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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Application of X-ray computed tomography for the characterization of graphite morphology in compact-graphite iron

Highlights: • X-ray Tomography is a powerful technique for graphite morphology characterization. • 3D characterization reveals true nodularity and spatial connectivity of graphite. • Tri-modal distribution of SG suggests a multi-stage graphite nucleation process. - Abstract: In the present study, high-energy X-ray tomography technique was used to characterize the graphite morphology of compact graphite iron. The size, shape, spatial connectivity and structure of different graphite morphologies were examined in 3D using computer-aided image analysis. In addition, nodularity analysis was performed in 3D and the results are compared to the traditional 2D technique. It is found that the variation of nodularity within 2 × 2 × 1.2 mm{sup 3} of the sample can be as high as 20 (%) from 2D analysis and the averaged nodularity (2D) is higher than that from 3D analysis. The size distribution of spheroidal graphite shows a tri-modal distribution, which suggests a multi-step nucleation process during solidification.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantifying Impacts of Biomass Pelletization on Fast Pyrolysis Using a Single-Particle Reactor, X-ray Computed Tomography, and Computational Modeling

The pore structure and density of lignocellulosic feedstocks dictate intraparticle transport phenomena and thereby play an important role in thermochemical conversion processes such as fast pyrolysis for biofuel and biochemical production. Variations in microstructure are inherent from different biomass species and can be introduced by preprocessing techniques such as cutting and pelletization. Morphological changes also occur during conversion and lead to vastly different pore structures and behavior during pyrolysis, which impact required conversion times and product distributions. The current work presents a comprehensive comparison of fast pyrolysis of neat and pelletized pine feedstocks, which includes single-particle experiments, modeling, and 3D imaging by X-ray computed tomography (XCT). The particle-scale model included anisotropic heat and mass transport in a shrinking particle with pyrolysis reactions based on the CRECK mechanism with boundary conditions informed by reactor-scale simulations of the single-particle reactor. The models were validated by measurements of the temperature and mass loss from single-particle pyrolysis experiments of neat and pelletized pine. Quantitative analysis of XCT geometries revealed that pyrolytic conversion yielded chars with increased porosity and permeability compared to the unpyrolyzed materials, along with decreased tortuosity and anisotropy. Pelletization of the pine feedstock resulted in a much denser, less permeable material, which converted slower and produced more residual char after pyrolysis compared to neat pine. The results from particle modeling revealed that accounting for the dynamic and anisotropic heat and mass transport caused by differences in pore structure is critical to achieving agreement with experimental results. Overall, this study highlights the dramatic differences in conversion behavior imparted by pelletization and the importance of capturing microstructural attributes in computational models to guide the design and optimization of pyrolysis processes for specific biomass feedstocks.

09 BIOMASS FUELS↗
DOE OSTI2018

A Combined Experimental and Computational Study on the Reaction Dynamics of the 1-Propynyl (CH 3 CC)–Acetylene (HCCH) System and the Formation of Methyldiacetylene (CH 3 CCCCH)

We investigated the 1-propynyl (CH 3 CC; X 2 A 1 ) plus acetylene/acetylene-d 2 (HCCH/DCCD; X 1 Σg + ) under single-collision conditions using the crossed molecular beams method. The reaction was found to produce C 5 H 4 plus atomic hydrogen (H) via an indirect reaction mechanism with a reaction energy of -123 ± 18 kJ mol –1 . Using the DCCD isotopologue, we confirmed that the hydrogen atom is lost from the acetylene reactant. Our computational analysis suggests the reaction proceeds by the barrierless addition of the 1-propynyl radical to acetylene, resulting in C 5 H 5 intermediate(s) that dissociate preferentially to methyldiacetylene (CH 3 CCCCH; X 1 A 1 ) via hydrogen atom emission with a computed reaction energy of -123 ± 4 kJ mol –1 . The barrierless nature of this reaction scheme suggests the 1-propynyl radical may be a key intermediate in hydrocarbon chain growth in cold molecular clouds like TMC-1, where methyl-substituted (poly)acetylenes are known to exist.

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

Enabling Efficient and Accurate Computational Studies of MOF Reactivity via QM/MM and QM/QM Methods

Electronic structure calculations can provide unique insight into metal–organic framework (MOF) reactivity and defect formation. Such calculations can be broadly categorized as utilizing either periodic or cluster models, each with their respective advantages and disadvantages. In the present study, we demonstrate how multiscale methods can leverage the advantages of both approaches to enable high levels of accuracy and computational efficiency in studies of MOF reactivity. Using defect formation in a zeolitic imidazolate framework (ZIF) as a prototypical example, we benchmark a quantum mechanics/molecular mechanics (QM/MM) scheme that enables highly efficient cluster-based calculations on MOFs. We demonstrate the importance of correctly accounting for the influence of both dative bond cleavage in the QM cluster and long-range mechanical coupling to the bulk to achieving accurate QM/MM studies of MOFs. We subsequently leverage these cluster models in a QM/QM scheme that goes beyond standard DFT to yield “gold-standard” correlated wave function results on MOFs at modest computational cost. Crucially, we find several cases in which the incorporation of these correlated corrections yields qualitatively important corrections over conventional DFT values.

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

Computational Screening of Supported Metal Oxide Nanoclusters for Methane Activation: Insights into Homolytic versus Heterolytic C–H Bond Dissociation

Since its discovery in zeolites, the [CuOCu] 2+ motif has played an important role in our understanding of selective methane activation over supported metal oxide nanoclusters. Although there are two known C-H bond dissoci-ation mechanisms, namely homolytic and heterolytic cleavage, most computational studies on optimizing metal oxide nanoclusters for improved methane activation reactivity have focused only on the homolytic mechanism. In this work, both mechanisms were examined for a set of 21 mixed metal oxide complexes of the form of [M 1 OM 2 ] 2+ (M 1 , M 2 = Mn, Fe, Co, Ni, Cu, Zn). Except for pure copper, heterolytic cleavage was found to be the dominant C-H bond activation pathway for all systems. Furthermore, mixed systems including [CuOMn] 2+ , [CuONi] 2+ , and [CuOZn] 2+ are predicted to possess similar methane activation activity as pure [CuOCu] 2+ . Furthermore, these results suggest that both homolytic and heterolytic mechanisms should be considered in computing methane activation energies on supported metal oxide nanoclusters.

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

Toward Computation-Guided Design of Tunable Organic-Inorganic CdS Quantum Dot Binary Superlattices

Combining the advantages of structural programmability in sequence-defined biomimetic molecules and the controllable packing geometry in nanoparticle superlattices, we demonstrate a self-assembled organic-inorganic superlattice whose structure can be altered with the slightest change in the sequence of the organic counterpart. Here, oleate-coated CdS quantum dots (QDs) form a square-packed superlattice with a 1:1 molar equivalence of a di-block amphiphilic peptoid (Nbrpe6Dig) in chloroform. In contrast, no apparent structure is observed in the organic solvent alone. Based on theoretical evidence, we show that the assembly is a binary superlattice where both the CdS QDs and the peptoids serve as building blocks and further predict a correlation between the superlattice structure and the peptoid sequence. The computationally guided prediction is validated by experiments where superlattice transformation is observed with modified peptoids. The mechanism identified in our work inspires new ways to control and tune organic-inorganic hybrid nanomaterial self-assembly.

Qi, Xin↗
DOE OSTI2016

Computational Approach for Epitaxial Polymorph Stabilization through Substrate Selection

With the ultimate goal of finding new polymorphs through targeted synthesis conditions and techniques, we outline a computational framework to select optimal substrates for epitaxial growth using first principle calculations of formation energies, elastic strain energy, and topological information. To demonstrate the approach, we study the stabilization of metastable VO2 compounds which provides a rich chemical and structural polymorph space. We find that common polymorph statistics, lattice matching, and energy above hull considerations recommends homostructural growth on TiO2 substrates, where the VO2 brookite phase would be preferentially grown on the a-c TiO2 brookite plane while the columbite and anatase structures favor the a-b plane on the respective TiO2 phases. Overall, we find that a model which incorporates a geometric unit cell area matching between the substrate and the target film as well as the resulting strain energy density of the film provide qualitative agreement with experimental observations for the heterostructural growth of known VO2 polymorphs: rutile, A and B phases. The minimal interfacial geometry matching and estimated strain energy criteria provide several suggestions for substrates and substrate-film orientations for the heterostructural growth of the hitherto hypothetical anatase, brookite, and columbite polymorphs. These criteria serve as a preliminary guidance for the experimental efforts stabilizing new materials and/or polymorphs through epitaxy. The current screening algorithm is being integrated within the Materials Project online framework and data and hence publicly available.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Computational design of CO-tolerant Pt 3 M anode electrocatalysts for proton-exchange membrane fuel cells

CO is a typical contaminant in hydrogen fuel produced via steam reforming. Development of a CO-tolerant Pt-based anode for the proton-exchange membrane fuel cells (PEMFC) is of great interest to avoid catalyst deactivation caused by strong CO adsorption on Pt. Pt–Ru, Pt–Mo, and Pt 3 Sn are three known Pt-based bimetallic CO-tolerant anode materials. The enhanced CO-tolerance of these alloys has been generally attributed to a bifunctional effect enabling oxidative CO removal at low applied potential and a ligand effect between the two metals which weakens CO bonding and reduces CO surface coverage. In this study, we use density functional theory to calculate surface adsorption states under reaction conditions for Pt-based alloys. Our calculations provide a molecular-level understanding of the enhanced CO-tolerance of Pt alloys induced by the second metal. We find that Mo and Sn dopants promote CO-tolerance by reducing CO surface coverage and enabling CO oxidative removal at low applied potential. In contrast, Ru is not able to promote CO electro-oxidation at the low applied potential of the operating conditions of a PEMFC anode. The CO-tolerance induced by Ru is attributed to the reduction of CO coverage on Pt sites. Based on these mechanisms, we carried out a computational screening of Pt 3 M electrocatalysts for CO-tolerant PEMFC anodes. A number of promising candidates have been identified for experimental examination.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Charging toward improved lithium-ion polymer electrolytes: exploiting synergistic experimental and computational approaches to facilitate materials design

Ion-conducting polymer electrolytes are attractive materials for all-solid-state lithium-ion batteries (LiBs) as a result of their potential to improve performance, mitigate safety concerns, and facilitate efficient device processing. Understanding how lithium-ionic transport is influenced by the macromolecular features, such as dielectric strength, solvation site connectivity, monomer segment relaxation timescales, segregation strength, and mesophase composition, is crucial for advancing ion-conducting materials. The integration of experimental and computational efforts can provide comprehensive links between chemical and structural characteristics (e.g., ion solvation, chain mobility, morphology) and macroscopic performance properties, accelerating the adoption of next-generation polymer electrolytes. Furthermore, this perspective highlights several synergistic experimental–computational approaches that have provided unique insights into local ion and polymer interactions, chain dynamics, and thermodynamics to promote the rational design of new ion-conducting polymers. Additionally, this work concludes with several suggested areas in which methodologies can be augmented, novel properties can be realized, and polymer electrolytes can be integrated to generate more robust LiB systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Recent advances in coupled cluster computations of open-shell atomic nuclei

In this contribution, we report on recent progress in coupled-cluster simulations of open-shell atomic nuclei using interactions consistently derived from chiral effective field theory. In particular, we compare different coupled-cluster approaches by computing binding energies and electric dipole polarizabilities in medium-mass calcium isotopes.

Marino, Francesco [Johannes Gutenberg Univ., Mainz↗
DOE OSTI2025

JACC.shared: Leveraging HPC Metaprogramming and Performance Portability for Computations That Use Shared Memory GPUs

In this work, we present JACC.shared, a new feature of Julia for ACCelerators (JACC), which is the performanceportable and metaprogramming model of the just-in-time and LLVM-based Julia language. This new feature allows JACC applications to leverage the high-performance computing (HPC) capabilities of high-bandwidth, on-chip GPU memory. Historically, exploiting high-bandwidth, shared-memory GPUs has not been a priority for high-level programming solutions. JACC.shared covers that gap for the first time, thereby providing a highlevel, portable, and easy-to-use solution for programmers to exploit this memory and supporting all current major accelerator architectures. Well-known HPC and AI workloads, such as multi/hyperspectral imaging and AI convolutions, have been used to evaluate JACC.shared on two exascale GPU architectures hosted by some of the most powerful US Department of Energy supercomputers: Perlmutter (NVIDIA A100) and Frontier (AMD MI250X). The performance evaluation reports speedup of up to 3.5× by adding only one line of code to the base codes, thus providing important accelerators in a simple, portable, and transparent way and elevating the programming productivity and performance-portability capabilities for Julia/JACC HPC, AI, and scientific applications.

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2025

Visualization of Noisy and Less Noisy Computational Basis States in Quantum Computing

Quantum computing technology holds substantial promise as a reliable computational paradigm. However, current noisy intermediate scale quantum (NISQ) systems, are significantly impacted by noise originating from hardware inconsistencies. This noise causes errors and lowers output fidelity. So we must find which basis states cause errors. However, there are two main challenges in analyzing noise corresponding to basis states. First, the noise distribution data is high dimensional in nature, thereby making its analysis challenging. Second, although functional box plots have been used in the state of the art research to understand such a high dimensional data, they suffer from clutter and occlusion issues because of overplotting. In this study, we introduce an innovative visualization pipeline to address the aforementioned challenges to provide a clear depiction of noisy and less-noisy basis states. Specifically, our proposed visualization pipeline comprises three stages namely, low dimensional embedding, clustering, and violin plot visualization, to reduce visual clutter and effectively analyze high-dimensional noise distribution data. Our analysis uses quantum machine learning (QML) circuits as case study for drawing a distinction between noisy and less noisy basis states.

Senapati, Priyabrata [Kent State University]↗