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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Solution–Processable Thermally Crosslinked Organic Radical Polymer Battery Cathodes

Organic radical polymers are promising cathode materials for next-generation batteries because of their rapid charge transfer and high cycling stability. However, these organic polymer electrodes gradually dissolve in the electrolyte, resulting in capacity fade. Several crosslinking methods have been developed to improve the performance of these electrodes, but they are either not compatible with carbon additives or compromise the solution processability of the electrodes. A one-step post-synthetic, carbon-compatible crosslinking method was developed to effectively crosslink an organic polymer electrode and allow for easy solution processing. The highest electrode capacity of 104 mAh g –1 (vs. a theoretical capacity of 111 mAh g –1 ) is achieved by introducing 1 mol % of the crosslinker, whereas the highest capacity retention (99.6 %) is obtained with 3 mol % crosslinker. In addition, mass transfer was observed in situ by using electrochemical quartz crystal microbalance with dissipation monitoring. Furthermore, these results may guide future electrode design toward fast-charging and high-capacity organic electrodes.

25 ENERGY STORAGE↗
DOE OSTI2020

Solution structure for an Encephalitozoon cuniculi adrenodoxin-like protein in the oxidized state

Encephalitozoon cuniculi is a unicellular, obligate intracellular eukaryotic parasite in the Microsporidia family and one of the agents responsible for microsporidosis infections in humans. Like most Microsporidia, the genome of E. cuniculi is markedly reduced and the organism contains mitochondria-like organelles called mitosomes instead of mitochondria. Here we report the solution NMR structure for a protein physically associated with mitosome-like organelles in E. cuniculi, the 128-residue, adrenodoxin-like protein Ec-Adx (UniProt ID Q8SV19) in the [2Fe-2S] ferredoxin superfamily. Oxidized Ec-Adx contains a mixed four-strand β-sheet, β2-β1-β4-β3 (↓↑↑↓), loosely encircled by three α-helices and two 310-helices. This fold is similar to the structure observed in other adrenodoxin and adrenodoxin-like proteins except for the absence of a fifth anti-parallel β-strand next to β3 and the position of α3. Cross peaks are missing or cannot be unambiguously assigned for 20 amide resonances in the 1 H- 15 N HSQC spectrum of Ec-Adx. These missing residues are clustered primarily in two regions, G48-V61 and L94-L98, containing the four cysteine residues predicted to ligate the paramagnetic [2Fe-2S] cluster. Missing amide resonances in 1 H- 15 N HSQC spectra are detrimental to NMR-based solution structure calculations because 1 H- 1 H NOE restraints are absent (glass half-empty) and this may account for the absent β-strand (β5) and the position of α3 in oxidized Ec-Adx. On the other hand, the missing amide resonances unambiguously identify the presence, and immediate environment, of the paramagnetic [2Fe-2S] cluster in oxidized Ec-Adx (glass half-full).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Use of Frit‐Disc Crucible Sets to Make Solution Growth More Quantitative and Versatile

The recent availability of step‐edge, frit‐disc crucible sets (generally sold as Canfield Crucible Sets or CCS) has led to multiple innovations associated with the group's use of solution growth. The use of CCS allows for the clean separation of liquid from solid phases during the growth process. This clean separation enables the reuse of the decanted liquid, either allowing for simple, economic, savings associated with recycling expensive precursor elements or allowing for the fractionation of a growth into multiple, small steps, revealing the progression of multiple solidifications. Clean separation of liquid from solid phases also allows for the determination of the liquidus line (or surface) and the creation, or correction, of composition–temperature phase diagrams. The reuse of clean decanted liquid has also allowed to prepare liquids ideally suited for the growth of large single crystals of specific phases by tuning the composition of the melt to the optimal composition for growth of the desired phase, often with reduced nucleation sites. Finally, it is discussed how solution growth and CCS use can be harnessed to provide a plethora of composition–temperature data points defining liquidus lines or surfaces with differing degrees of precision to either test or anchor artificial intelligence and/or machine‐learning‐based attempts to augment and extend the limited experimentally determined database.

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

A solution framework for linear PDE-constrained mixed-integer problems

Abstract We present a general numerical solution method for control problems with state variables defined by a linear PDE over a finite set of binary or continuous control variables. We show empirically that a naive approach that applies a numerical discretization scheme to the PDEs to derive constraints for a mixed-integer linear program (MILP) leads to systems that are too large to be solved with state-of-the-art solvers for MILPs, especially if we desire an accurate approximation of the state variables. Our framework comprises two techniques to mitigate the rise of computation times with increasing discretization level: First, the linear system is solved for a basis of the control space in a preprocessing step. Second, certain constraints are just imposed on demand via the IBM ILOG CPLEX feature of a lazy constraint callback. These techniques are compared with an approach where the relations obtained by the discretization of the continuous constraints are directly included in the MILP. We demonstrate our approach on two examples: modeling of the spread of wildfire and the mitigation of water contamination. In both examples the computational results demonstrate that the solution time is significantly reduced by our methods. In particular, the dependence of the computation time on the size of the spatial discretization of the PDE is significantly reduced.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method

While recent advances in deep learning have shown promising efficiency gains in solving time-dependent partial differential equations (PDEs), matching the accuracy of conventional numerical solvers still remains a challenge. One strategy to improve the accuracy of deep learning-based solutions for time-dependent PDEs is to use the learned model as the coarse propagator in the Parareal method and a traditional numerical method as the fine solver. However, successful integration of deep learning into the Parareal method requires consistency between the coarse and fine solvers, particularly for PDEs exhibiting rapid changes such as sharp transitions. Here, to ensure this consistency, we propose using convolutional neural networks (CNNs) to learn the fully discrete time-stepping operator defined by the same numerical scheme employed as the fine solver. We demonstrate the effectiveness of the proposed method in solving the classical and mass-conservative Allen–Cahn (AC) equations. Through iterative updates in the Parareal algorithm, our approach achieves a significant computational speedup compared to traditional fine solvers while converging to high-accuracy solutions. Our results highlight that the proposed hybrid Parareal algorithm effectively accelerates simulations, particularly when implemented on multiple GPUs, and converges to the desired accuracy in only a few iterations. Another advantage of our method is that the CNN model is trained on trajectory-based data generated from random initial conditions, such that the trained model can be used to solve the AC equations with various initial conditions without retraining. This work demonstrates the potential of integrating neural network methods into parallel-in-time frameworks for efficient and accurate simulations of time-dependent PDEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Molar Absorptivities of U(VI), U(IV), and Pu(III) in Nitric Acid Solutions of Various Concentrations Relevant to Developing Nuclear Fuel Recycling Flowsheets

Testing of a co-decontamination (CoDCon) tributyl phosphate (TBP) based solvent extraction flowsheet is being performed to optimize conditions for achieving a target U/Pu ratio in the Pu-containing product. The flowsheet is designed to directly produce a mixture of U and Pu with a U/Pu mass ratio of 7/3. The system is monitored in real time using optical spectroscopy, which allows for control of the U/Pu ratio in the aqueous U/Pu nitrate product. On-line spectrophotometric monitoring of the aqueous stream at the reductive stripping step involves determination of U(VI), U(IV) and Pu(III) concentrations and relies on molar absorptivities of these species in nitric acid at a number of wavelengths corresponding to their peaks’ maxima. The magnitudes of these molar absorptivities are a sensitive function of nitric acid concentration and have to be determined as precisely as possible using an off-line spectrophotometry under well controlled conditions. This step (called training set acquisition) is typically performed not in a high contamination area of radiological glovebox where flow-through cells and other on-line equipment are installed but in a less aggressive environment of a radiological fume hood with much better control of stock solutions quality, dilution factors, optical absorbance scale calibration, etc. This is followed by the calibration transfer between the off-line instrument and the instrument deployed during extraction and stripping runs. This paper reports values of molar absorptivities of U(VI), U(IV), and Pu(III) in nitric acid solution of 0.5 M to 4 M concentration and compares them with available technical literature data. Results of off-line spectrophotometric analysis of selected aqueous grab samples from one of CoDCon runs show satisfactory agreement with total uranium and plutonium concentrations determined in the same samples by ICP-MS.

Sinkov, Sergey I.↗
DOE OSTI2021

Cluster Variation Method Analysis of Correlations and Entropy in BCC Solid Solutions

Solid solutions occur when multiple chemical species share sites of a common crystal lattice. Although the single site occupation is random, chemical interaction preferences bias the occupation probabilities of neighboring sites, and this bias reduced the entropy of mixing below its ideal value. Sufficiently strong bias leads to symmetry-breaking phase transitions. Here, we apply the cluster variation method to explore solid solutions on body centered cubic lattices in the context of two specific compounds that exhibit opposite ordering trends. Employing density functional theory to model the energetics, we show that CuZn exhibits an order-disorder transition to the CsCl prototype structure, while AlLi instead takes the NaTl prototype structure, and we evaluate their temperature-dependent order parameters, correlations and entropies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Here, we present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

42 ENGINEERING↗
DOE OSTI2022

Extraordinary creep resistance in a non-equiatomic high-entropy alloy from the optimum solid-solution strengthening and stress-assisted precipitation process

Improving creep resistance has commonly been achieved by the optimization of alloy design that results into strong solid-solution strengthening and/or coherent precipitates for dislocation blockage. High-entropy alloys (HEAs), despite their single-phase solid-solution nature, only exhibit creep properties that are comparable to precipitate-strengthened ferritic alloys. Moreover, many HEAs are found to be plagued with many incoherent second phases after long-term annealing, which reduces the lifetime and thus prohibits their usage at elevated temperatures. The present work demonstrates the extraordinary creep resistance of a non-equiatomic Al 0.3 CoCrFeNi HEA, in which the creep strain rate is found to be several orders of magnitude lower than the Cantor alloy and its subsets. Using a suite of characterization tools such as atom probe tomography (APT) and transmission electron microscopy (TEM), it was shown that a B2 precipitate phase that has been widely seen during annealing is suppressed during the early stage of the creep deformation. Currently, metastable and coherent L1 2 precipitates emerge and provide significant creep strengthening. This observation is rationalized by the coupling between the applied stress and the lattice mismatch. In the range of 973 ~ 1033 K, the stress exponent and activation energy were determined to be 3–6.53 and 390–548.2 kJ·mol –1 , respectively. The creep lifetime, on the other hand, is comparable to Cantor subset alloys because the precipitate free zone near the grain boundaries does not provide sufficient constraint for the grain boundary cavity growth. Furthermore, the present work provides a pathway to design novel HEAs with improved creep resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Accelerating the solution of the S N equations with highly anisotropic scattering using the Fokker-Planck approximation

The discrete ordinates method can model forward-peaked transport problems accurately. However, convergence of discrete ordinates solution can become arbitrarily slow upon use of standard iterative procedures like source iteration. Standard zeroth and first moment-based acceleration methods like nonlinear diffusion acceleration and diffusion synthetic acceleration are ineffective in accelerating such problems because these methods do not correct higher order Legendre-moments of angular flux. In this paper, we explore the idea of using the Fokker-Planck approximation as a preconditioner to accelerate the convergence of solution of forward-peaked transport problems. Finally, we observe that such preconditioning can successfully reduce the iteration count by up to four orders of magnitude, and solver runtime by up to three orders of magnitude when compared to diffusion synthetic acceleration.

42 ENGINEERING↗
DOE OSTI2022

Chemical and environmental stability of monazite-cheralite solid solutions Ln 1-2 x Ca x Th x PO 4 (Ln = Pr, Nd; x = 0–0.15): A thermodynamic study

Monazite-cheralite ceramics are a promising waste form for actinides. To elucidate the long-term behavior of this matrix in aqueous solutions, this study measured thermodynamic data for Th-rhabdophanes Ln 1-2 x Ca x Th x PO 4 ·nH 2 O (with Ln = Pr, Nd; x = 0–0.15) and the associated anhydrous monazite-cheralites Ln 1-2 x Ca x Th x PO 4 . Further, solubility experiments at 298K and high temperature oxide melt solution calorimetry were combined for calculation of ΔG$^°_f$, ΔH$^°_f$ and S$^°_m$ of Th-rhabdophanes and associated monazite-cheralites. Standard solubility constants were employed in a geochemical simulation using the PHREEQC software, the results of which confirmed the high chemical stability of the monazite-cheralite phases and supported their use as a specific conditioning matrix for the long-term immobilization of actinides.

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

Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties

Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In this work, our previous approach (Kadeethum et al., 2021d) of conditional generative adversarial networks (cGAN) developed for the solution of steady-state problems involving highly heterogeneous material properties is extended to time-dependent problems by adopting the concept of continuous cGAN (CcGAN). The CcGAN that can condition continuous variables is developed to incorporate the time domain through either element-wise addition or conditional batch normalization. Moreover, this framework can handle training data that contain different timestamps and then predict timestamps that do not exist in the training data. As a numerical example, the transient response of the coupled poroelastic process is studied in two different permeability fields: Zinn & Harvey transformation and a bimodal transformation. The proposed CcGAN uses heterogeneous permeability fields as input parameters while pressure and displacement fields over time are model output. Our results show that the model provides sufficient accuracy with computational speed-up. This robust framework will enable us to perform real-time reservoir management and robust uncertainty quantification in poroelastic problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

High-current-density electrosynthesis of formate from captured CO 2 solution by MOF-derived bismuth nanosheets

Greenhouse gas emissions present a significant challenge to humanity, and utilizing renewable electricity to convert emitted CO 2 into value-added products offers a promising solution; however, traditional CO 2 capture and regeneration processes remain energy-intensive, restricting the overall system efficiency and decarbonization efficacy. In this study, an advanced direct reduction of captured CO 2 with large current densities for formate electrosynthesis was demonstrated without the need for CO 2 regeneration or compression. The bismuth nanosheet (DRM-BiNS) was synthesized by direct reduction of a Bi-based MOF, representing a new class of catalytic materials with a large surface area and interconnected pores, suitable for the direct reduction of captured CO 2 . By seamlessly combining experimentation and simulation, insights into the structure-parameter-performance relation were acquired in a flow cell setting, including critical membrane-electrode distance, cell orientation, and pumping flow rate. Important flow-cell components, such as catholyte volume, electrode substrate, membrane choice, and ionomer type, were also carefully examined to enhance the cell performance. In sharp contrast to prior studies limited to current densities below 20 mA/cm 2 in bicarbonate-based captured CO 2 solutions, this work demonstrates a remarkable current density of 300 mA/cm 2 with an FE to formate comparable to the case with gas-fed CO 2 reduction. Moreover, the process sustained an FE above 50% at a high current density of 500 mA/cm 2 . The DRM-BiNS catalyst exhibited outstanding selectivity, activity, and stability, significantly outperforming oxide-derived bismuth nanosheets (OD-BiNS) in captured CO 2 reduction. Furthermore, these findings offer critical insights into the development of sustainable and scalable CO 2 utilization technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Impact of an SRA (hexylene glycol) on irreversible drying shrinkage and pore solution properties of cement pastes

Cementitious materials shrink when exposed to decreasing relative humidities, which may result in cracking. Shrinkage reducing admixtures (SRAs) can be used to reduce this drying shrinkage. Although many studies have shown that SRAs reduce the surface tension of the pore solution, the effects of SRAs on other pore solution properties and their relationship to drying shrinkage have been poorly characterized. In this work, we investigate the impact of an SRA (hexylene glycol) on the drying and re-humidification of a cement paste over an extended relative humidity range. The reduction in the first drying shrinkage by the SRA depends on relative humidity. The SRA also significantly reduces the irreversible drying shrinkage. We concluded that the SRA impacts drying shrinkage by acting on the capillary forces, by acting on the specific range of relative humidity over which those forces occur, and potentially by acting on the surface stresses through pore wall adsorption.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Combined effect of NaAlO2 and NaOH on the early age hydration of Portland cement with a high concentration of borate solution

The early age hydration of ordinary Portland cement in 5 M borate solution, with or without the addition of NaAlO{sub 2} and/or NaOH, was investigated. Hydration was measured using calorimetry, X-ray diffraction, thermogravimetric analysis, {sup 11}B and {sup 27}Al magic angle spinning NMR analysis, and scanning transmission electron microscopy. The purpose of this study was to reveal the mechanisms behind the retardation caused by a high concentration borate solution and the restoration of hydration through the combined addition of NaOH and NaAlO{sub 2}. Amorphous ulexite, which formed on the surface of the cement particles and prevented the further dissolution of anhydrous phases, was identified as the retardation product. The mechanism of the combined addition of NaAlO{sub 2} and NaOH in overcoming retardation was to transform amorphous ulexite into B-AFt and gowerite (CaB{sub 6}O{sub 10}·5H{sub 2}O). Formation of gowerite is favorable because of the much higher boron immobilization capacity in weight percentage, comparing to usual boron-containing phases reported previously.

36 MATERIALS SCIENCE↗
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 OSTI2020

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Formation of zinc carbonate phases on dissolving calcite, aragonite, and vaterite in acidic aqueous solutions

Calcium carbonate (CaCO 3 ) minerals serve as a major sink to retain metal ions through mineral-water interfacial reactions in which the capacity and long-term stability of contaminant uptake are influenced by coupled dissolution and precipitation reactions of both primary and secondary carbonate minerals (i.e., mineral replacement). Notably, recent studies of calcite reactivity in acidic solutions containing high levels of metal ions demonstrated complex behavior under conditions of sustained disequilibrium. Here, we explored the reactivity of three CaCO 3 polymorphs (calcite, aragonite, and vaterite) with acidic Zn 2+ -containing aqueous solutions using a suite of imaging techniques including optical and scanning electron microscopies, synchrotron-based micro X-ray fluorescence, and transmission X-ray microscopy. Zn uptake by calcite occurred through a two-step process: the formation of a thin layer of the zinc precipitate on the substrate surface followed by the growth of fibrous and radiating hydrozincite particles from the layer. In contrast, Zn uptake by aragonite occurred by mineral replacement where the secondary Zn carbonate phase preserved the external morphology of the original crystal (i.e., a pseudomorph). The replacement of vaterite by hydrozincite occurred within the confined space beneath the porous shell of vaterite, signifying that the primary mechanism driving Zn carbonate precipitation was chemical exchange through the pores. When multiple CaCO 3 polymorphs coexisted, the replacement of aragonite and vaterite occurred preferentially over that of calcite. These results demonstrate the distinct morphological and mineralogical controls over the reactivities of calcium carbonate minerals with Zn 2+ under acidic conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗