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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Strain in Metal Halide Perovskite Thin Films - Interfacial Mechanical Coupling

Hybrid organic−inorganic metal halide perovskites (MHPs) are promising semiconductors for photovoltaics and optoelectronics, but their commercial viability is limited by instability, particularly strain induced by mismatch in coefficients of thermal expansion (CTE) between the perovskite film and substrate. Here, we investigate strain development and relaxation in MHP thin films using in situ bending and Grazing Incidence Wide-Angle X-ray Scattering (GIWAXS). We quantify the film− substrate interfacial mechanical coupling and identify interfacial slippage beyond a critical strain (∼0.4%), with Br-2PACz exhibiting comparatively stronger interfacial mechanical coupling among common interface modifiers. Time-resolved GIWAXS reveals reversible macrostrain during thermal cycling driven by CTE mismatch. Leveraging this behavior, we introduce a prestrain process that induces persistent compressive strain after cooling, with partial relaxation over time. These results provide insight into interfacial mechanical coupling and strain dynamics, offering a framework for strain engineering in perovskite devices.

metals↗
DOE OSTI2024

Toward a Universal Model of Hyporheic Exchange and Nutrient Cycling in Streams

In this paper we demonstrate that several ubiquitous hyporheic exchange mechanisms can be represented simply as a one-dimensional diffusion process, where the diffusivity decays exponentially with depth into the streambed. Based on a meta-analysis of 106 previously published laboratory measurements of hyporheic exchange (capturing a range of bed morphologies, hydraulic conditions, streambed properties, and experimental approaches) we find that the reference diffusivity and mixing length-scale are functions of the permeability Reynolds Number and Schmidt Number. These dimensionless numbers, in turn, can be estimated for a particular stream from the median grain size of the streambed and the stream's depth, slope, and temperature. Application of these results to a seminal study of nitrate removal in 72 headwater streams across the United States, reveals: (a) streams draining urban and agricultural landscapes have a diminished capacity for in-stream and in-bed mixing along with smaller subsurface storage zones compared to streams draining reference landscapes; (b) under steady-state conditions nitrate uptake in the streambed is primarily biologically controlled; and (c) median reaction timescales for nitrate removal in the hyporheic zone are 0.5 and 20 hr for uptake by assimilation and denitrification, respectively. While further research is needed, the simplicity and extensibility of the framework described here should facilitate cross-disciplinary discussions and inform reach-scale studies of pollutant fate and transport and their scale-up to watersheds and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Extreme flood return levels in a U.S. mid-Atlantic estuary using 40-year fluvial-coastal model simulations

Using an integrated watershed-coastal modeling framework, we conducted long-term historical simulations (1980-2019) of fluvial and coastal flooding in the Delaware Bay and River, a vulnerable estuarine system in the U.S., at high spatial resolutions. By applying process-based models, we generated physically consistent and spatially detailed estimates of estuarine, riverine, and surge-driven extreme water level compared to previous studies that used field data only. We then evaluated changes in the magnitude of flood events using the 40-year simulations and detrended Floodwater Depth values with stationary extreme value analysis. Our detailed assessment of spatial-varying extreme values revealed how different flood-generation mechanisms can dominate various zones in the estuary. The datasets produced through this work will be valuable for long-term flood hazard mitigation planning in coastal communities in the Delaware Bay and River region. Additionally, this work will serve as a benchmark for other coastal flood hazard modeling communities worldwide, aiding them in systematically modeling long-term and continuous extreme flood events.

Coastal and Riverine Flooding↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tools for unbinned unfolding

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. Here, in order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Topological nanophotonics for photoluminescence control

Rare-earth-doped nanocrystals are emerging light sources that can produce tunable emissions in colours and lifetimes, which has been typically achieved in chemistry and material science. However, one important optical challenge – polarization of photoluminescence – remains largely out of control by chemistry methods. Control over photoluminescence polarization can be gained via coupling of emitters to resonant nanostructures such as optical antennas and metasurfaces. However, the resulting polarization is typically sensitive to position disorder of emitters, which is difficult to mitigate. Recently, new classes of disorder-immune optical systems have been explored within the framework of topological photonics. Here we explore disorder-robust topological arrays of Mie-resonant nanoparticles for polarization control of photoluminescence of nanocrystals. We demonstrate polarized emission from rare-earth-doped nanocrystals governed by photonic topological edge states supported by zigzag arrays of dielectric resonators. We verify the topological origin of polarized photoluminescence by comparing emission from nanoparticles coupled to topologically trivial and nontrivial arrays of nanoresonators. We expect that our results may open a new direction in the study of topology-enable emission properties of topological edge states in many photonic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Facies Analysis and Depositional Environments of the Upper Cambrian Eau Claire Formation in Central and Northern Illinois

The Cambrian Eau Claire Formation is a confining unit for geologic carbon dioxide (CO2) storage, and potentially a confining unit for hydrogen storage, within the underlying Mount Simon Sandstone in the Central and Northern Illinois Basin. However, extensive regional studies on lateral continuity, environment of deposition, and depositional fabric of the Eau Claire Formation in the Illinois Basin are minimal compared to studies of the underlying Mount Simon Sandstone. This study presents an integrated facies analysis using sedimentological, stratigraphic, ichnological, and mineralogical data to interpret the depositional environments of the Eau Claire Formation, emphasizing the dynamic nature of sedimentary systems. By combining core analyses, thin-section analysis, facies interpretation from geophysical logs, and regional stratigraphic framework interpretation, this work provides insights into the integrated depositional framework across diverse depositional environments. These findings suggest that the Eau Claire Formation in the Central and Northern Illinois Basin was deposited in a shallow marine environment, ranging from tidal flats to offshore settings. Its mixed siliciclastic-carbonate succession was primarily controlled by relative sea-level change.

58 GEOSCIENCES↗
DOE OSTI2020

Multi‐scale simulation of rock compaction through breakage models with microstructure evolution

Abstract. Regional subsidence due to fluid depletion includes the interaction among multiple physical processes. Specifically, rock compaction is governed by coupled hydro-mechanical feedbacks involving fluid flow, effective stress change and pore collapse. Although poroelastic models are often used to explain the delay between depletion and subsidence, recent evidence indicates that inelastic effects could alter the rock microstructure, thus exacerbating coupling effects. Here, a constitutive law built within the framework of Breakage Mechanics is proposed to account for the inherent connection between rock microstructure, hydraulic conductivity, and pore compaction. Furthermore, it is embedded into a 1-D hydromechanical coupled finite element analysis (FEA) to explore the effects of micro-structure rearrangement on the development of reservoir compaction. Numerical examples with the proposed model are compared with simulations under constant hydraulic conductivity to illustrate the model capability to capture the non-linear processes of reservoir compaction induced by fluid depletion.

Buscarnera, Giuseppe↗
DOE OSTI2025

Common practices for quantifying methane emissions from plumes detected by remote sensing

This document provides a set of community-accepted practices for quantifying methane emissions based on plumes detected via spectroscopic remote sensing. Its primary goal is to promote consistency in the generation, validation, reporting, and quality assessment of methane emission estimates derived from remote sensing radiances. Developed by subject matter experts with deep experience across all stages of the measurement process, this guidance reflects a critical evaluation of current methodologies and highlights key practices needed to produce reliable, interoperable, and traceable products. The focus is specifically on methane emissions quantified from distinct plumes originating from localized sources, rather than diffuse emissions spread over large regions, which are beyond the scope of this work. This document is intended to serve both data producers and users. For producers, it offers a framework for aligning with field-recognized standards to ensure their outputs meet rigorous quality and transparency criteria. For users, it provides a reference to assess dataset fitness-for-purpose by highlighting essential metadata, assumptions, and methodological choices that underpin emission estimates. By fostering a shared understanding of best practices, this work aims to enhance comparability, confidence, and utility of remotely sensed methane emission products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗
DOE OSTI2026

Understanding carbon sourcing and transport originating from the helicon antenna surfaces during high-power helicon discharge in DIII-D Tokamak

The high-power helicon wave system in the DIII-D tokamak could potentially introduce new plasma–material interaction (PMI) challenges owing to rectified RF sheath potentials that develop near the antenna and surrounding plasma-facing components. We present the first application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to helicon-induced PMIs, extending previous STRIPE studies of ICRH antennas by incorporating net erosion, local re-deposition, and three-dimensional global impurity transport. The integrated workflow couples SOLPS, COMSOL, RustBCA, GITR, and GITRm to simulate carbon erosion, re-deposition, and impurity transport for two experimentally constrained DIII-D H-mode helicon operating scenarios with different antenna–plasma gaps, coupled RF powers, and edge plasma conditions. COMSOL predicts rectified RF sheath potentials of 1–5 kV localized near the lower portion of the antenna, where the magnetic field intersects the surface at grazing incidence. Carbon self-sputtering dominates the erosion source, whereas RF-accelerated D + ions contribute approximately 1% of the total gross erosion. The smaller-gap operating scenario exhibits substantially stronger gross erosion, enhanced local re-deposition (∼12%), and a larger confined carbon inventory owing to increased plasma accessibility and broader RF sheath coverage. Comparison with available DIII-D measurements shows no distinct change in the global carbon signal that correlates with the helicon RF pulse, consistent with the simulations indicating that the helicon-generated carbon source remains small compared with the existing background carbon inventory under the present graphite-wall operating conditions. These results demonstrate the capability of STRIPE to integrate RF sheath modeling, plasma transport, surface interaction physics, and three-dimensional impurity transport for the interpretation of helicon-induced PMIs. The study further identifies the principal sources of modeling uncertainty, including grazing-angle RF sheath physics, slow-wave resolution, plasma-background extrapolation, and trace-impurity transport assumptions, providing a framework for future validation and model development.

Kumar, Atul [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2020

Demonstrating the Critical Role of Solvation in Supported Ti and Nb Epoxidation Catalysts via Vapor-Phase Kinetics

Catalytic oxidation of hydrocarbons with hydrogen peroxide (H 2 O 2 ) has been of the utmost importance for several decades. The vast majority of studies have been performed in the condensed phase, even though condensed phases introduce complex solvent effects and can promote the leaching of active sites. In response, we have built a custom reactor system to understand H 2 O 2 activation and selective oxidation in the vapor-phase. In this report, we study the epoxidation of cyclohexene with H 2 O 2 over four Lewis-acidic metal oxide catalysts: Ti and Nb grafted on SiO 2 and on the Zr based metal–organic framework, NU-1000. The M–SiO2 materials are highly selective to the formation of epoxides and diols, as they can be in the condensed phase, while the NU-1000 based materials are far more prone to overoxidation to CO 2 , which appears to be connected to their strong reactant adsorption. Apparent activation energies are calculated for all materials when operating in the same kinetic regime, and the heats of cyclohexene adsorption into their pores are then used to directly compare intrinsic enthalpies of activation in the vapor vs condensed phase for the M–SiO 2 catalysts. Nb–SiO 2 catalysts exhibit similar intrinsic enthalpies of activation in the vapor and condensed phases, whereas the condensed phase transition state in Ti–SiO 2 is 24 kJ/mol lower in energy than that of the same material in the vapor phase. These experiments establish another methodology for understanding the various roles of solvent in selective oxidation reactions and studying these reactions under conditions that differ significantly from the thousands of prior studies in the condensed phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A cost–benefit framework to evaluate capacity upgrade options in overhead line transmission planning

This paper presents the methodology behind the new Reconductoring Economic and Financial Analysis (REFA) tool, an open-access software, used by transmission utilities to evaluate transmission capacity enhancement options. The proposed methodology is intended to be used in a new planning stage, after the capacity expansion and prior to the individual transmission project engineering, allowing capacity upgrade options (reconductoring, rebuild or voltage upgrade), and respective conductor selection, to be compared under the same economic basis. Furthermore, the REFA tool implements a methodology to rank project options and conductor types based on economic criteria, considering an approximation of the ampacity and sag constraints. Results, using 5 real transmission lines in the US, show that least-cost combinations of project and conductor types can be very diverse, which emphasizes the need for the proposed methodology and tool.

Advanced conductors↗
DOE OSTI2022

A -type antiferromagnetic order in the Zintl-phase insulator EuZn 2 P 2

Zintl phases, containing strongly covalently bonded frameworks with separate ionically bonded ions, have emerged as a critical materials family in which to couple magnetism and strong spin-orbit coupling to drive diverse topological phases of matter. Here we report the single-crystal synthesis, magnetic, thermodynamic, transport, and theoretical properties of the Zintl compound EuZn 2 P 2 that crystallizes in the anti-La 2 O 3 (CaAl 2 Si 2 ) P-3m1 structure, containing triangular layers of Eu 2+ ions. In-plane resistivity measurements reveal insulating behavior with an estimated activation energy of E g = 0.11eV. Specific heat and magnetization measurements indicate antiferromagnetic ordering at T N = 23K. Curie-Weiss analysis of in-plane and out of plane magnetic susceptibility from T = 150 to 300 K yields p eff = 8.61 for μ 0 H⊥c and p eff = 7.74 for μ 0 H//c, close to the expected values for the 4f 7 J = S = 7/2 Eu 2+ ion and indicative of weak anisotropy. Below T N , a significant anisotropy of χ ⊥ /χ // ≈ 2.3 develops, consistent with A-type magnetic order as observed in isostructural analogs and as predicted by the density functional theory calculations reported herein. The positive Weiss temperatures of θ W =19.2K for μ 0 H⊥c and θ W =41.9K for μ 0 H//c show a similar anisotropy and suggest competing ferromagnetic and antiferromagnetic interactions. Comparing Eu magnetic ordering temperatures across trigonal EuM 2 X 2 (M= divalent metal, X= pnictide) shows that EuZn 2 P 2 exhibits the highest ordering temperature, with variations in T N correlating with changes in expected dipolar interaction strengths within and between layers and independent of the magnitude of electrical conductivity. These results provide experimental validation of the crystochemical intuition that the cation Eu 2+ layers and the anionic (M 2 X 2 ) 2– framework can be treated as electronically distinct subunits, enabling further predictive materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization

Here, we consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit

We report ambitious targets for carbon emissions reductions are highlighting new challenges for electrification strategies, leading to an increased focus on building load flexibility and energy management to complement the variability inherent in renewable energy generation. Over the next decade millions of existing homes could undergo electrification retrofits, and there is an urgent need to understand the potential impacts of electrifying major residential loads such as water and space heating on community load characteristics, resident energy bills, and the utility's distribution system. Behind-the-meter distributed energy resources (DERs), including efficiency measures, photovoltaics (PV), battery storage, managed electric vehicle (EV) charging, and controls such as home energy management systems (HEMS), can significantly alter a neighborhood's load profile and provide benefits to both the residents and the grid. We present a novel approach to characterizing the impact of a hypothetical neighborhood-scale residential retrofit program on individual homes' energy use profiles, associated utility bills, and the local distribution system. We modeled a mixed-fuel community of 30 single-family homes in Denver, Colorado, and compared the effects of retrofit scenarios ranging from conventional energy-efficiency upgrades to full electrification with and without more advanced DER technologies. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate for this and similar neighborhoods. Our buildings-to-grid co-simulation framework includes a generic secondary distribution feeder model to capture voltage profiles, transformer loading, and other grid impacts in each case. We also calculated the carbon emissions associated with energy use in the community. The methodology developed here can be broadly applied to community-scale beneficial electrification studies in other regions, climates, utility infrastructures, and building typologies to make specific, targeted recommendations based on quantified projections of energy demand in any given community. Our findings indicate that residential electrification can be achieved without negatively impacting the monthly utility bill, and that a combination of conventional energy-efficiency measures, PV, battery, controls, and managed EV charging to maximize a community's demand flexibility is a promising strategy. Adding DERs (especially PV) as part of efficient electrification produces much bigger savings than efficient electrification without DERs. A key barrier is that upgrades require upfront costs, and modest utility bill savings result in long payback periods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. Freno and Carlberg (2019) to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression model that maps features – which comprise error indicators that are derived from standard a posteriori error-quantification techniques – to a random variable for the approximate-solution error at each time instance. The proposed framework considers a wide range of candidate features, regression methods, and additive noise models. We consider primarily recursive regression techniques developed for time-series modeling, including both classical time-series models (e.g., autoregressive models) and recurrent neural networks (RNNs), but also analyze standard non-recursive regression techniques (e.g., feed-forward neural networks) for comparative purposes. Finally, numerical experiments conducted on multiple benchmark problems illustrate that the long short-term memory (LSTM) neural network, which is a type of RNN, outperforms other methods and yields substantial improvements in error predictions over traditional approaches.

42 ENGINEERING↗
DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗