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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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Distributed fiber optic sensors allow the measurement of structural parameters such as static/dynamic strain, temperature, pressure, and vibrations at thousands of locations along a single fiber cable. Deep neural network (DNN) algorithms were developed for rapid data processing speed and vibration event classification.
With support from the US Department of Energy Office of Science, we have developed and utilized extreme ultraviolet (XUV) spectroscopy and sum frequency generation vibrational spectroscopy as probes of charge, spin, and solvation structure and dynamics in molecules and at interfaces. This work is crucial to advancing fundamental understanding of the processes that control the efficiency and speed of energy conversion and information processing in molecules and at interfaces. Accordingly, it has important applications for developing next generation technologies for information storage and processing with increasing data storage density and processing rates as well as developing new methods for efficient energy conversion and storage.
Knowledge of the internal structure of an object or device under investigation proceeds from the basic idea of constructing its dynamic behavioral relations governed by a set of differential/algebraic equations that characterize its response. These equations can be partial differential equations leading to finite element or finite difference relations requiring a complex numerical solution on a super computer or ordinary differential equations requiring sophisticated numerical integration techniques to obtain the desired solution. Discrete dynamic systems evolving from digitized data acquisition are typically captured by sampled-data (continuous-to-discrete) representations characterized by a set of difference equations specifying the underlying system dynamics. In any case, with a mathematical description in hand, Grey-Box modeling techniques have evolved, concerned with the estimation of model parameters embedded in a prescribed set of equations (the system) governing its behavior, while capturing the underlying physical phenomenology of the problem at hand.
An investigation into the Box Assembly with Removable Component (BARC) structure is conducted by utilizing computational simulations and experimental structural testing in order to determine the complex dynamical responses instigated by the central cut of the system. Because the dynamics of the BARC system is complex, this study focuses primarily on analyzing the behavior of the box assembly (BA) system. The investigation explores the dynamics of the BA system by varying the central cut widths, ranging from a cut as wide as 0.5” cut to a 0.25” cut system, as well as a 0.1” cut and a system with no cut at all. Experimental testing is performed on each system including a free vibration test using an impact hammer to excite and identify the dominant frequencies of each structure. This testing is followed by pseudo-random vibration tests and swept sinusoidal excitation tests to determine the nonlinear aspects of these systems, such as the possible existence of nonlinear softening, hardening, and/or damping. The results show that nonlinear softening and nonlinear damping are present in each system. The no-cut system demonstrated the highest peak frequencies throughout all the tests, being the most rigid structure. The 0.25” cut system was shown to have the highest peak frequencies among all the cut systems in both the finite elemenet analysis (FEA) and impact testing. This trend did not continue, though, in the random and harmonic testing, possibly due to the added stiffness of the test setup with the slip table and stinger. The results show the importance of accurately measuring the central cut width and how possible geometric uncertainties change the overall dynamical behaviors of complex systems, such as natural characteristics, nonlinear responses, coupling of modes, and oscillating amplitudes.
Water-in-salt electrolytes (WiSE) are attractive for electrochemical energy storage applications owing to their wide electrochemical stability windows and inherent safety. The high concentration of salts is widely known to suppress the decomposition of water, which otherwise limits the stability of conventional aqueous salt-in-water electrolytes (SiWE). Nevertheless, the microstructural features of WiSE that lead to this enhanced stability have not yet been fully elucidated. In this work, ab initio molecular dynamics simulations were performed to study the energetic, structural, and spectroscopic properties of LiTFSI SiWE and WiSE solutions. A detailed mapping of water-water and water-anion hydrogen bonding and cation–anion electrostatic interactions is reported for both electrolytes. Structural features are presented in terms of both radial and spatial distribution functions. Analysis of IR and vibrational power spectra reveal key differences in the intermolecular interactions in SiWE and WiSE that arise from modified solvation shell structures. Finally, the results obtained herein reveal the most important structural and spectroscopic differences between the electrolytes in normal and superconcentrated concentrations.
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Understanding the negative thermal expansion (NTE) mechanism is of great importance. In this work, we consider the new NTE compound GdFe(CN) 6 (αv = -34.2×10 -6 K -1 ) as a case study to investigate the NTE mechanism from the perspective of the lattice vibrational dynamics. The atomic mean-square displacements suggest that the NTE of GdFe(CN) 6 comes from the strong tension effect induced by the transverse vibrations of the atomic –Fe–Ctriple bondN–Gd– linkages, with the largest contribution given by N atoms. Lattice dynamics calculations show that three low-frequency optical modes at about 50 cm -1 show the largest negative Grüneisen parameters thus providing the largest contribution to the NTE. In conclusion, the existence of these unusual low-frequency vibrational modes can be ascribed to the presence of GdN 6 trigonal prisms in the framework structure of GdFe(CN) 6 .
Iron-based Prussian blue analogues (PBAs) have gained attention as low-cost, relatively higher-potential cathodes for sodium-ion batteries, due to their open 3D-framework structures. However, understanding the local structural changes is critical to unveiling the intercalation pathways and degradation mechanisms. We employ here operando Raman spectroscopy to probe the changes in the cyanide vibrational modes during cycling and degradation after cycling, which are not adequately resolved by X-ray diffraction due to the low structure factor and limited X-ray sensitivity of the cyanide groups. Vibrational spectroscopy has thus proven essential for deciphering these complex materials. Additionally, we implement pre-sodiation strategies to assess the impact of sodium inventory loss by pairing PBAs with a hard carbon anode in a full-cell configuration. Operando galvanostatic electrochemical impedance spectroscopy (EIS) and ex situ X-ray photoelectron spectroscopy (XPS) further elucidate the interface evolution and the role of water molecules in forming the cathode-electrolyte interphase (CEI) and solid-electrolyte interphase (SEI). The insights gained advance the understanding of PBAs and enhance their practical viability.
Solid-state carbon materials, such as graphite and graphene, are at the forefront of materials research because of their unique electronic, vibrational, and mechanical properties, leading to a broad range of potential and realized applications. One key application is their role as basic structural units of carbon fiber (CF), a lightweight alternative to steel. In CF, a delicate relationship exists between ultimate material strength and the atomic-scale density of defects contained at the inter- and intra-subunit levels. Computational studies provide insight into the stability of various types of defects that can form in these systems and connect with experimental observables such as bandgap and spectroscopic measurements. Therefore, the literature contains many computational studies that focus on changes induced by defects, including vacancies and Dienes transformations (Stone-Wales and Thrower defects). However, wide-ranging methods and cell sizes have been used, and property-specific information is often lacking. Furthermore, this review summarizes key literature findings, paying particular attention to changes in the electronic, vibrational, and mechanical properties induced by defects in graphitic materials relevant to CF.
The pure rotational spectrum of pyrimidine (m-C 4 H 4 N 2 ), the meta-substituted dinitrogen analog of benzene, has been studied in the millimeter-wave region from 235 GHz to 360 GHz. The rotational spectrum of the ground vibrational state has been assigned and fit to yield accurate rotational and distortion constants. Over 1700 distinct transitions were identified for the normal isotopologue in its ground vibrational state and least-squares fit to a partial sextic S-reduced Hamiltonian. Transitions for all four singly substituted 13 C and 15 N isotopologues were observed at natural abundance and were likewise fit. Deuterium-enriched samples of pyrimidine were synthesized, giving access to all eleven possible deuterium-substituted isotopologues, ten of which were previously unreported. Experimental values of rotational constants and computed values of vibration–rotation interaction constants and electron-mass corrections were used to determine semi-experimental equilibrium structures (r e SE ) of pyrimidine. The r e SE structure obtained using coupled-cluster with single, double, and perturbative triple excitations [CCSD(T)] corrections shows exceptional agreement with the re structure computed at the CCSD(T)/cc-pCV5Z level (≤0.0002 Å in bond distance and ≤0.03° in bond angle). Of the various computational methods examined, CCSD(T)/cc-pCV5Z is the only method for which the computed value of each geometric parameter lies within the statistical experimental uncertainty (2σ) of the corresponding semi-experimental coordinate. The exceptionally high accuracy and precision of the structure determination is a consequence of the large number of isotopologues measured, the precision and extent of the experimental frequency measurements, and the sophisticated theoretical treatment of the effects of vibration–rotation coupling and electron mass. Taken together, these demanding experimental and computational studies establish the capabilities of modern structural analysis for a prototypical monocyclic aromatic compound.
Pressure-induced phase transitions from the zircon structure-type (I4 1 /amd) to the scheelite structure type (I4 1 /a) are known for many ternary oxides systems (ABO 4 ). In this work, we present the first high-pressure study on synthetic stetindite (CeSiO 4 ) by a combination of in situ high-pressure synchrotron powder X-ray diffraction up to 36 GPa, implemented with and without dual sided laser heating, and in situ high-pressure Raman spectroscopy up to 43 GPa. Two phase transitions were identified: zircon to a high-pressure low-symmetry (HPLS) phase at 15 GPa and then to a scheelite at 18 GPa. The latter from HPLS scheelite phase was found irreversible; i.e., scheelite is fully quenchable at ambient conditions, as in other zircon-type phases. The bulk moduli (K 0 ) of stetindite, HPLS, and high-pressure scheelite phases were determined, respectively, as 171(5), 105(4), and 221(40) GPa by fitting to a second-order Birch-Murnaghan equation of state. The pressure derivatives of vibrational modes and Gru''neisen parameters of the zircon-structured polymorph are similar to those of other orthosilicate minerals. In conclusion, due to the larger ionic radii of Ce 4+ , with respect to Zr 4+ , stetindite was found to possess a softer bulk modulus and undergo the phase transitions at a lower pressure than zircon (ZrSiO 4 ), such observations are consistent with what were found in coffinite (USiO 4 ).
[FeFe] hydrogenases are the most active H 2 converting catalysts in nature, but their extreme oxygen sensitivity limits their use in technological applications. The [FeFe] hydrogenases from sulfate reducing bacteria can be purified in an O 2 -stable state called H inact . To date, the structure and mechanism of formation of H inact remain unknown. Our 1.65 Å crystal structure of this state reveals a sulfur ligand bound to the open coordination site. Furthermore, in-depth spectroscopic characterization by X-ray absorption spectroscopy (XAS), nuclear resonance vibrational spectroscopy (NRVS), resonance Raman (RR) spectroscopy and infrared (IR) spectroscopy, together with hybrid quantum mechanical and molecular mechanical (QM/MM) calculations, provide detailed chemical insight into the Hinact state and its mechanism of formation. This may facilitate the design of O 2 -stable hydrogenases and molecular catalysts.
The aqueous proton is a common and long-studied species in chemistry, yet there is currently intense interest devoted to understanding its hydration structure and transport dynamics. Typically described in terms of two limiting structures observed in gas-phase clusters, the Zundel H 5 O 2 + and Eigen H 9 O 4 + ions, the aqueous structure is less clear due to the heterogeneity of hydrogen bonding environments and room-temperature structural fluctuations in water. The linear infrared (IR) spectrum, which reports on structural configurations, is challenging to interpret because it appears as a continuum of absorption, and the underlying vibrational modes are strongly anharmonically coupled to each other. Recent two-dimensional IR (2D IR) experiments presented strong evidence for asymmetric Zundel-like motifs in solution, but true structure–spectrum correlations are missing and complicated by the anharmonicity of the system. In this study, we employ high-level vibrational self-consistent field/virtual state configuration interaction calculations to demonstrate that the 2D IR spectrum reports on a broad distribution of geometric configurations of the aqueous proton. We find that the diagonal 2D IR spectrum around 1200 cm –1 is dominated by the proton stretch vibrations of Zundel-like and intermediate geometries, broadened by the heterogeneity of aqueous configurations. There is a wide distribution of multidimensional potential shapes for the proton stretching vibration with varying degrees of potential asymmetry and confinement. In conclusion, we find specific cross peak patterns due to aqueous Zundel-like species. These studies provide clarity on highly debated spectral assignments and stringent spectroscopic benchmarks for future simulations.
A reference lattice, away from which elastic distortions induced by the spin texturing of two-dimensional (2D) magnets take hold, is motivated from a picture of pairwise Biot-Savart interactions among identical solenoids that either elongate or compress a (“zero-current”) spring lattice. Applied to a paradigmatic CrSiTe 3 monolayer, the reference is given by the average between the atomic positions of ferromagnetic (FM) and Néel antiferromagnetic (AFM) lattices; such an atomic disposition permits understanding structural distortions and elastic energies due to magnetism readily. Furthermore, the anisotropic speed of sound in the magnetic ground state explains an observed anisotropy of vibrational frequencies on similar magnets. Elastic stiffness constants are reported, too. Magnetic energies in four Ising structural configurations were calculated and the strain needed for those 2D magnets to undergo an AFM to FM quantum phase transition was determined as well.
Aluminate salts precipitated from caustic alkaline solutions exhibit a correlation between the anionic speciation and the identity of the alkali cation in the precipitate, with the aluminate ions occurring either in monomeric (Al(OH) 4 – ) or dimeric (Al 2 O(OH) 6 2– ) forms. The origin of this correlation is poorly understood as are the roles that oligomeric aluminate species play in determining the solution structure, prenucleation clusters, and precipitation pathways. Characterization of aluminate solution speciation with vibrational spectroscopy results in spectra that are difficult to interpret because the ions access a diverse and dynamic configurational space. To investigate the Al(OH) 4 – and Al 2 O(OH) 6 2– anions within a well-defined crystal lattice, inelastic neutron scattering (INS) and Raman spectroscopic data were collected and simulated by density functional theory for K 2 [Al 2 O(OH) 6 ], Rb 2 [Al 2 O(OH) 6 ], and Cs[Al(OH) 4 ]·2H 2 O. These structures capture archetypal solution aluminate species: the first two salts contain dimeric Al 2 O(OH) 6 2– anions, while the third contains the monomeric Al(OH) 4 – anion. Here, comparisons were made to the INS and Raman spectra of sodium aluminate solutions frozen in a glassy state. In contrast to solution systems, the crystal lattice of the salts results in well-defined vibrations and associated resolved bands in the INS spectra. The use of a theory-guided analysis of the INS of this solid alkaline aluminate series revealed that differences were related to the nature of the hydrogen-bonding network and showed that INS is a sensitive probe of the degree of completeness and strength of the bond network in hydrogen-bonded materials. Results suggest that the ionic size may explain cation-specific differences in crystallization pathways in alkaline aluminate salts.
In materials with strong electron-phonon (e-ph) interactions, charge carriers can distort the surrounding lattice and become trapped, forming self-localized (small) polarons. We recently developed an ab initio approach based on canonical transformations to efficiently compute the formation and energetics of small polarons A different approach based on a Landau-Pekar energy functional has been proposed in the recent literature. Here in this work, we analyze and compare these two methods in detail. We show that the small polaron energy is identical in the two formalisms when using the same polaron wave function. We also show that our canonical transformation formalism can predict polaron band structures and can properly treat zero- and finite-temperature lattice vibration effects, although at present using a fixed polaron wave function. Conversely, the energy functional approach can compute the polaron wave function, but as we show here, it neglects lattice vibrations and cannot address polaron self-localization and thermal band narrowing. Taken together, this work relates two different methods developed recently to study polarons from first-principles, highlighting their merits and shortcomings and discussing them both in a unified formalism.
Metals are excellent conductors for phonon transportation such as vibration, sound, and heat. Generally, metal sound insulators require multimaterial structure or defects and unimetal sound insulators are challenging. Therefore, a design of a defect-free sound insulator made by single alloys with multiple friction stir processes (FSPs) is proposed. Periodic friction stir processing can induce superlattice-like local mechanical properties’ modifications. By experimental acoustic characterization, it is observed that FSP can introduce clear acoustic–elastic property contrast on an aluminum plate by the presence of stir zone and heat-affected zones. In numerical simulations, the signature FSP-induced property profile is periodically and parallelly arranged on a long aluminum plate. The transmission gap frequencies are present on the frequency spectrum with the sound propagation direction perpendicular to the FSP paths. Disorder offsets on FSP periodicity are further introduced. Anderson localization is found on a resonance frequency, which provides −11 dB sound reduction by an exponential decay. Due to the finite design length, the slight disorder can also enhance sound insulation in the periodic transmission gap frequency. In conclusion, with analysis and comparison with different configurations, the best performance in the models can achieve −30 dB sound insulation in the 350 mm-long aluminum alloy plate with 14 parallel FSPs.
Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.