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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

High-resolution national mapping of natural gas composition substantially updates methane leakage impacts

Methane is emitted from oil and gas operations alongside heavier hydrocarbons and non-hydrocarbon gases, shaping emissions management decision-making, including air quality impacts. Yet, most assessments assume fixed gas composition, overlooking significant spatial and temporal variations. Here, we generate a high-resolution, data-driven map of natural gas composition across the United States, reconstructing methane, heavier hydrocarbons, and non-hydrocarbon species using spatio-temporal interpolation and oil-and-gas production patterns. Our approach is able to reduce composition prediction errors by 39% in terms of Mean Absolute Error (MAE) compared to standard techniques and reveals that methane loss rates have been underestimated by more than 50% in some regions. Beyond methane, we uncover substantial variability in co-emitted gases, exposing blind spots in current emissions inventories and emissions management frameworks. Our work enables more accurate emissions assessments, guides targeted measurement strategies, and informs emissions management decision-making. It also provides a general framework for prediction in environmental applications that integrate sparse measurements with auxiliary variables.

03 NATURAL GAS↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Low-spin cobalt(ii) redox shuttle by isocyanide coordination

Coordination of the strong-field ligand 2,6-dimethylphenyl isocyanide (DMP-CN) to the Co(PY5Me 2 ) framework, where PY5Me 2 represents the pentadentate ligand 2,6-bis(1,1-bis(2-pyridyl)ethyl)pyridine, has resulted in a new low-spin Co(II) complex with a relatively low reorganization energy and fast electron-transfer kinetics compared to the prototypical cobalt tris-bipyridine redox shuttle, [Co(bpy) 3 ] 3+/2+ , where bpy represents 2,2'-bipyridine. Despite nearly 160 mV reduced regeneration driving force, the [Co(PY5Me 2 )(DMP-CN)]3+/2+ redox shuttle displayed an increased regeneration efficiency relative to [Co(bpy)3] 3+/2+ , however recombination losses hinder the performance as evidenced by a reduced recombination resistance. Finally, future directions point to low-spin Co(II) redox shuttles with more negative redox potentials to be paired with a dye or dye mixture that absorbs into the near infrared region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

CO 2 fertilization of terrestrial photosynthesis inferred from site to global scales

Significance The magnitude of the CO 2 fertilization effect on terrestrial photosynthesis is uncertain because it is not directly observed and is subject to confounding effects of climatic variability. We apply three well-established eco-evolutionary optimality theories of gas exchange and photosynthesis, constraining the main processes of CO 2 fertilization using measurable variables. Using this framework, we provide robust observationally inferred evidence that a strong CO 2 fertilization effect is detectable in globally distributed eddy covariance networks. Applying our method to upscale photosynthesis globally, we find that the magnitude of the CO 2 fertilization effect is comparable to its in situ counterpart but highlight the potential for substantial underestimation of this effect in tropical forests for many reflectance-based satellite photosynthesis products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Statistics of Green's functions on a disordered Cayley tree and the validity of forward scattering approximation

The accuracy of the forward scattering approximation for two-point Green's functions of the Anderson localization model on the Cayley tree is studied. A relationship between the moments of the Green's function and the largest eigenvalue of the linearized transfer-matrix equation is proved in the framework of the supersymmetric functional-integral method. The new large-disorder approximation for this eigenvalue is derived and its accuracy is established. Using this approximation the probability distribution of the two-point Green's function is found and compared with that in the forward scattering approximation (FSA). It is shown that FSA overestimates the role of resonances and thus the probability for the Green's function to be significantly larger than its typical value. The error of FSA increases with increasing the distance between points in a two-point Green's function.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Preparing Distribution Utilities for Utility-Scale Storage and Electric Vehicles: A Novel Analytical Framework

Emerging distributed energy resources (DERs)—such as solar photovoltaics (PV), battery energy storage systems (BESS), and electric vehicles (EVs)—are expected to increase substantially in India in the coming years following policy-driven targets of the Government of India to modernize its electricity system, reduce greenhouse gas emissions (GHGs), and improve air quality. These emerging technologies can pose challenges to distribution utilities, forcing overhauls in planning and operational practices. They can also create challenges in power system infrastructure planning and cause more frequent system operational violations (e.g., network voltage bounds and loading thresholds) if not properly integrated. The impacts on the localized power distribution grid from these emerging technologies manifest in increased infrastructure investments and erratic shifts in demand patterns. These impacts are not yet well understood, and analytic solutions are not readily available. To address these challenges, the National Renewable Energy Laboratory (NREL), in collaboration with BSES Rajdhani Power Ltd. (BRPL), developed an advanced power distribution system impact analysis framework of BRPL’s distribution system. This framework helps analyze the readiness of the power distribution network to accommodate emerging technologies and the potential opportunities they might introduce. The framework has been predominantly set up to evaluate distributed PV, BESS, and EVs. In this collaboration between NREL and BRPL, we developed and evaluated the framework on two distribution feeders in the BRPL territory for various scenarios of BESS and EVs. BESS are evaluated for their effectiveness on the grid to mitigate present and future feeder overloading scenarios, and they are subsequently analyzed for their costs compared to the costs of traditional upgradation measures. Scenarios include assessing the effects of EV density on grid infrastructure upgrades and interlinking EV management with BESS integration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Building a better framework for evaluating human well-being impacts in global change analysis: The example of energy security

Human well-being can be greatly impacted by the global environmental and socio-economic change captured in Integrated Models of Global Change (IMGCs). Though most IMGCs address some aspects of well-being, their underlying modeling approach and ‘philosophy’ differ widely, and some key elements – like energy security – are omitted. In this report, we describe a project in which we set out to a create a framework through which the well-being dimensions of the household are connected to key drivers of socio-economic and environmental change – and how the needed metrics, data and modeling methods can be brought to bear. We focus on the well-being dimensions of energy, and lay out the necessary elements to capturing household energy security – using household energy burden as the relevant metric. We begin by showing the conceptual linkage of energy burden to environmental drivers like temperature change, using a simple and straightforward conceptual framework. We then go further to use the example of GCAM-USA to show how some key analytical features of the model can provide insight into how energy security across different groups can change along alternative pathways to sustainability. We compare our preliminary assessment of household energy burden to existing data and suggest further steps to improve and refine this analysis in future research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Incorporation of Ion Transport Chains into Multivariate MOF for Improved Water Oxidation

The climate crisis demands clean energy technologies to cut CO 2 emissions from fossil fuels. Hydrogen fuel cells and solar-driven CO 2 reduction are promising, but both rely on efficient water oxidation. Polypyridyl ruthenium complexes are active catalysts for water oxidation; however, they exhibit poor stability and recyclability. Our group improved performance by embedding these complexes into metal−organic frameworks (MOFs). As water oxidation is pH-dependent, proton management further enhances reactivity. To address the issue, we introduced proton transfer pathways into the MOF structure. Specifically, we incorporated −SO 3 H groups onto the biphenyl linkers of UiO-67 loaded with [Ru(tpy)(dcbpy)OH 2 ]PF 6 catalyst (where tpy = 2,2′:6′,2″-terpyridine; dcbpy = 5,5-dicarboxy-2,2′- bipyridine). The sulfonated MOF exhibited a 2.5-fold increase in oxygen evolution compared to the nonsulfonated analogue. After 1 h of electrolysis, the sulfonated MOF exhibited a turnover number of 25 for oxygen evolution reaction compared to 10 for the native MOF, demonstrating the benefits of built-in proton management.

Catalysts↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗
DOE OSTI2023

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Li, Jinyang↗
DOE OSTI2025

Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems

Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.

25 ENERGY STORAGE↗
DOE OSTI2022

Trait relationships of fungal decomposers in response to drought using a dual field and laboratory approach

Abstract Decomposer fungi play a fundamental role in terrestrial ecosystem dynamics. In the southwestern United States, climate change is causing more frequent and severe droughts, which may alter fungal community composition and activity. Investigating relationships between fungal traits may improve the prediction of fungal responses to drought. In this dual field and laboratory experiment, we examine whether trade‐offs occur between traits associated with drought. Specifically, we test the hypothesis that fungi sort into lifestyles specializing in growth y ield, resource a cquisition, and drought s tress tolerance (“YAS” framework). For the field experiment, we constructed microbial “cages” containing sterilized litter and 1 of 10 fungal isolates. These cages were placed in long‐term drought and control plots in a southern Californian grassland for 6 and 12 months. We measured fungal hyphal length per unit litter mass loss for growth yield, the potential activities of four extracellular enzymes for resource acquisition, and the ability to grow in the drought versus control plots for drought stress tolerance. We compared these results with a laboratory microcosm experiment constructed with the same fungal isolates and that measured the same fungal traits. The field experiment corroborated our laboratory results, in that no trade‐offs were observed between growth yield and resource acquisition traits. However, in contrast to the laboratory experiment, drought tolerance was negatively related to extracellular enzyme activity and growth yield in the field, implying a trade‐off. Despite this observed trade‐off in the field, growth yield was not hindered by drought. We propose a modification to the YAS framework, by combining the growth yield and resource acquisition lifestyles, which may be more appropriate for this arid system. This joint laboratory and field approach contextualizes a theoretical framework in microbial ecology and improves understanding of fungal community response to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Flood Estimation under Snowmelt and Rain-on-Snow Processes in Alaska: A Military Installation Perspective

Accurate flood estimation in snow-dominated and high-latitude regions remains challenging due to complex interactions among rainfall, snowmelt, and rain-on-snow (ROS) processes, which are not captured in conventional precipitation-based intensity-duration-frequency (PREC-IDF) curves. This study evaluates the Next-Generation IDF (NG-IDF) framework, an extension of PREC-IDF that incorporates total water available for runoff (precipitation minus changes in snow water equivalent), in two contrasting Alaskan watersheds of Department of Defense (DoD) significance: the Little Chena River Basin (LCRB) in interior Alaska, a tributary of the Chena River that flows through Fort Wainwright and near Eielson Air Force Base, and the Upper Ship Creek Basin (USCB), which drains Joint Base Elmendorf-Richardson near Anchorage. Using long-term SNOTEL observations and event-based rainfall-runoff modeling, NG-IDF and PREC-IDF flood estimates were compared against observation-based flood frequency analyses. Results show that NG-IDF consistently reduces flood-estimation bias by 15–20% relative to PREC-IDF, particularly for snowmelt- and ROS-dominated events. In the interior LCRB, permafrost conditions can substantially amplify flood responses during snowmelt events, an effect not explicitly represented in standard design tools. These findings demonstrate that NG-IDF provides a more physically consistent and transferable framework for flood estimation in cold regions, with potential relevance to mission-critical DoD installation resilience. Projected increases in ROS frequency and permafrost degradation across Alaska further emphasize the need to integrate physics-based hydrologic models that explicitly represent snow and permafrost processes to enhance design resilience and operational readiness for military and other critical infrastructure.

Yan, Hongxiang (ORCID:000000022387403X)↗
DOE OSTI2023

Bayesian Monte Carlo Evaluation of Imperfect (n, 233 U) Data and Model

Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g., imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Psychosocial drivers of land management behaviour: How threats, norms, and context influence deforestation intentions

Understanding how private landholders make deforestation decisions is of paramount importance for conservation. Behavioural frameworks from the social sciences have a lot to offer researchers and practitioners, yet these insights remain underutilised in describing what drives landholders' deforestation intentions under important political, social, and management contexts. Using survey data of private landholders in Queensland, Australia, we compare the ability of two popular behavioural models to predict future deforestation intentions, and propose a more integrated behavioural model of deforestation intentions. We found that the integrated model outperformed other models, revealing the importance of threat perceptions, attitudes, and social norms for predicting landholders' deforestation intentions. Social capital, policy uncertainty, and years of experience are important contextual moderators of these psychological factors. Here, we conclude with recommendations for promoting behaviour change in this deforestation hotspot and highlight how others can adopt similar approaches to illuminate more proximate drivers of environmental behaviours in other contexts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Toward a sustainable circular economy of multilayer plastic films: Life cycle and techno-economic assessment with a focus on end-of-life treatment and multiple recovery cycles

This study presents a life cycle assessment (LCA) and techno-economic analysis (TEA) of end-of-life technologies for treating polyethylene–polyamide barrier film waste, focusing on quality degradation across recovery cycles. Novel treatment methods are experimentally validated, while others are drawn from literature and industry consultations. A displacement approach, assuming no quality loss, is first applied. Results show that solvent-based recycling via the solvent-targeted recovery and precipitation (STRAP) process outperforms alternatives across environmental indicators, reducing global warming potential (GWP) by 40% compared to landfilling. Incineration performs worst in most categories, particularly eutrophication (80% higher than landfilling), due to nitrogen emissions. Experimentally validated downcycling (pelletizing) proves more economically viable. The assumption of infinite recoverability is overly optimistic. To address this, we propose a mathematical framework accounting for a finite number of recovery cycles. This refined model shows reduced GWP and cost savings for solvent recovery, making its benefits less pronounced than initially estimated. Sensitivity and uncertainty analyses reveal strong dependence on recovered material quality and solvent recovery efficiency, underscoring the need for optimized process design. Finally, hotspot analysis identifies greenhouse gas emissions from the polyamide supply chain as the dominant GWP contributor. In conclusion, the results underscore potential trade-offs across pathways and show that solvent-based recovery’s sustainability depends heavily on process conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ab Initio Study of Vibronic and Magnetic 5f-to-5f and Dipole-Allowed 5f-to-6d and Charge-Transfer Transitions in [UX 6 ] n – (X = Cl, Br; n = 1, 2)

The absorption spectra of the octahedral [UX 6 ] n– (X = Cl, Br; n = 1, 2) complexes in the near-infrared (NIR) and UV–vis spectral regions were studied theoretically, using a relativistic restricted active space second-order perturbation theory (RASPT2) wavefunction framework, with the spin–orbit (SO) coupling treated by state interaction, in conjunction with Kohn–Sham density functional theory calculations for determining the vibrational normal modes. The electric-dipole-allowed ligand-to-metal charge-transfer (LMCT) and 5f-to-6d transitions, and the electric-dipole-forbidden 5f-to-5f ligand field (LF) transitions, are thereby obtained within the same theoretical framework. For the 5f-to-5f LF transitions, the observed absorption intensity is mostly due to vibronic coupling with low-energy electric-dipole-allowed transitions, but in some cases, the magnetic dipole intensity of the purely electronic transition has comparable intensity to the vibronic transitions. Here, experimental LF spectra of 5f 2 open-shell systems have been reported decades back, but ab initio calculations of their vibronic intensity have not yet been reported in the literature. Although the LF spectra for the 5f 2 systems can be assigned in detail, based on the calculations, the spectra are very complex and the underlying electronic states are strongly multiconfigurational. Therefore, the usefulness of the LF spectra beyond serving as a “fingerprint” of the LF and the metal oxidation state appears to be limited.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation

Fibrillated cellulose derived from forestry feedstocks represents a renewable and high-strength materials platform for circular bioeconomies. However, its practical implementation is hindered by the irreversible aggregation of nanocellulose architectures, including cellulose nanofibers (CNFs). Solvent-based dispersion offers a simple and practical route to prevent CNF aggregation. Here, in this work, we integrate classical and enhanced sampling molecular dynamics (MD) simulations with experimental suspension rheology and atomic force microscopy (AFM) to elucidate how solvent environments tune CNF–CNF interactions and dispersion stability. CNF–CNF contact free energies computed from MD simulations reveal reduced aggregation in acetone/water, γ-valerolactone (GVL)/water, and tetrahydrofuran (THF)/water and pure acetone compared with pure water, reflecting stronger CNF-solvent relative to inter-CNF interactions. Correspondingly, CNF-solvent suspensions in these solvent systems exhibit stronger inter-fibril network structures and enhanced recovery compared to water, indicating improved CNF-solvent affinity. Liquid cell AFM imaging in acetone–water mixtures and in pure acetone further confirm the presence of well-dispersed CNFs. By combining multiscale computation with targeted experiments, this study establishes a rational framework for solvent design to achieve stable nanocellulose dispersions for high-strength biobased materials and efficient bioenergy conversion.

cellulose↗