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

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mixed nitrate and metal contamination influences operational speciation of toxic and essential elements

Environmental contamination constrains microbial communities impacting diversity and total metabolic activity. The former S-3 Ponds contamination site at Oak Ridge Reservation (ORR), TN, has elevated concentrations of nitric acid and multiple metals from decades of processing nuclear material. To determine the nature of the metal contamination in the sediment, a three-step sequential chemical extraction (BCR) was performed on sediment segments from a core located upgradient (EB271, non-contaminated) and one downgradient (EB106, contaminated) of the S-3 Ponds. The resulting exchangeable, reducing, and oxidizing fractions were analyzed for 18 different elements. Comparison of the two cores revealed changes in operational speciation for several elements caused by the contamination. Those present from the S-3 Ponds, including Al, U, Co, Cu, Ni, and Cd, were not only elevated in concentration in the EB106 core but were also operationally more available with increased mobility in the acidic environment. Other elements, including Mg, Ca, P, V, As, and Mo, were less operationally available in EB106 having decreased concentrations in the exchangeable fraction. Here, the bioavailability of essential macro nutrients Mg, Ca, and P from the two types of sediment was determined using three metal-tolerant bacteria previously isolated from ORR. Mg and Ca were available from both sediments for all three strains; however, P was not bioavailable from either sediment for any strain. The decreased operational speciation of P in contaminated ORR sediment may increase the dependence of the microbial community on other pools of P or select for microorganisms with increased P scavenging capabilities. Hence, the microbial community at the former S-3 Ponds contamination site may be constrained not only by increased toxic metal concentrations but also by the availability of essential elements, including P.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deciphering the multiple deformation mechanisms responsible for sustained work hardening in a FeCrCoNi medium entropy alloy

Two important and desirable properties of materials for most structural applications are high tensile strength and ductility, which typically require high work hardening to delay necking. Here, in this work, we designed and tensile tested a face-centered cubic (fcc) Fe-Cr-Co-Ni medium-entropy alloy in which multiple deformation mechanisms are triggered during tensile loading at different temperatures to induce sustained work hardening. Our strategy involved control of the relative stabilities of the fcc, hcp (hexagonal close-packed), and bcc (body-centered cubic) phases in this quaternary system via high-throughput thermodynamic calculations. This alloy not only exhibits extensive deformation-induced nanotwinning at room temperature, but also displays a two-step sequential phase transformation [γ (fcc) → ε (hcp) martensite → α’ (bcc) martensite] at 77 K, which contrasts with the single-step phase transformation [γ → ε martensite] observed in many other fcc high/medium entropy alloys with a low stacking fault energy. The sequence of phase transformation at 77K was supported by first-principles density functional theory calculations. This work provides new templates for the design of alloys capable of multiple deformation mechanisms for sustained work hardening.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synthesis of multicomponent oxygen evolution reaction coatings via block copolymer templating with vapor- and solution-phase precursors

Porous mixed transition metal oxide heterostructures are promising electrocatalysts due to their high surface area. However, achieving conformal multicomponent oxide coatings with controlled nanoscale architectures remains challenging. Here, we report a synthesis strategy that integrates solution-based swelling infiltration (SBI) with gas-phase sequential infiltration synthesis (SIS) in a block copolymer template to fabricate porous, high-surface-area, conformal mixed-oxide electrocatalytic coatings. In this approach, a PS75-P4VP25 block copolymer (BCP) film is first infiltrated with transition metal acetylacetonate precursors via SBI, followed by exposure to gas phase precursors of ZnO via SIS process. Thermal annealing of the infiltrated BCP films converts them into all-inorganic Fe–ZnO, Fe–Co–ZnO, and Fe–Ni–ZnO coatings. Electrochemical testing on 70 nm thick conformal coatings demonstrates promising oxygen evolution reaction (OER) activity in alkaline media, with mass-specific current densities up to 1.0 × 10 5 mA/g at an overpotential of 330 mV (vs. RHE) at ultralow loading (~0.005 g/cm 2 ). Among the compositions, Fe–Co–ZnO and amorphous Fe–Ni–ZnO show the best OER performance, delivering current densities of 2.00 and 3.04 mA/cm 2 , respectively, compared to 1.52 mA/cm 2 for Fe–ZnO.. This work establishes SBI–SIS as a versatile route for fabricating nm-thin, high-performance multicomponent oxide heterostructures on cost-efficient supports, enabling efficient catalyst utilization in electrochemical energy conversion application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Water treatment based on atomically engineered materials: Atomic layer deposition and beyond

Global water stress and challenges for producing sufficient supplies of fit-for-purpose water are amplifying. Atomically engineered interfaces are emerging as a powerful tool in the fabrication of advanced water treatment materials. Atomic layer deposition (ALD) and recently developed related methods, such as sequential infiltration synthesis (SIS), offer a tremendously diverse library of chemistries for interface functionalization. Thickness, stoichiometry, and physicochemical properties can be manipulated with precision. We review their fundamental physical chemistry and processing factors. ALD/SIS engineering strategies, including direct deposition, growth with intermediate layers, and secondary treatment are presented with realization of efficient water treatment. We lay out a pathway to establishing an ALD/SIS-based universal functionalization platform for water treatment, including sensitization strategies, in situ regulation, secondary reactions, and simulation/machine learning. Furthermore, we also provide a perspective on ALD/SIS-based interface engineering via synergy with other widely used interface engineering techniques to develop facile, versatile, and energy-efficient strategies for tackling increasingly complex water challenges.

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

Hyperspectral imaging for real-time waste materials characterization and recovery using endmember extraction and abundance detection

Hyperspectral imaging, combined with advanced spectral unmixing techniques and artificial intelligence, offers a powerful solution for improving material identification and classification. Here, this study evaluates the effectiveness of the pixel purity index and the sequential maximum angle convex cone algorithms in extracting and validating spectral signatures from pure samples of paper components (cellulose and lignin) and plastic (polypropylene). Principal-component analysis showed that both algorithms captured nearly all relevant variance for the tested materials. Spectral signatures were compared using the spectral angle mapper, revealing high similarity in the short-wave infrared region and greater variability in the visible near-infrared range. The methodology was then applied to a disposable coffee cup to detect and quantify mixed materials, accurately estimating material abundance and object area with less than 1% error. This approach enhances material classification, supporting product verification, quality control, and automated sorting for sustainable waste management and resource recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fabrication of nanoporous doped metal oxide coatings via selective infiltration of a block copolymer template: The case of Al-doped zinc oxide

Conductive nanoporous conformal coatings are important for various applications including touchscreens, displays, and electrochromic windows, though design of such coating with controlled porosity and composition remains challenging. We propose a sequential infiltration synthesis (SIS) method for the fabrication of nanoporous conductive aluminum-doped zinc oxide (AZO) films by infiltrating block copolymer (BCP) templates, specifically polystyrene-block-polyvinyl pyridine (PS-b-P4VP), with diethyl zinc and trimethyl aluminum precursors. Tunability of both porosity and electrical conductivity is achieved by precisely controlling the swelling behavior of the polymer templates and adjusting the number of SIS cycles. Further, our results reveal that porosity levels can reach up to 80 %, with resistivity as low as ~7.83 Ωcm. Using conductive atomic force microscopy (C-AFM) and Hall effect measurements, we show that lower concentrations of aluminum doping relative to zinc (around 1: 17) results in higher conductivity of the AZO films. Additionally, AZO is based on zinc oxide (ZnO) which is cost-efficient, abundant and less toxic than commonly used transparent conductive oxides such as indium tin oxide (ITO) and fluorine-doped tin oxide (FTO). Therefore, this novel approach opens new possibilities for creating highly porous, conformal, conductive AZO coatings with customizable porosity and composition, making them ideal candidates for diverse optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of substrate temperature on the growth of Nb 3 Sn film on Nb by multilayer sputtering

Here, we report on the fabrication of niobium tin (Nb 3 Sn) films by multilayer sequential sputtering on niobium at substrate temperatures ranging from room temperature to 250 °C. The multilayers were then annealed inside a separate vacuum furnace at 950 °C for 3 h. The material properties of the films were characterized by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, atomic force microscopy, and transmission electron microscopy. The superconducting properties of the films were studied by four-point probe resistivity measurements from room temperature to below the superconducting critical temperature T c . The highest film T c was 17.76 K, obtained when the multilayers were deposited at room temperature. When the deposition temperature was raised to 250 °C, a significant reduction in voids was achieved while the film's T c was 17.58 K.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Amine-Thiol/Selenol Chemistry for Efficient and Stable Perovskite Solar Cells

Controlling the crystallization of perovskites is imperative to reduce defect densities in perovskite thin films and extend device lifetimes. In this work, combinations of amine and chalcogenide ligands were introduced in the sequential deposition method to fabricate highly crystalline and orientated formamidinium lead iodide thin films with reduced defect densities and increased charge carrier lifetimes. The dual additives can tune the perovskite intermediate state and control the crystallization leading to devices with improved efficiencies and stabilities. While thiophenol failed to prevent the amine ligand from degrading the perovskite precursors, benzene selenol combinations with amine-ligands drastically changed the solution chemistry to increase the PbI 2 conversion to highly crystalline and orientated α-FAPbI 3 films with lower defect densities. X-ray photoelectron spectroscopy studies reveal benzene selenol evaporates from the thin film leaving behind a modified surface, which is associated with the amine additive. These results indicate the amine selection can be used to tune the surface properties. Lastly, we propose a highly tunable I 2 reduction strategy using chalcogenide chemistry to help enable the realization of perovskite solar cells with high performance and stability.

defects↗
DOE OSTI2022

Porous but Mechanically Robust All-Inorganic Antireflective Coatings Synthesized using Polymers of Intrinsic Microporosity

Herein, we introduce polymer of intrinsic microporosity 1 (PIM-1) to design single-layer and multilayered all-inorganic antireflective coatings (ARCs) with excellent mechanical properties. Using PIM-1 as a template in sequential infiltration synthesis (SIS), we can fabricate highly uniform, mechanically stable conformal coatings of AlO x with porosities of ~50% and a refractive index of 1.41 compared to 1.76 for nonporous AlO x that is perfectly suited for substrates commonly used in high-end optical systems or touch screens (e.g., sapphire, conductive glass, bendable glass, etc.). We show that such films can be used as a single-layer ARC capable of reduction of the Fresnel reflections of sapphire to as low as 0.1% at 500 nm being deposited only on one side of the substrate. We also demonstrate that deposition of the second layer with higher porosity using block copolymers enables the design of graded-index double-layered coatings. AlO x structures with just two layers and a total thickness of less than 200 nm are capable of reduction of Fresnel reflections under normal illumination to below 0.5% in a broad spectral range with 0.1% reflection at 700 nm. Additionally, and most importantly, we show that highly porous single-layer and graded-index double-layered ARCs are characterized by high hardness and scratch resistivity. The hardness and the maximum reached load were 7.5 GPa and 13 mN with a scratch depth of about 130 nm, respectively, that is very promising for the structures consisting of two porous AlO x layers with 50% and 85% porosities, correspondingly. Such mechanical properties of coatings can also allow their application as protective layers for other optical coatings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Control of electron recollision and molecular nonsequential double ionization

Abstract Intense laser pulses lasting a few optical cycles, are able to ionize molecules via different mechanisms. One such mechanism involves a process whereby within one optical period an electron tunnels away from the molecule, and is then accelerated and driven back as the laser field reverses its direction, colliding with the parent molecule and causing correlated non-sequential double ionization (NSDI). Here we report control over NSDI via spectral-phase pulse shaping of femtosecond laser pulses. The measurements are carried out on ethane molecules using shaped pulses. We find that the shaped pulses can enhance or suppress the yield of dications resulting from electron recollision by factors of 3 to 6. This type of shaped pulses is likely to impact all phenomena stemming from electron recollision processes induced by strong laser fields such as above threshold ionization, high harmonic generation, attosecond pulse generation, and laser-induced electron diffraction.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Toward Statistical Real-Time Power Fault Detection

We propose statistical fault detection methodology based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The paper includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates.

bolted faults↗
DOE OSTI2020

An Analysis of Post Attack Impacts and Effects of Learning Parameters on Vulnerability Assessment of Power Grid

Due to the increasing number of heterogeneous devices connected to electric power grid, the attack surface increases the threat actors. Game theory and machine learning are being used to study the power system failures caused by external manipulation. Most of existing works in the literature focus on one-shot process of attacks and fail to show the dynamic evolution of the defense strategy. In this paper, we focus on an adversarial multistage sequential game between the adversaries of the smart electric power transmission and distribution system. We study the impact of exploration rate and convergence of the attack strategies (sequences of action that creates large scale blackout based on the system capacity) based on the reinforcement learning approach. We also illustrate how the learned attack actions disrupt the normal operation of the grid by creating transmission line outages, bus voltage violations, and generation loss. This simulation studies are conducted on IEEE 9 and 39 bus systems. The results show the improvement of the defense strategy through the learning process. The results also prove the feasibility of the learned attack actions by replicating the disturbances created in simulated power system.

attack impacts↗
DOE OSTI2020

Advanced Multilevel Monte Carlo Methods

This article reviews the application of some advanced Monte Carlo techniques in the context of multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations, which can be biassed in some sense, for instance, by using the discretization of an associated probability law. The MLMC approach works with a hierarchy of biassed approximations, which become progressively more accurate and more expensive. Additionally, using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider some Markov chain Monte Carlo and sequential Monte Carlo methods, which have been introduced in the literature, and we describe different strategies that facilitate the application of MLMC within these methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

On the Training and Generalization of Deep Operator Networks

Here, we present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two subnetworks, namely the branch and trunk networks. Typically, the two subnetworks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram–Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

deep operator networks↗
DOE OSTI2020

A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet

We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters. For example, based on the same ice sheet model and data set, the 99th percentile of the Antarctic ice sheet contribution to sea level rise in 2300 increases from 6.5 m to 13.1 m when we increase the number of calibrated parameters from three to 11. With previous calibration methods, it would be challenging to go beyond five parameters. Here, this work provides an important next step toward improving the uncertainty quantification of complex, computationally intensive and decision-relevant models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Fast Temporal Decomposition Procedure for Long-Horizon Nonlinear Dynamic Programming

We propose a fast temporal decomposition procedure for solving long-horizon nonlinear dynamic programs. The core of the procedure is sequential quadratic programming (SQP) that utilizes a differentiable exact augmented Lagrangian as the merit function. Within each SQP iteration, we approximately solve the Newton system using an overlapping temporal decomposition strategy. We show that the approximate search direction is still a descent direction of the augmented Lagrangian provided the overlap size and penalty parameters are suitably chosen, which allows us to establish the global convergence. Moreover, we show that a unit step size is accepted locally for the approximate search direction and further establish a uniform, local linear convergence over stages. This local convergence rate matches the rate of the recent Schwarz scheme (Na et al. 2022). However, the Schwarz scheme has to solve nonlinear subproblems to optimality in each iteration, whereas we only perform a single Newton step instead. Numerical experiments validate our theories and demonstrate the superiority of our method.

97 MATHEMATICS AND COMPUTING↗