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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Electronic structure of reduced CeO 2 (111) surfaces interacting with hydrogen as revealed through electron energy loss spectroscopy in comparison with theoretical investigations

Based on both, ab-initio cluster calculations as well as periodic density functional based loss function calculations, we have assigned the origins of the valence electron excitation regime in electron energy loss spectra of well-ordered ceria films in (111) orientation at various states of reduction as well as after exposing the reduced films to hydrogen from the gas phase (Li et al, Angew. Chem. Int. Ed., 58 (2019) 14686–14693). The explicit calculation of intensity distributions using the dipole approximation allow us to draw conclusions about the nature of oxygen vacancies, which occur upon reduction and how those interact with hydrogen to form hydride species. Herein, we also reports a brief discussion of vibrational excitations of hydrogen loaded ceria and corroborates previous interpretations, based on inelastic neutron scattering.

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

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗
DOE OSTI2022

A low-rank power iteration scheme for neutron transport criticality problems

Computing effective eigenvalues for neutron transport often requires a fine numerical resolution. Here, the main challenge of such computations is the high memory effort of classical solvers, which limits the accuracy of chosen discretizations. In this work, we derive a method for the computation of effective eigenvalues when the underlying solution has a low-rank structure. This is accomplished by utilizing dynamical low-rank approximation (DLRA), which is an efficient strategy to derive time evolution equations for low-rank solution representations. The main idea is to interpret the iterates of the classical inverse power iteration as pseudo-time steps and apply the DLRA concepts in this framework. In our numerical experiment, we demonstrate that our method significantly reduces memory requirements while achieving the desired accuracy. Analytic investigations show that the proposed iteration scheme inherits the convergence speed of the inverse power iteration, at least for a simplified setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Atomic-scale characterization of (electro-)catalysts and battery materials by atom probe tomography

To optimise the performance of heterogeneous (electro-)catalysts and battery electrodes, it is essential to establish the structure-property relationships. This can only be achieved by obtaining a full three-dimensional (3D) characterisation of atomic-scale structure and chemistry of these advanced materials. Atom probe tomography (APT) can provide unique insights into 3D elemental distribution, including low atomic mass elements, with sub-nanometer spatial resolution. Despite its superior chemical analytical capability and spatial resolution, APT has limited applications for sustainable energy materials due to technical and analytical challenges. The recent developments in experimental setups, sample preparation methods and data analysis algorithms have opened the door to the possibility of analysing heterogeneous (electro-)catalysts and battery electrodes. This perspective provides an overview of these developments, exemplified by recent achievements of analyzing model heterogeneous model (electro-)catalysts, nanoparticles, zeolites, MOFs and Li-ion battery electrodes, followed by a discussion of limitations, opportunities, and potentials of APT for future scientific exploration.

atom probe tomography↗
DOE OSTI2023

Batch Scale Production of 3D Printed Extraction Sorbents Using a Low-Cost Modification to a Desktop Printer

This study reports a simple modification to a commercial resin 3D printer that significantly reduces the amount of prepolymer material needed for the production of extraction sorbents. Here, the modified printing platform is demonstrated in the printing of two imidazolium-based ionic liquid (IL) monomers. Two geometries resembling a blade-type polymeric ionic liquid (PIL) sorbent used in thin-film microextraction and a fiber-type sorbent used in solid-phase microextraction (SPME) were printed. The SPME PIL sorbents were used to extract 10 organic contaminants, including plasticizers, antimicrobial agents, UV filters, and pesticides, from water followed by high-performance liquid chromatographic (HPLC) analysis. To compare the extraction performance of the SPME sorbents, seven fibers printed with the same prepolymer composition from the same printing batch as well as different batches were evaluated. The results revealed highly reproducible extraction efficiencies for all tested sorbents with no statistical difference in their extraction performance. Method validation showed acceptable linearity ($R$ 2 > 0.92) for all analytes with limits of detection and limits of quantification ranging from 0.13 to 45 μg L –1 and 0.43 to 150 μg L –1 , respectively.

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

Comprehensive Assessment of the Accuracy of the Ideal Adsorbed Solution Theory for Predicting Binary Adsorption of Gas Mixtures in Porous Materials

Quantifying the adsorption of chemical mixtures in porous adsorbents is critical to developing these materials for useful separation applications. The ideal adsorbed solution theory (IAST) is the most widely applied mixing theory for predicting mixture adsorption using single-component adsorption data, but a perceived lack of experimental data has limited previous efforts to explore the accuracy of IAST in a systematic way. In this paper, we take advantage of a large collection of binary experimental data for gas adsorption that became available recently to tackle this issue. Specifically, we identify more than 400 examples in which binary adsorption data and single-component data are available in the same publication and apply IAST to all these examples. This analysis includes experimental data from 63 gas mixtures of 37 different molecular species and 174 different adsorbents. In addition to being the most systematic evaluation to date of the accuracy of IAST for gas adsorption, these data will be valuable for future efforts to test or develop mixing theories that improve upon IAST.

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

Homoleptic Perchlorophenyl “Ate” Complexes of Thorium(IV) and Uranium(IV)

The reaction of AnCl 4 (DME) n (An = Th, n = 2; U, n = 0) with 5 equiv of LiC 6 Cl 5 in Et 2 O resulted in the formation of homoleptic actinide-aryl “ate” complexes [Li(DME) 2 (Et 2 O)] 2 [Li(DME) 2 ][Th(C 6 Cl 5 ) 5 ] 3 ([Li][1]) and [Li(Et 2 O) 4 ][U(C 6 Cl 5 ) 5 ] ([Li][2]). Similarly, the reaction of AnCl 4 (DME)n (An = Th, n = 2; U, n = 0) with 3 equiv of LiC 6 Cl 5 in Et 2 O resulted in the formation of heteroleptic actinide-aryl “ate” complexes [Li(DME) 2 (Et 2 O)][Li(Et 2 O) 2 ][ThCl 3 (C 6 Cl 5 ) 3 ] ([Li][3]) and [Li(Et 2 O) 3 ][UCl 2 (C 6 Cl 5 ) 3 ] ([Li][4]). Density functional calculations show that the An–C ipso σ-bonds are considerably more covalent for the uranium complexes vs the thorium analogues, in line with past results. Additionally, good agreement between experiment and calculations is obtained for the 13 C ipso NMR chemical shifts in [Li][1] and [Li][3]. Here, the calculations demonstrate a deshielding by ca. 29 ppm from spin–orbit coupling effects originating at Th, which is a direct consequence of 5f orbital participation in the Th–C bonds.

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

Electron-Donating para -Substituent (X) Enhances the Water Oxidation Activity of the Catalyst Ru(4'-X-terpyridine)(phenanthroline-SO 3 ) +

Recently, our group has developed a Ru-based water oxidation catalyst (WOC) with pendant sulfonate (1, Ru(4'-X-terpyridine)(phenanthroline-SO 3 )OTf (X = H, 1a) that shows high activity under both sacrificial oxidant (CAN, Ce(NH 4 ) 2 (NO 3 ) 6 , Ce IV ) and electrocatalytic conditions, in both acidic and neutral media. Here, we demonstrate that the functionalization of the 4'-X-terpyridine ligand with an electron-donating substituent X = OEt (1b) makes potentials of Ru II /Ru III redox catalysis more negative, whereas when X = NO 2 (1c) and CF 3 (1d), potentials are more positive. For 1b, full conversion of the sacrificial oxidant Ce IV occurred in 0.4 h (7 h for 1a), with an initial rate of 2.07 μmol O 2 s –1 and a turnover frequency of 7.6 s –1 , which is 30-fold faster than that for 1a at [cat] 0 = 20 μM. Under electrocatalytic conditions, water oxidation by 1b is three times faster than that by the parent catalyst 1a at close to the same potential. Extensive computations have identified differences in the initial PCET steps of the water oxidation by catalysts 1a, 1b, and 1d, and demonstrated the increased probability of the O 2 formation via the oxide relay pathway in the order 1b< 1a < 1d.

14 SOLAR ENERGY↗
DOE OSTI2025

U 4+/5+/6+ in a Conserved Pseudotetrahedral Imidophosphorane Coordination Sphere

While several ligand systems support uranium across a range of oxidation states, spanning more than two oxidation states in a conserved coordination geometry is uncommon among structurally authenticated complexes. Imidophosphorane ligands significantly stabilize high-valent lanthanide and actinide complexes. Here, we report a series of homoleptic uranium imidophosphorane complexes, spanning the +4, +5 and +6 oxidation states in a four-coordinate pseudotetrahedral ligand field. The +6 oxidation state is accessible using a mild ferrocenium oxidant, yielding a rare example of U6+ in a pseudotetrahedral coordination environment. As the formal oxidation state increases, the U–N distances gradually contract, consistent with the Shannon ionic radii of U 4+/5+/6+ . Compared to reported complexes, the short U–N distances observed in the U 6+ complex are more comparable to dianionic imido ligands than monoanionic amido ligands.

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

A Geometric Measure Theory Approach to Identify Complex Structural Features on Soft Matter Surfaces

The structural features that protrude above or below a soft matter interface are well-known to be related to interfacially mediated chemical reactivity and transport processes. It is a challenge to develop a robust algorithm for identifying these organized surface structures, as the morphology can be highly varied and they may exist on top of an interface containing significant interfacial roughness. A new algorithm that employs concepts from geometric measure theory, algebraic topology, and optimization is developed to identify candidate structures at a soft matter surface, and then, using a probabilistic approach, to rank their likelihood of being a complex structural feature. The algorithm is tested for a surfactant laden water/oil interface, where it is robust to identifying protrusions responsible for water transport against a set identified by visual inspection. To our knowledge, this is the first example of applying geometric measure theory to analyze the properties of a chemical/materials science system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

From Latent Dynamics to Meaningful Representations

While representation learning has been central to the rise of machine learning and artificial intelligence, a key problem remains in making the learnt representations meaningful. For this the typical approach is to regularize the learned representation through prior probability distributions. However such priors are usually unavailable or are ad hoc. To deal with this, recent efforts have shifted towards leveraging the insights from physical principles to guide the learning process. In this spirit, we propose a purely dynamics-constrained representation learning framework. Instead of relying on predefined probabilities, we restrict the latent representation to follow overdamped Langevin dynamics with a learnable transition density — a prior driven by statistical mechanics. We show this is a more natural constraint for representation learning in stochastic dynamical systems, with the crucial ability to uniquely identify the ground truth representation. We validate our framework for different systems including a real-world fluorescent DNA movie dataset. Here, we show that our algorithm can uniquely identify orthogonal, isometric and meaningful latent representations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Learning Latent Representations to Bridge Coarse-Grained and Atomistic Resolutions in Polymer Simulations

We present a machine-learning-based framework for learning reduced-order representations of polymer chain conformations across coarse-grained (CG) and united-atom (UA) fidelities. By employing linear singular value decomposition and nonlinear autoencoders, we compress high-dimensional polymer configurations into latent spaces with minimal loss of structural accuracy. Crucially, we demonstrate a near-perfect linear mapping between CG and UA latent spaces, enabling an efficient super-resolution back-mapping procedure that reconstructs high-fidelity UA configurations from CG simulations. While minor structural inaccuracies occur, they are effectively corrected through a brief molecular dynamics relaxation, forming a practical hybrid machine learning−physics scheme. This approach establishes the key structural prerequisites for accelerated polymer dynamics simulations: a compact and accurate latent encoding of polymer chain conformations and a validated multi-fidelity mapping that permits reconstruction of UA structures from CG configurations. The extension of this framework to explicit time evolution within the latent space, enabling dynamics to be propagated at CG fidelity and decoded to UA resolution only when required, represents a natural and well-motivated direction for future work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

SCF Framework, HF Stability, and RPA Correlation for Jordan–Wigner-Transformed Spin Hamiltonians on Arbitrary Coupling Topologies

Mapping spins to fermions via the Jordan–Wigner (JW) transformation can render mean-field (Hartree–Fock, HF) descriptions effective for strongly correlated spin systems. As established in recent work, the application of such approaches is not limited by the nonlocal structure of JW strings or by site ordering because string operators can be absorbed into Thouless rotations of a Slater determinant, and the variational optimization of a unitary Lie-algebraic similarity transformation removes any ordering dependence. Leveraging these ideas, we develop a self-consistent field (SCF) scheme that expresses the mean-field energy as a functional of the single-particle density matrix, providing an alternative to gradient-based optimization of Thouless parameters. We derive the analytical orbital Hessian to diagnose HF stability and compute the ground-state correlation energy through the random-phase approximation (RPA). Benchmark results for the XXZ and J 1 –J 2 model on one- and two-dimensional lattices demonstrate that RPA significantly improves mean-field accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗