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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mixed matrix membranes for post-combustion carbon capture: From materials design to membrane engineering

Mixed matrix materials (MMMs) containing nanofillers dispersed in continuous polymer matrices have emerged as an exciting and versatile platform to develop membranes with superior CO 2 /N 2 separation performance for post-combustion carbon capture. Both polymers and nanofillers can be vertically designed and engineered to combine the advantages of excellent processability (derived from polymers) and strong size-sieving ability or/and significant permanent porosity (originated from nanofillers). However, the MMMs face major challenges, such as interfacial incompatibility, particle agglomeration, and poor thin-film formability. This report provides a comprehensive yet critical review of various polymers and nanofillers (such as metal-organic frameworks, covalent organic frameworks, and two-dimensional materials) with promising CO 2 /N 2 separation properties. We exhaustively describe strategies to improve interfacial compatibility, such as in situ syntheses of polymers and nanofillers and functionalization of both components to improve adhesion. Moreover, we highlight various approaches to engineer the MMMs into thin-film composite (TFC) membranes. The review reveals the structure/property relationship in these MMMs and outlines the challenges and opportunities to realize their potential for practical applications.

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

Development of carbon nanosensor for vinpocetine at zinc oxide modified carbon matrix with anionic surfactant for clinical application

A nano-level sensing method was formulated for the trace-level detection and determination of vinpocetine (VIN) at a zinc oxide nanomaterial-based carbon matrix with immobilised anionic surfactant sodium dodecyl sulfate (ZnO-SDS/CPE) using cyclic voltammetry (CV) and square wave voltammetry (SWV) approaches. The catalytic properties of ZnO impact the electron rate of VIN activity, resulting in a threefold increase in the VIN peak response with the ZnO-SDS-modified sensor. Here, the effects of several factors, including scan rate, pH, accumulation length, modifier amount, and concentration, were investigated on the VIN peak current. The electro-oxidation of VIN by pH study involves one proton and one electron. The CV method also investigated the effect of scan rate. The charge transfer coefficient (α) is obtained to be 0.58, and the heterogeneous rate constant (k°) is estimated to be 4.46 s⁻¹. The concentration effect of VIN was studied using the SWV method. Specifically, the SWV methodology achieved the lowest detection limit compared to previously published approaches, with estimated values of the Limit of Detection (LOD) and Limit of Quantification (LOQ) being 2.2 × 10⁻⁸ M and 7.7 × 10⁻⁸ M, respectively. The trace level of VIN in tablet and urine samples was determined using the modified sensor. Moreover, the sensor demonstrates particular reproducibility and long-term stability, making it suitable for real-time applications.

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

AA2024/SiC metal matrix composites simultaneously improve ductility and cracking resistance during elevated temperature deformation

Here this study uses the stir-casting technique to combine a semi-solid AA2024 alloy directly with finely-sized β-SiC p embedded as a powder or with mechanically alloyed granules as a delivery agent. Liquid-state primary fabrication tends to form agglomerates of reinforcement particles, whereas rolling better distributes the composite constituents. Sub-micron reinforcements of low volume fractions do not significantly increase the hardness of the composite materials. Uniaxial tensile testing at elevated temperatures over a wide range of strain rates showed simultaneous increases in the ductility and crack resistance of AA2024 + SiC p granules embedded as a powder when compared to the non-reinforced control material at lower strain rates, with the same toughness as the control material. The maximum engineering strain of 252.7 ± 19.2% was observed in AA2024/SiCp at a strain rate of 10 -4 s -1 . This improvement in properties is attributed to grain refinement in the MMCs, leading to pinning events during the straining and ductility increases. The resultant impediments to grain growth and crack propagation allow the fine-sized reinforcements to control dynamic microstructural changes during fatigue. Cube {001}<100> is a dominant texture component in AA2024, whereas the Goss {011}<100> and S {123}<634> components mainly represent the texture of the discontinuously reinforced aluminum matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Artificial extracellular matrix scaffolds of mobile molecules enhance maturation of human stem cell-derived neurons

Human induced pluripotent stem cell (hiPSC) technologies offer a unique resource for modeling neurological diseases. However, iPSC models are fraught with technical limitations including abnormal aggregation and inefficient maturation of differentiated neurons. These problems are in part due to the absence of synergistic cues of the native extracellular matrix (ECM). We report on the use of three artificial ECMs based on peptide amphiphile (PA) supramolecular nanofibers. All nanofibers display the laminin-derived IKVAV signal on their surface but differ in the nature of their non-bioactive domains. Here, we find that nanofibers with greater intensity of internal supramolecular motion have enhanced bioactivity toward hiPSC-derived motor and cortical neurons. Proteomic, biochemical, and functional assays reveal that highly mobile PA scaffolds caused enhanced β1-integrin pathway activation, reduced aggregation, increased arborization, and matured electrophysiological activity of neurons. Our work highlights the importance of designing biomimetic ECMs to study the development, function, and dysfunction of human neurons.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Improving convergence of the Matrix Power Control Algorithm for random vibration testing

Herein, this paper describes modifications to the Matrix Power Control Algorithm (MPCA) to improve convergence for Random Vibration Control (RVC) testing. In particular, this paper presents Multiple-Input Multiple-Output (MIMO) implementations of MPCA in simulation and experiment. An Euler–Bernoulli beam model was simulated with applied base excitations and the Box Assembly with Removable Component (BARC) was used in experiment to validate results. The Bayesian optimization package Dragonfly was used to optimize control parameters. Additionally, a moving-average was employed and optimized to improve the measured response feedback for MPCA, reduce the number of averages needed to be taken between control updates, and further improve convergence. The key results of this paper show that the performance of MPCA can be improved by tuning the control parameters and by applying an optimized moving-average. Furthermore, it is demonstrated that convergence can be achieved within 12 drive-frames, which greatly enhances vibration control capability.

42 ENGINEERING↗
DOE OSTI2020

Pyrazine-Fused Porous Graphitic Framework-Based Mixed Matrix Membranes for Enhanced Gas Separations

Membrane-based separations can mitigate the capital- and energy-intensive challenges associated with traditional thermally driven processes. To further push the boundary of gas separations, mixed matrix membranes (MMMs) have been extensively exploited; however, identifying an optimal nanofiller to boost the separation performance of MMMs beyond Robeson permeability-selectivity upper bounds remains an ongoing challenge. In this study, a new class of MMMs based on pyrazine-fused crystalline porous graphitic frameworks (PGFs) is reported. At a loading of 6 wt % PGFs, the MMMs surpass the current H 2 /CH 4 Robeson upper bound, ideally suited for applications such as H 2 regeneration. In addition, the fabricated MMMs exhibit appealing CO 2 separation performance, closely approaching the current Robeson upper bounds for CO 2 separation. Compared with the pristine polymeric membranes, the PGF-based MMMs display a record-high enhancement of gas permeability over 120% while maintaining intrinsic gas selectivities. Highlighting the crucial role of the crystallinity of nanofillers, this study demonstrates a facile and effective approach in formulating high-performance MMMs, complementing state-of-the-art membrane formation processes. The design principles open the door to energy-efficient separations of gas mixtures with enhanced productivity compatible with the current membrane manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluating Use of Boron- and Hafnium-Modified Polysilazanes for Ceramic Matrix Minicomposites

In this study, the potential of polymer-derived Ceramic Matrix Composites (CMCs) is demonstrated by addition of thin ceramic coatings on Carbon Fiber (CF) bundles. Boron- and hafnium- modified polysilazane liquid precursors were synthesized and used to infiltrate the fiber bundles of CF to fabricate lab-scale Si(B)CN/CF and Si(Hf)CN/CF CMC mini-composites, respectively. The precursor-to-ceramic conversion process was achieved at 800°C with crosslinked precursor to ceramic yield of approx. 90% in Ar environment. The Si(B)CN/CF contained Si-N and B-N bonds, while Si-N and Hf-O-Si bonds were observed for Si(Hf)CN/CF sample with uniform and dense surfaces. Room-temperature tensile tests showed that the Si(Hf)CN/CF sample could reach a tensile strength of ~790 MPa and elastic modulus of 66.88 GPa among the composites. Oxidation study of the Si(Hf)CN/CF mini-composites showed higher stability compared to SiCN/CF and Si(B)CN/CF mini-composites up to 1500°C.

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

A comparison between the one- and two-step spin-orbit coupling approaches based on the ab initio Density Matrix Renormalization Group

The efficient and reliable treatment of both spin-orbit coupling (SOC) and electron correlation is essential for understanding f-element chemistry. Here, we analyze two approaches to the problem, the one-step approach where both effects are treated simultaneously, and the two-step state interaction approach. We report an implementation of the ab initio density matrix renormalization group(DMRG) with a one-step treatment of the SOC effect which can be compared to prior two-step treatments on an equal footing. Using a dysprosium octahedral complex and bridged dimer as benchmark systems, we identify characteristics of problems where the one-step approach is beneficial for obtaining the low-energy spectrum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository

Spent nuclear fuel repository simulations are currently not able to incorporate detailed fuel matrix degradation (FMD) process models due to their computational cost, especially when large numbers of waste packages breach. The current paper uses machine learning to develop artificial neural network and k-nearest neighbor regression surrogate models that approximate the detailed FMD process model while being computationally much faster to evaluate. Further, using fuel cask temperature, dose rate, and the environmental concentrations of CO 3 2- , O 2 , Fe 2+ , and H 2 as inputs, these surrogates show good agreement with the FMD process model predictions of the UO 2 degradation rate for conditions within the range of the training data. A demonstration in a full-scale shale repository reference case simulation shows that the incorporation of the surrogate models captures local and temporal environmental effects on fuel degradation rates while retaining good computational efficiency.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Random Matrix Theory in Cd isotopes

Random matrix theory (RMT) is used to provide a measure of the chaoticity (q) of calculated results for the spectra for various Cd isotopes. Here, the goal is to gain a better understanding of the internal dynamics in play; namely, whether it tracks with regular or irregular (chaotic) behavior as determined through an RMT analyses of calculated spectra. The basis-state configurations used to determine the spectra includes all positive, negative, natural (J π = 1 – , 2 + , ...), and unnatural parity configurations (J π = 0 – , 1 + , 2 – , ...), unless suppressed for comparative purposes. The results show that when intruder-state configurations are in play, regular behavior emerges, but when not in play, chaotic behavior seems to dominate the dynamics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A fast, matrix-based method to perform omnidirectional pressure integration

Abstract Experimentally-measured pressure fields play an important role in understanding many fluid dynamics problems. Unfortunately, pressure fields are difficult to measure directly with non-invasive, spatially resolved diagnostics, and calculations of pressure from velocity have proven sensitive to error in the data. Omnidirectional line integration methods are usually more accurate and robust to these effects as compared to implicit Poisson equations, but have seen slower uptake due to the higher computational and memory costs, particularly in 3D domains. This paper demonstrates how omnidirectional line integration approaches can be converted to a matrix inversion problem. This novel formulation uses an iterative approach so that the boundary conditions are updated each step, preserving the convergence behavior of omnidirectional schemes while also keeping the computational efficiency of Poisson solvers. This method is implemented in Matlab and also as a GPU-accelerated code in CUDA-C++. The behavior of the new method is demonstrated on 2D and 3D synthetic and experimental data. Three-dimensional grid sizes of up to 125 million grid points are tractable with this method, opening exciting opportunities to perform volumetric pressure field estimation from 3D PIV measurements.

42 ENGINEERING↗
DOE OSTI2021

K-Shell photoabsorption in Si 11+ : Relativistic contributions via Breit-Pauli R-matrix calculations

As a part of the investigation of the entire Si isonuclear sequence for x-ray spectral diagnostics, we focus on the Li-like Si 11+ ion, a stable component that features prominently in x-ray astrophysical spectra. In this work, we perform R-matrix calculations for the photoabsorption cross section of Si 11+ including relativistic contributions via the use of a Breit-Pauli Hamiltonian. Relativistic effects—predominantly spin-orbit splitting of resonances and mass-velocity and one-body Darwin global shifts of energies— are shown to be tractable and of importance for accuracy in x-ray spectral modeling data banks.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Matrix Metalloproteinases as Candidate Antigenic Determinants for Anti‐Tumor Autoantibodies in Human Ovarian Cancer: A Post Hoc Analysis

Circulating antibodies in patients with cancer can facilitate the identification of accessible epitopes on autoantigens expressed by tumors. To identify previously unrecognized protein targets in ovarian cancer, we computationally assessed a heptapeptide consensus motif (VPELGHE, flanked by two cysteine residues yielding a cyclic nonapeptide under oxidizing conditions) previously discovered via phage display-based epitope mapping of autoantibodies in patients. Eight proteins associated with ovarian cancer encompass amino acid sequences similar to the consensus motif and were, therefore, considered as candidate native autoantigens. Among these candidate targets, however, matrix metalloproteinase 14 (MMP14) demonstrates gene expression that is both high and negatively correlated with survival in ovarian cancer patient cohorts. MMP14 protein levels are also stable in tumor versus non-tumor tissues. Moreover, the corresponding heptapeptide mimic in MMP14 occurs within an α-helical secondary structural element observed in its catalytic domain. These findings demonstrate that a subset of patient-derived autoantibodies may interact with a previously unknown antigenic epitope found in MMP14 and other MMPs, thereby providing opportunities for the development of new targeted agents.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Time-dependent variational principle for mixed matrix product states in the thermodynamic limit

Here, we describe a time evolution algorithm for quantum spin chains whose Hamiltonians are composed of an infinite uniform left and right bulk part, and an arbitrary finite region in between. The left and right bulk parts are allowed to be different from each other. The algorithm is based on the time-dependent variational principle (TDVP) of matrix product states. It is inversion-free and very simple to adapt from an existing TDVP code for finite systems. The importance of working in the projective Hilbert space is highlighted. We study the quantum Ising model as a benchmark and an illustrative example. The spread of information after a local quench is studied in both the ballistic and the diffusive case. We also offer a derivation of TDVP directly from symplectic geometry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Constructing Hubbard models for the hydrogen chain using sliced-basis density matrix renormalization group

Sliced-basis DMRG(sb-DMRG) is used to simulate a chain of hydrogen atoms and to construct low-energy effective Hubbard-like models. The downfolding procedure first involves a change of basis to a set of atom-centered Wannier functions constructed from the natural orbitals of the exact DMRG one-particle density matrix. The Wannier function model is then reduced to a fewer-parameter Hubbard-like model, whose parameters are determined by minimizing the expectation value of the Wannier Hamiltonian in the ground state of the Hubbard Hamiltonian. This indirect variational procedure not only yields compact and simple models for the hydrogen chain, but also allows us to explore the importance of constraints in the effective Hamiltonian, such as the restricting the range of the single-particle hopping and two-particle interactions, and to assess the reliability of more conventional downfolding. The entanglement entropy for a model's ground state, cut in the middle, is an important property determining the ability of DMRG and tensor networks to simulate the model, and here we study its variation with the range of the interactions. Counterintuitively, we find that shorter ranged interactions often have larger entanglement.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exact matrix product state representation and convergence of a fully correlated electronic wavefunction in the infinite-basis limit

Here In this paper we present the exact representation of a fully correlated electronic wavefunction as the single-particle basis approaches completeness. It consists of a half-infinite chain of matrices of exponentially increasing size. The complete basis limit is illustrated numerically using the density-matrix renormalization-group method by computing the core-valence entanglement in the C 2 ground state in increasing subsets of cc-pVTZ and pVQZ bases until convergence is reached.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Abridged spectral matrix inversion: parametric fitting of X-ray fluorescence spectra following integrative data reduction

Recent improvements in both X-ray detectors and readout speeds have led to a substantial increase in the volume of X-ray fluorescence data being produced at synchrotron facilities. This in turn results in increased challenges associated with processing and fitting such data, both temporally and computationally. Herein an abridging approach is described that both reduces and partially integrates X-ray fluorescence (XRF) data sets to obtain a fivefold total improvement in processing time with negligible decrease in quality of fitting. The approach is demonstrated using linear least-squares matrix inversion on XRF data with strongly overlapping fluorescent peaks. This approach is applicable to any type of linear algebra based fitting algorithm to fit spectra containing overlapping signals wherein the spectra also contain unimportant (non-characteristic) regions which add little (or no) weight to fitted values, e.g. energy regions in XRF spectra that contain little or no peak information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Adapting In Situ Accelerators for Sparsity With Granular Matrix Reordering

Neural network (NN) inference is an essential part of modern systems and is found at the heart of numerous applications ranging from image recognition to natural language processing. In situ NN accelerators can efficiently perform NN inference using resistive crossbars, which makes them a promising solution to the data movement challenges faced by conventional architectures. Although such accelerators demonstrate significant potential for dense NNs, they often do not benefit from sparse NNs, which contain relatively few non-zero weights. Processing sparse NNs on in situ accelerators results in wasted energy to charge the entire crossbar where most elements are zeros. To address this limitation, this paper proposes Granular Matrix Reordering (GMR): a preprocessing technique that enables an energy-efficient computation of sparse NNs on in situ accelerators. GMR reorders the rows and columns of sparse weight matrices to maximize the crossbars' utilization and minimize the total number of crossbars needed to be charged. The reordering process does not rely on sparsity patterns and incurs no accuracy loss. Finally, GMR achieves an average of 28% and up to 34% reduction in energy consumption over seven pruned NNs across four different pruning methods and network architectures.

97 MATHEMATICS AND COMPUTING↗