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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

TiO 2 Nanoparticles Enabling Photocatalytic Desulfurization for C–C Coupling Reaction Using Visible Light

We uncover a surface-mediated mechanism for visible-light-driven photocatalytic desulfurization of thiols on titanium dioxide (TiO 2 ) nanoparticles under LED illumination (>420 nm). Combined diffuse reflectance, infrared, and cryogenic electron paramagnetic resonance spectroscopy verify the formation of a surface thiol–Ti charge-transfer complex that introduces visible-light absorption into an otherwise UV-active reaction system. Selective photoexcitation of this surface complex drives interfacial charge transfer, directly activating the adsorbed thiol and inducing C–S bond scission without the band gap excitation of TiO 2 . The resulting carbon-centered radical intermediates are intercepted by styrene to form C–C coupling products. These results uncover a distinct surface-mediated photocatalytic mechanism in which charge-transfer complex formation governs both light absorption and chemical activation, providing a mechanistic framework for extending visible-light reactivity to wide-bandgap metal oxide photocatalysts.

addition of carbon radical to vinyl groups↗
DOE OSTI2019

Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2

Abstract Recent advances in scanning transmission electron microscopy (STEM) allow the real-time visualization of solid-state transformations in materials, including those induced by an electron beam and temperature, with atomic resolution. However, despite the ever-expanding capabilities for high-resolution data acquisition, the inferred information about kinetics and thermodynamics of the process, and single defect dynamics and interactions is minimal. This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data. To circumvent this problem, we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS 2 . The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds, which are then classified into different categories using unsupervised clustering methods. We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy, providing insight into point-defect dynamics and reactions. This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Galvanic Replacement-Driven Transformations of Atomically Intermixed Bimetallic Colloidal Nanocrystals: Effects of Compositional Stoichiometry and Structural Ordering

Galvanic replacement reactions dictated by deliberately designed nanoparticulate templates have emerged as a robust and versatile approach that controllably transforms solid monometallic nanocrystals into a diverse set of architecturally more sophisticated multimetallic hollow nanostructures. The galvanic atomic exchange at the nanoparticle/liquid interfaces induces a series of intriguing structure-transforming processes that interplay over multiple time and length scales. Using colloidal Au–Cu alloy and intermetallic nanoparticles as structurally and compositionally fine-tunable bimetallic sacrificial templates, we show that atomically intermixed bimetallic nanocrystals undergo galvanic replacement-driven structural transformations remarkably more complicated than those of their monometallic counterparts. Here, we interpret the versatile structure-transforming behaviors of the bimetallic nanocrystals in the context of a unified mechanistic picture that rigorously interprets the interplay of three key structure-evolutionary pathways: dealloying, Kirkendall diffusion, and Ostwald ripening. By deliberately tuning the compositional stoichiometry and atomic-level structural ordering of the Au–Cu bimetallic nanocrystals, we have been able to fine-maneuver the relative rates of dealloying and Kirkendall diffusion with respect to that of Ostwald ripening through which an entire family of architecturally distinct complex nanostructures are created in a selective and controllable manner upon galvanic replacement reactions. The insights gained from our systematic comparative studies form a central knowledge framework that allows us to fully understand how multiple classic effects and processes interplay within the confinement by a colloidal nanocrystal to synergistically guide the structural transformations of complex nanostructures at both the atomic and nanoparticulate levels.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Cascade random-quasi-phase-matched harmonic generation in polycrystalline ZnSe

Here, we experimentally and numerically investigated the cascade random-quasi-phase-matched harmonic generation in polycrystalline zinc selenide with tightly focused mid-infrared nanosecond laser pulses. We observed a sharp transition between two distinct intensity scaling regimes as a function of pump energy from sub I N to super I N , where I is the pump intensity and N is the harmonic order. To gain an understanding of the observations of this complex nonlinear process, we carried out simulations of the coupled three-wave mixing equations with random grain sizes and orientations. We found a grain size dependence of the effective phase-matching condition, suggesting that manipulation of grain size can be an effective pathway to phase-matching optimization. This approach may provide a framework for effectively enhancing high harmonic generation and other wave mixing effects in polycrystalline materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Hydrogen-Bonded Organic Frameworks: A Rising Class of Porous Molecular Materials

Hydrogen-bonded organic frameworks (HOFs) are a class of porous molecular materials that rely on the assembly of organic building blocks by means of hydrogen-bonding interactions to form two-dimensional (2D) and three-dimensional (3D) crystalline networks. The reversible nature of the hydrogen-bond formation endows HOFs with the attributes of solution processability and simple regeneration. High-quality single crystals of HOFs can be grown easily for unambiguous superstructure determination by single-crystal X-ray diffraction, which is crucial for the elucidation of superstructure–property relationships. During the past decade, considerable progress has been achieved in realizing stable HOFs with permanent porosities by focusing on the design of molecular building blocks in order to introduce rigidity, auxiliary [π···π] interactions, and interpenetration of their frameworks to sustain the extended networks. The applications of HOFs are far-reaching, spanning catalysis, energy, and biomedical products as well as the storage and separation of fine chemicals. In this paper, we, first of all, provide an overview of the chronological development of HOFs, starting from the seminal work by Marsh and Duchamp in 1969 on the crystal superstructure of the hydrogen-bonded networks of trimesic acid. We identify the development of novel hydrogen-bonding motifs such as diaminotriazine (DTA), the introduction of the concept of molecular tectonics, and the establishment of permanent porosity in HOFs as being some of the milestones, which incentivized the current burgeoning research endeavors on developing HOFs as multifunctional materials. This Account is focused primarily on surveying the strategies for constructing porous 3D HOFs based on organic building blocks with peripheral carboxyl groups. These strategies are presented in the following categories: (1) the polycatenation of 2D networks by trigonal building blocks to form global 3D frameworks, (2) the utilization of building blocks with 3D geometries—tetrahedral and trigonal prismatic—that are predisposed to form 3D networks, and (3) the docking by shape-fitting of geometrically labile building blocks. We emphasize how the molecular geometry of the building blocks plays an important role in modulating the superstructures of extended frameworks so as to address specific applications. Recognizing that the in silico design of HOFs is the ultimate goal of researchers in this field, we also discuss the recent advances in superstructure prediction that lead to the formation of porous supramolecular crystals and assess the complications in implementing computational methods for HOFs with complex superstructures. We hope this Account will inspire the development of new supramolecular designs and creative approaches to crystal engineering that aid and abet the assembly of multifunctional HOFs with customizable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2022

In Situ Synthesized Homochiral Spiroborate Ester Metal–Organic Framework with Mono–, Di–, and Trivalent Cations

Here we report here a one–step method for synthesizing multi–component and in–situ–formed homochiral spiroborate–ester–based metal–organic framework CPM–B1. This unique material successfully integrates COF fragment spiroborate ester within the MOF and simultaneously incorporate homochirality and helicity. In addition, CPM–B1 is a rare example of framework materials that results from the cooperative assembly of three charge–complementary cations: +1 (lithium), +2 (cobalt), and +3 (boron). The sophistication of the co–assembly is further highlighted by the three structural roles of lithium ions. This unique structure contributes to its multi–functional properties such as ionic conductivity and catalytic activity for oxygen reduction reaction (by CPM–B1 carbonized material) and provides a new path to develop MOF materials with complex secondary building units and multi–functional applications.

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

Assessment of critical flaw sizes and crack driving forces during additive manufacturing of metallic materials

Additive manufacturing (AM) of complex engineering components is often plagued by a high susceptibility to cracking, particularly in high-strength metallic materials. While alloy design efforts have made progress in mitigating solidification defects, there remains a need for mechanistic guidelines to predict susceptibility to solid-state cracking. To address this gap, driving forces for the growth of melt pool cracks are calculated across a wide range of alloys using an efficient computational framework. Calculations are coupled with rapid single track laser experiments to elucidate trends in cracking from laser melting. The analyses conducted here highlight the important role of material properties in susceptibility to cracking, notably fracture toughness and elastic modulus. An important finding is that residual stresses that are limited in magnitude to the yield stress of the material are likely insufficient to drive cracking during cooling. Furthermore, the implications of these results are discussed in the context of alloy design for AM and residual stress accumulation during AM.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Lead(II) coordination polymers based on rigid-flexible 3,5-bis-oxyacetate-benzoic acid: Structural transition driven by temperature control

Three Pb(II) complexes ([Pb{sub 3}(BOABA){sub 2}(H{sub 2}O)]·H{sub 2}O){sub n} (1), ([Pb{sub 4}(BOABA){sub 2}(µ{sub 4}-O)(H{sub 2}O){sub 2}]·H{sub 2}O){sub n} (2), and [Pb{sub 3}(BOABA){sub 2}(H{sub 2}O)]{sub n} (3) (H{sub 3}BOABA=3,5-bis-oxyacetate-benzoic acid) were obtained under the same reaction systems with different temperatures. Complexes 1 and 2 are two dimensional (2D) networks based on Pb-BOABA chains and Pb{sub 4}(µ{sub 4}-O)(COO){sub 6} SBUs, respectively. Complex 3 presents an interesting three dimensional (3D) framework, was obtained by increasing the reaction temperature. Structural transition of the crystallization products is largely dependent on the reaction temperature. Moreover, the fluorescence properties of complexes 1–3 have been investigated. - Graphical abstract: Three Pb(II) coordination polymers were obtained under the same reaction systems with different temperatures. Both of complexes 1 and 2 are 2D network. 3 presents a 3D framework based on Pb–O–C rods SBUs. The 2D to 3D structures transition between three complexes was achieved successfully by temperature control. - Highlights: • Three Pb(II) complexes were obtained under the same reaction systems with different temperatures. • Structural transition of the crystallization products is largely dependent on the reaction temperature. • The luminescence properties studies reveal that three complexes exhibit yellow fluorescence emission behavior, which might be good candidates for obtaining photoluminescent materials.

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

Control of solvent adsorption in porous liquids through solvent-solvent interactions

Type 3 Porous Liquids (PLs) are a new class of materials that offer transformative potential for gas capture and utilization. These PLs are comprised of a bulky solvent with a suspended empty nanoporous material that enables selective and high-capacity capture from dilute and complex gas steams. The relationship between the nanoporous material, the structure of the solvent molecules, and the time dependence of solvent adsorption into the nanoporous material governs long term adsorption properties. Herein, molecular dynamics (MD) calculations evaluated the solvent dynamics of nine neat solvents with a broad range of chemical identities and experimental density measurements probed the time-dependent adsorption of these solvents into the ZIF-8 metal-organic frameworks (MOFs) to form PL dispersions over three weeks. Two of the nine dispersion, using glyceryl triacetate and 2’-hydroxyacetopheneone as solvents, presented sufficiently low solvent infiltration—characterized by solvent adsorption less than 40% of the ZIF-8 pore volume—to be viable as PLs. The experimental solvent adsorption data for ZIF-8 dispersed in the four aromatic solvents (acetophenone, methylbenzoate, 2-isopropylphenol, and 2’-hydroxyacetophenone) was fit to a kinetic model to quantify rate of solvent adsorption into ZIF-8. The rates of adsorption of these four similar-sized solvents demonstrated that solvent adsorption is slowed and limited by solvent clustering dynamics that are, in turn, driven by intermolecular hydrogen bonding. Ultimately, experimental solvent sorption measurements indicated that glyceryl triacetate and 2’-hydroxyacetophenone with ZIF-8 formed PLs with the most stable porosity and the greatest potential for gas capture. In conclusion, the combined experimental-computational materials exploration framework used here reveals a novel relationship between molecular scale solvent dynamics and macroscale solvent adsorption kinetics critical for the discovery and rapid evaluation of Type 3 PL compositions.

Hydrogen bonding↗
DOE OSTI2025

NEML2: An efficient and modular multiphysics constitutive modeling library for hybrid computing environments

This paper presents NEML2, an open-source, high-performance library developed for constitutive material modeling, designed to support the flexible and modular development of models for complex material behavior. Building on the foundational structure of its predecessor, NEML, the NEML2 library introduces significant improvements, including enhanced vectorization, automatic differentiation, and seamless integration with PyTorch, facilitating the application of machine learning techniques in material simulations. NEML2 provides a C++ backend with Python bindings, enabling users to create custom material models that can be executed efficiently on both CPU and GPU platforms. The library also supports coupling with Multiphysics simulation frameworks like MOOSE, making it suitable for realistic simulations involving coupled physical processes. Rigorous quality assurance through unit and regression testing ensures the reliability of results, while the extensible, user-friendly design encourages collaboration and reproducibility across the scientific community. This paper provides an overview of NEML2’s architecture, core features, and applications, highlighting its impact on accelerating material qualification and advancing computational methods in materials science.

GPU↗
DOE OSTI2023

High-strength, lightweight nano-architected silica

Continuous nanolattices are an emerging class of mechanical metamaterials that are highly attractive due to their superior strength-to-weight ratios, which originate from their spatial architectures and nanoscale-sized elements possessing near-theoretical strength. Rational design of frameworks remains challenging below 50 nm because of limited methods to arrange small elements into complex architectures. Here, we fabricate silica frameworks with ~4- to 20-nm-thick elements using self-assembly and silica templating of DNA origami nanolattices and perform in situ micro-compression testing to examine the mechanical properties. We observe strong effects of lattice dimensions on yield strength (σ $\mathcal{y}$ ) and failure mode. Silica nanolattices are found to exhibit yield strengths higher than those of any known engineering materials with similar mass density. The robust coordination of the nanothin and strong silica elements leads to the combination of lightweight and high-strength framework materials offering an effective strategy for the fabrication of nanoarchitected materials with superior mechanical properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. Further, we show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Mesoscale Modeling Approach for Quantifying Microstructure-Aware Micromechanical Responses in Metal Hydrides

Metal hydrides can undergo significant volume changes upon hydrogen uptake and release, which induce a mechanical response that depends not only on the evolving hydrogen composition but also on the microstructure. We present a comprehensive mesoscale modeling framework based on microelasticity theory to quantify the micromechanical responses of metal hydrides, specifically focusing on a hydrogenating polycrystalline MgH 2x particle within a host material as a model micromechanical system. Utilizing digitally generated realistic microstructures and density-functional-theory-derived parameters, we analyzed highly nonuniform local stress profiles in the polycrystalline hydrides under the clamping force exerted by the host during hydrogenation. Our framework also allows us to predict the corresponding strain energy accumulation and mechanical hot spots formation in the hydrides, highlighting their roles in thermodynamic destabilization and mechanical failure, respectively. Through extensive parametric simulations, we further quantified the influence of interface type, crystallinity, grain size, loading ratio, and host stiffness, providing practical guidance for optimizing microstructural design and host material selection. This proposed approach is broadly applicable to micromechanical systems with complex microstructural features involving chemical reaction- and/or phase-transformation-induced deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Commercial Fault Detection and Diagnostics Tools: What They Offer, How They Differ, and What’s Still Needed

It is estimated that 5%–30% energy use in commercial buildings is wasted due to faults and errors in operations. Automated fault detection and diagnostics (AFDD) technologies can address this waste by identifying (detecting) deviations from normal or expected operation (faults), and resolving (diagnosing) the type of problem or its location, minimizing the need for complex manual analysis of operational data. Although currently underutilized, AFDD is a powerful approach to ensuring efficient building operations. AFDD offers the potential to greatly improve performance, and to do so cost effectively. There is currently a diverse landscape of AFDD technologies on the market, but no common framework exists to characterize such tools, or distinguish one offering from another. It is difficult to determine from vendor websites and marketing materials key technology features and capabilities, or the overall state of the technology. In this paper, we present an AFDD characterization framework, and findings from applying the framework to survey over a dozen technologies from today’s market. This paper outlines the current state of the market, as well as outstanding needs in the industry, derived from direct engagement of, and technical assistance provided to users of AFDD technology. A core set of interrelated informational, organizational, and technical needs and barriers that must be addressed to realize the full potential of AFDD at scale are identified. Lastly, based on information gathered through a survey and discussion with both vendors and users, several opportunities to further advance the technology are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

MOOSE–Workbench Integration and MOOSE Meshing Capability Enhancements to Facilitate Inputs and Outputs for Multiphysics Modeling

The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source framework that supports many of the US Department of Energy’s (DOE’s) Nuclear Energy Advanced Modeling and Simulation (NEAMS) technical areas (TA). These TAs develop and use NEAMS physics and coupling modules in multiple ways to enable the research and development of complex physics models. In addition to the MOOSE framework, the NEAMS Workbench user interface provides a common analysis environment with user-interaction accelerators that streamline the tasks of model creation, review, execution, and output inspection. In FY 2024, objectives were realized in the MOOSE framework application development support and user-oriented improvements. Application development improvements support both developers and users with an expanded Reactor Module and Mesh System, stateful material property support for mortar contact, and customizable convergence criteria. Additionally, new user-oriented features were implemented in the MOOSE framework language server, including autocompletion snippets, definition from source and find reference navigations, and syntax overrides. Lastly, improvements were made to the input interpreter necessary to support the MOOSE language server and the NEAMS Workbench so that they can interact with syntactically incomplete user inputs. These improvements and more were intended to address stakeholder feedback and improve developer and user ability to conduct advanced nuclear energy modeling and simulation in support of DOE and industry needs.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Luminescent and Magnetic Tb-MOF Flakes Deposited on Silicon

The synthesis of a terbium-based 2D metal–organic framework (MOF), of formula [Tb(MeCOO)(PhCOO) 2 ] (1), a crystalline material formed by neutral nanosheets held together by Van der Waals interactions, is presented. The material can be easily exfoliated by sonication and deposited onto different substrates. Uniform distributions of Tb-2D MOF flakes onto silicon were obtained by spin-coating. We report the luminescent and magnetic properties of the deposited flakes compared with those of the bulk. Complex 1 is luminescent in the visible and has a sizeable quantum yield of QY = 61% upon excitation at 280 nm. Photoluminescence measurements performed using a micro-Raman set up allowed us to characterize the luminescent spectra of individual flakes on silicon. Magnetization measurements of flakes-on-silicon with the applied magnetic field in-plane and out-of-plane display anisotropy. Ac susceptibility measurements show that 1 in bulk exhibits field-induced slow relaxation of the magnetization through two relaxation paths and the slowest one, with a relaxation time of τ lf ≈ 0.5 s, is assigned to a direct process mechanism. The reported exfoliation of lanthanide 2D-MOFs onto substrates is an attractive approach for the development of multifunctional materials and devices for different applications.

2D MOFs↗