 containers revealed by multi-modal imaging

Aluminum-based Al–Si alloys are widely used as phase change materials (PCMs) in thermal energy storage (TES) systems owing to their high volumetric latent heat and superior thermal conductivity. However, their long-term reliability is limited by degradation processes that remain insufficiently understood. In this work, we employ a multimodal, correlative characterization framework to systematically resolve the degradation behavior of eutectic Al–Si PCMs in contact with SS304 containers under repeated thermal cycling. By integrating high-resolution electron microscopy, three-dimensional X-ray fluorescence (3D XRF) imaging, and differential scanning calorimetry (DSC), we directly link spatially resolved compositional and microstructural evolution to changes in thermophysical properties. The correlative analysis reveals that elemental leaching of Fe, Cr, and Ni from the stainless-steel container into the PCM drives the formation of intermetallic compounds (IMCs) both at the interface and within the bulk PCM, leading to pronounced compositional heterogeneity. These interfacial reactions and diffusion-induced transformations progressively destabilize the Al–Si eutectic, reducing the effective phase-transforming fraction. Consistent with these observations, DSC measurements show a decrease in melting temperature and latent heat of fusion with thermal cycling. These results underscore the critical influence of interfacial reactions and material compatibility on the stability, durability, and overall performance of Al–Si-based TES systems.

25 ENERGY STORAGE↗

DOE OSTI2026

Decoding the proton’s gluonic density with lattice QCD-informed machine learning

We present a first machine learning-based decoding of the gluonic structure of the proton from lattice QCD using a variational autoencoder inverse mapper (VAIM). Harnessing the power of generative AI, we predict the parton distribution function (PDF) of the gluon given information on the reduced pseudo-Ioffe-time distributions (RpITDs) as calculated from an ensemble with lattice spacing a ≈ 0.09 fm and a pion mass of M π ≈ 310 MeV. The resulting gluon PDF is consistent with phenomenological global fits within uncertainties, particularly in the intermediate-to-high-x region where lattice data are most constraining. A subsequent correlation analysis confirms that the VAIM learns a meaningful latent representation, highlighting the potential of generative AI to bridge lattice QCD and phenomenological extractions within a unified analysis framework.

Gluon parton distribution function↗

DOE OSTI2021

Inactivated STAT5 pathway underlies a novel inhibitory role of EBF1 in chronic lymphocytic leukemia

B-cell chronic lymphocytic leukemia (CLL) is a disease caused by gradual accumulation of functionally incompetent lymphocytes. The majority of CLL cases are accompanied by chemoresistance. Early B cell factor 1 (EBF1) is a crucial contributor to B-cell lymphopoiesis. This study is to explore the effect of EBF1 on CLL cell progression and its involvement in regulating the signal transducers and activators of transcription 5 (STAT5) pathway. We conducted a correlation analysis between EBF1 and the clinical characteristics of CLL patients. Subsequently, EBF1 was overexpressed by transfection with EBF1 overexpression plasmid and the STAT5 pathway was also blocked by treatment with SH-4-54 in isolated CD20{sup +} B lymphocytes to investigate their roles in the regulation of cellular functions. STAT5, Janus kinase 2 (JAK2) expression and their phosphorylation levels were determined by quantitative PCR and Western blot analyses. The in vivo effects of EBF1 on tumor growth were evaluated using a xenotransplant model. Downregulation of EBF1 was observed in CD20{sup +} B lymphocytes of CLL patients. EBF1 overexpression disrupted the activation of STAT5 pathway, as evidenced by decreased expression and phosphorylation levels of STAT5 and JAK2. Furthermore, overexpression of EBF1 repressed viability and cell cycle entry, and increased apoptosis of CD20{sup +} B lymphocytes by inhibiting the STAT5 pathway. Finally, EBF1 exerted antitumor effects in nude mice. Overall, our study elucidates the inhibitory role of EBF1 in CLL through inactivation of the STAT5 pathway, which may provide new targets for CLL treatment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Vertical Hydrologic Exchange Flows Control Methane Emissions from Riverbed Sediments

CH 4 emissions from inland waters are highly uncertain in the current global CH 4 budget, especially for streams, rivers, and other lotic systems. Previous studies have attributed the strong spatiotemporal heterogeneity of riverine CH 4 to environmental factors such as sediment type, water level, temperature, or particulate organic carbon abundance through correlation analysis. However, a mechanistic understanding of the basis for such heterogeneity is lacking. Here, in this study, we combine sediment CH 4 data from the Hanford reach of the Columbia River with a biogeochemical-transport model to show that vertical hydrologic exchange flows (VHEFs), driven by the difference between river stage and groundwater level, determine CH 4 flux at the sediment–water interface. CH 4 fluxes show a nonlinear relationship with the magnitude of VHEFs, where high VHEFs introduce O 2 into riverbed sediments, which inhibit CH 4 production and induce CH 4 oxidation, and low VHEFs cause transient reduction in CH 4 flux (relative to production) due to reduced advective CH 4 transport. In addition, VHEFs lead to the hysteresis of temperature rise and CH 4 emissions because high river discharge caused by snowmelt in spring leads to strong downwelling flow that offsets increasing CH 4 production with temperature rise. Our findings reveal how the interplay between in-stream hydrologic flux besides fluvial-wetland connectivity and microbial metabolic pathways that compete with methanogenic pathways can produce complex patterns in CH 4 production and emission in riverbed alluvial sediments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t – 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

42 ENGINEERING↗

DOE OSTI2022

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Valuable knowledge of catalysis is often hidden in a large amount of scientific literature. There is an urgent need to extract useful knowledge to facilitate scientific discovery. Here this work takes the first step toward the goal in the field of catalysis. Specifically, we construct the first information extraction benchmark data set that covers the field of catalysis and also develop a general extraction framework that can accurately extract catalysis-related entities from scientific literature with 90% extraction accuracy. We further demonstrate the feasibility of leveraging the extracted knowledge to help users better access relevant information in catalysis through an entity-aware search engine and a correlation analysis system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Mapping Molecular Adsorption Configurations with < 5 nm Spatial Resolution through Ambient Tip-Enhanced Raman Imaging

We interrogate para-mercaptobenzoic acid (MBA) molecules chemisorbed onto plasmonic silver nanocubes through tip-enhanced Raman (TER) spectral nano-imaging. Through a detailed examination of the spectra, aided by correlation analysis and density functional theory calculations, we find that MBA chemisorbs onto the plasmonic particles with at least two distinct configurations: S- and CO2-bound. High spatial resolution TER mapping allows us to distinguish between the distinct adsorption geometries with a pixel-limited (< 5 nm) spatial resolution under ambient laboratory conditions.

Gabel, Matthew L.↗

DOE OSTI2023

Effects of the Grazing Incidence Geometry on X-ray Photon Correlation Spectroscopy Measurements

X-ray photon correlation spectroscopy (XPCS) is a versatile tool to measure dynamics on the nanometer to micrometer scale in bulk samples. XPCS has also been applied in grazing incidence (GI) geometry to examine the dynamics of surface layers. However, considering GI scattering experiments more universally, the GI geometry leads to a superposition of signals due to reflection and refraction effects, also known from the distorted-wave Born approximation (DWBA). In this paper, the impact of these reflection and refraction effects on the correlation analysis is determined experimentally by measuring grazing incidence transmission XPCS (GT-XPCS) and grazing incidence XPCS (GI-XPCS) simultaneously for a thin film sample, showing non-equilibrium dynamics. Here the results of the GI and GT geometry comparisons are combined within the framework of the standardly applied, simplified DWBA. These calculations allow identifying the main contributions of the detected signal from the leading scattering terms along the out-of-plane direction q z , which dominate the measured intensity pattern on the detector. In combination with the calculation of the non-linear effect of refraction in GTSAXS and GISAXS, it is possible to identify experimental conditions that can be chosen to run experiments and data analysis as close as possible to transmission XPCS and to explain which limitations for data interpretations are observed. Consequently, the beam exposure can be significantly reduced by using GI geometry only. Calculations of experimental settings prior to experiments are detailed to determine suitable q z regions for a variety of material systems measured in bulk-sensitive GI-XPCS experiments, allowing us to determine the scaling behavior of typical decay times as a function of q that is comparable to the scaling behavior obtained in distortion-free GT-XPCS or transmission XPCS experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Quantifying the Correlation between Coordination Chemistry, Interfacial Formation, and Electrochemical Performances for Mg Battery Electrolytes

Here, the rise of magnesium batteries as promising post-Li-ion energy storage technologies has sparked considerable attention toward understanding the fundamental aspects of coordination chemistry concerning Mg cations in multivalent electrolytes. This exploration includes investigating how coordination influences crucial electrolyte properties like solubility, electroreduction stability, and the formation of the interphase, all of which are pivotal for practical battery applications. Despite recent progress in developing a few functional electrolytes, a comprehensive understanding of the solvation structure that can facilitate efficient Mg deposition performance and the formulation of general design rules based on the solvation structure is still lacking. In our study, we endeavor to establish a connection between solvent and anion interactions with Mg 2+ , interface formation, and cycling performance through a series of organic ether solvents (tetrahydrofuran, glyme, diglyme, and triglyme) and amine solvents (dimethylamine, 3-methoxypropylamine, and dimethoxyethylamine). Our findings reveal a distinct coordination trend for solvent/Mg 2+ and (Mg-TFSI):solvent across various solvents, which dictates the extent of ion pairing for TFSI salts with increasing solvent molecule size and denticity. The solvated species in the bulk electrolyte across different solvents lead to diverse interfacial chemistries with varying decomposition components. We also explore the cycling efficiency as well as Mg deposition overpotentials for different solvents. A correlation analysis was conducted to assess the interplay between the structure and performance. Lastly, we apply the insights gained from these results to tailor the relative anion/Mg 2+ coordination structures using cosolvent systems, aiming for improved cell performance.

25 ENERGY STORAGE↗

DOE OSTI2025

Surface Chemistry and Particle Morphology Changes in Pine Biomass under Indirect Thermal Gradients: Implications for Feed Screw Design

The conversion of biomass feedstocks into fuels and chemicals using fast pyrolysis is a promising approach to renewable energy. Feed screws that convey biomass to pyrolysis reactors, however, often encounter plugging. Indirect heating of the feed screw occurs due to contact with the pyrolysis chamber, resulting in a heating gradient ranging from ambient temperature (22 °C) to reactor temperature (500 °C). Given that major cell wall macromolecules, such as lignin, cellulose, and hemicellulose, begin to produce bio-oils and volatile resin acid compounds within this temperature gradient, we hypothesized that indirect heating during feed screw conveyance is sufficient to cause premature degradation of biomass. We characterized this degradation by observing increases in surface roughness, changes in overall particle morphology, and the production and deposition of bio-oils on biomass particle surfaces. Correlative analysis between optical in situ hot-stage microscopy, confocal Raman spectroscopy, and SEM analysis revealed that heating at temperatures as low as 375 °C caused significant increases in surface roughness, with large fissures forming between and within cell walls. Additionally, droplets of bio-oil were observed on particles, especially in the bark and cambium samples. This work suggests that these phenomena contribute to particle agglomeration, leading to feed screw plugging, and that engineering a solution to cool the feed screw could prevent particle agglomeration and reduce plugging incidents, thereby increasing biomass processing efficiency.

09 BIOMASS FUELS↗

DOE OSTI2024

Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry

Abstract The role of manganese (Mn) in ecosystem carbon (C) biogeochemical cycling is gaining increasing attention. While soil Mn is mainly derived from bedrock, atmospheric deposition could be a major source of Mn to surface soils, with implications for soil C cycling. However, quantification of the atmospheric Mn cycle, which comprises emissions from natural (desert dust, sea salts, volcanoes, primary biogenic particles, and wildfires) and anthropogenic sources (e.g., industrialization and land‐use change due to agriculture), transport, and deposition, remains uncertain. Here, we use compiled emission data sets for each identified source to model and quantify the atmospheric Mn cycle by combining an atmospheric model and in situ atmospheric concentration measurements. We estimated global emissions of atmospheric Mn in aerosols (<10 μm in aerodynamic diameter) to be 1,400 Gg Mn year −1 . Approximately 31% of the emissions come from anthropogenic sources. Deposition of the anthropogenic Mn shortened Mn “pseudo” turnover times in 1‐m‐thick surface soils (ranging from 1,000 to over 10,000,000 years) by 1–2 orders of magnitude in industrialized regions. Such anthropogenic Mn inputs boosted the Mn‐to‐N ratio of the atmospheric deposition in non‐desert dominated regions (between 5 × 10 −5 and 0.02) across industrialized areas, but that was still lower than soil Mn‐to‐N ratio by 1–3 orders of magnitude. Correlation analysis revealed a negative relationship between Mn deposition and topsoil C density across temperate and (sub)tropical forests, consisting with atmospheric Mn deposition enhancing carbon respiration as seen in in situ biogeochemical studies.

Environmental Sciences & Ecology↗

DOE OSTI2024

Tidally Modulated Glacial Slip and Tremor at Helheim Glacier, Greenland

Numerical modeling of ice sheet motion and hence projections of global sea level rise require information about the evolving subglacial environment, which unfortunately remains largely unknown due to its difficulty of access. Here we advance such subglacial observations by reporting multi–year observations of seismic tremor likely associated with glacier sliding at Helheim Glacier. This association is confirmed by correlation analysis between tremor power and multiple environmental forcings on different timescales. Variations of the observed tremor power indicate that different factors affect glacial sliding on different timescales. Effective pressure may control glacial sliding on long (seasonal/annual) timescales, while tidal forcing modulates the sliding rate and tremor power on short (hourly/daily) timescales. Polarization results suggest that the tremor source comes from an upstream subglacial ridge. This observation provides insights on how different factors should be included in ice sheet modeling and how their timescales of variability play an essential role.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Iron mineral dissolution releases iron and associated organic carbon during permafrost thaw

It has been shown that reactive soil minerals, specifically iron(III) (oxyhydr)oxides, can trap organic carbon in soils overlying intact permafrost, and may limit carbon mobilization and degradation as it is observed in other environments. However, the use of iron(III)-bearing minerals as terminal electron acceptors in permafrost environments, and thus their stability and capacity to prevent carbon mobilization during permafrost thaw, is poorly understood. We have followed the dynamic interactions between iron and carbon using a space-for-time approach across a thaw gradient in Abisko (Sweden), where wetlands are expanding rapidly due to permafrost thaw. We show through bulk (selective extractions, EXAFS) and nanoscale analysis (correlative SEM and nanoSIMS) that organic carbon is bound to reactive Fe primarily in the transition between organic and mineral horizons in palsa underlain by intact permafrost (41.8 ±10.8 mg carbon per g soil, 9.9 to 14.8% of total soil organic carbon). During permafrost thaw, water-logging and O 2 limitation lead to reducing conditions and an increase in abundance of Fe(III)-reducing bacteria which favor mineral dissolution and drive mobilization of both iron and carbon along the thaw gradient. By providing a terminal electron acceptor, this rusty carbon sink is effectively destroyed along the thaw gradient and cannot prevent carbon release with thaw.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Coupling physics in machine learning to predict properties of high-temperatures alloys

Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A theoretical investigation of the effect of Ga alloying on thermodynamic stability, electronic-structure, and oxidation resistance of Ti 2 AlC MAX phase

We present a systematic investigation of thermodynamic stability, phase-reaction, and chemical activity of Al containing disordered Ti 2 (Al-Ga)C MAX phases using machine-learning driven high-throughput framework to understand the oxidation resistance behavior with increasing temperature and exposure to static oxygen. The A-site (at Al) disordering of Ti 2 AlC with Ga shows significant change in the chemical activity of Al with increasing temperature and exposure to static oxygen, which is expected to enable surface segregation of Al, thereby, the formation of Al 2 O 3 and improved oxidation resistance. We performed in-depth convex hull analysis of ternary Ti-Al-C, Ti-Ga-C, and Ti-Al-Ga-C based MAX phase, and provide detailed contribution arising from electronic, chemical and vibrational entropies. The thermodynamic analysis shows change in the Gibbs formation enthalpy (ΔG form ) at higher temperatures, which implies an interplay of temperature-dependent enthalpy and entropic contributions in oxidation resistance Ga doped Ti 2 AlC MAX phases. A detailed electronic structure and chemical bonding analysis using crystal orbital Hamilton population method reveal the origin of change in phases stability and in oxidation resistance in disorder Ti2(Al 1-x Ga x )C MAX phases. Our electronic structure analysis correlate well with the change in oxidation resistance of Ga doped MAX phases. We believe our study provides a useful guideline to understand to role of alloying on electronic, thermodynamic, and oxidation related mechanisms of bulk MAX phases, which can work as a precursor to understand oxidation behavior of twodimensional MAX phases, i.e., MXenes (transition metal carbides, carbonitrides and nitrides).

36 MATERIALS SCIENCE↗

DOE OSTI2026

The detection of marine microseismic activity with the CUORE tonne-scale cryogenic experiment

Vibrations from experimental setups and the environment are a persistent source of noise for low-temperature calorimeters searching for rare events, including neutrinoless double beta ( 0νββ ) decay or dark matter interactions. Such noise can significantly limit experimental sensitivity to the physics case under investigation. Here, we report the detection of marine microseismic vibrations using mK-scale calorimeters. This study employs a multi-device analysis correlating data from CUORE, the leading experiment in the search for 0νββ decay with mK-scale calorimeters, and the Copernicus Earth Observation program, revealing the seasonal impact of Mediterranean Sea activity on CUORE’s energy thresholds, resolution, and sensitivity over four years. The detection of marine microseisms underscores the need to address faint environmental noise in ultra-sensitive experiments. Understanding how such noise couples to the detector and developing mitigation strategies is essential for next-generation experiments. We demonstrate one such strategy: a noise decorrelation algorithm implemented in CUORE using auxiliary sensors, which reduces vibrational noise and improves detector performance. Enhancing sensitivity to 0νββ decay and to rare events with low-energy signatures requires identifying unresolved noise sources, advancing noise reduction methods, and improving vibration suppression systems, all of which inform the design of next-generation rare event experiments.

experimental nuclear physics↗