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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Structural, Electrical, and Electromechanical Properties of Inverse Hybrid Perovskites from First-Principles: The Case of (CH 3 NH 3 ) 3 OI

Inverse-hybrid perovskites (IHPs) with large polarization have recently been predicted from first-principles computations. Here, we use one representative from the IHP class of materials, (CH 3 NH 3 ) 3 OI (MA3OI), to propose a route to the first-principles prediction of structural and electrical properties, such as polarization, polarization reversibility, and the associated coercive field for hybrid organic–inorganic perovskites. The route relies on the construction of the polarization reversal path that models experimental measurements. Such a path was found to play an important role in the ground-state search as well as in the identification of competing structural variations. The latter is believed to be the origin of the structural disorder that is characteristic of hybrid organic–inorganic perovskites. The application of such an approach to MA3OI leads to the prediction of several structural variations that are expected to result in a structurally disordered phase above 766 K and of the polar ground state with a polarization of 25.3 μC/cm 2 that is reversible with the application of an electric field. The upper estimate for the coercive field associated with homogeneous polarization reversal is 6.9 GV/m. The piezoelectric constants of MA3OI are predicted to be an order of magnitude smaller in comparison with a prototypical inorganic ferroelectric PbTiO 3 ; however, the low symmetry of the MA3OI structure yields finite values for all components of the piezoelectric tensor. The polarization in MA3OI is tunable by the epitaxial strain (11.5% change under 5% epitaxial strain), although less so as compared with PbTiO 3 .

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

Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces

Enantiospecific heterogeneous catalysis utilizes chiral surfaces to resolve enantiomers via structure sensitive surface chemistry. The catalyst design challenge is the identification of chiral surface structures that maximize enantiospecificity. Herein, we develop data driven models for the enantiospecificity of tartaric acid reactions on chiral Cu(hkl) R&S surfaces. Measurements of enantiospecific rate constants were obtained by using curved Cu(hkl) R&S surfaces that enable kinetic measurements on hundreds of chiral surface orientations. One model uses feature vectors derived from generalized coordination numbers to capture the local structure around Cu atoms exposed by the Cu(hkl) R&S surfaces. The second model introduces the use of chiral cubic harmonic functions to capture the symmetry constraints of the face-centered cubic Cu structure. The model using 58 generalized coordination numbers has a fitting error similar to that of the model using only 5 cubic harmonic functions. The two models predict maxima in the enantiospecificity on surfaces with very similar surface orientations. The models developed in this work are applicable for any enantiospecific reaction happening on any chiral material with a cubic lattice structure, opening the way to understanding the surface structure sensitivity of the enantiospecific reaction kinetics.

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

Vibrational modes and crystallographic structure of Cd 3 As 2 and (Cd 1-x Zn x ) 3 As 2 epilayers

Low-temperature Raman scattering is used to study the crystal structure of molecular-beam epitaxially grown layers of the Dirac semimetal Cd 3 As 2 and its related alloy (Cd 1-x Zn x ) 3 As 2 . The combination of narrow-linewidth spectra, multiple growth directions and full polarization analysis allows improved accuracy in identifying the irreducible representation of over 57 Raman-active vibrations. Several disagreements with previous identifications are found. Structurally, the results agree with the centrosymmetric I41/acd space group of bulk-grown Cd 3 As 2 and are clearly distinct from the Raman spectra of nanoscale platelets and wires. Three-fold twinning is seen in (112) Cd 3 As 2 grown on (111) zincblende substrates corresponding to the three possible tetragonal orientations. In dilute (Cd 1-x Zn x ) 3 As 2 , phonons have a frequency and scattering amplitude dependence on Zn concentration that is continuous with Cd 3 As 2 but at least one frequency is absent at the alloy endpoint, preventing a simple one-mode description of the alloy phonon.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Imaging gate-tunable Tomonaga–Luttinger liquids in 1H-MoSe 2 mirror twin boundaries

One-dimensional electron systems exhibit fundamentally different properties than higher-dimensional systems. For example, electron-electron interactions in one-dimensional electron systems have been predicted to induce Tomonaga-Luttinger liquid behaviour. Naturally occurring grain boundaries in single-layer transition metal dichalcogenides exhibit one-dimensional conducting channels that have been proposed to host Tomonaga-Luttinger liquids, but charge density wave physics has also been suggested to explain their behaviour. Clear identification of the electronic ground state of this system has been hampered by an inability to electrostatically gate such boundaries and tune their charge carrier concentration. Here we present a scanning tunnelling microscopy and spectroscopy study of gate-tunable mirror twin boundaries in single-layer 1H-MoSe 2 devices. Gating enables scanning tunnelling microscopy and spectroscopy for different mirror twin boundary electron densities, thus allowing precise characterization of electron-electron interaction effects. Visualization of the resulting mirror twin boundary electronic structure allows unambiguous identification of collective density wave excitations having two velocities, in quantitative agreement with the spin-charge separation predicted by finite-length Tomonaga-Luttinger liquid theory.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A series of Rb 4 Ln 2 (P 2 S 6 )(PS 4 ) 2 (Ln = La, Ce, Pr, Nd, Sm, Gd) rare earth thiophosphates with two distinct thiophosphate units [P V S 4 ] 3- and [P IV 2 S 6 ] 4-

A series of rubidium rare earth thiophosphates with the formula Rb 4 Ln 2 (P 2 S 6 )(PS 4 ) 2 (Ln = La, Ce, Pr, Nd, Sm, and Gd) were synthesized using the high temperature molten flux crystal growth method utilizing a RbBr flux. Single crystals of all title compounds, as well as phase pure powders of the La-, Ce-, and Sm-containing compositions, were obtained. Single crystals of the title compounds were characterized by single crystal and powder X-ray diffraction for structure and phase identification. Rb 4 Ln 2 (P 2 S 6 )(PS 4 ) 2 crystallizes in the monoclinic crystal system adopting the P2 1 /n space group for the large rare earths (Ln = La, Ce, Pr) and the C2/c space group for the smaller rare earths (Ln = Nd, Sm, Gd). This Rb 4 Ln 2 (P 2 S 6 )(PS 4 ) 2 series is a rare example of thiophosphates containing both tetrahedral [P V S 4 ] 3– and dimeric [P IV 2 S 6 ] 4– thiophosphate units that, in this structural family, link corrugated rare earth sulfide chains into sheets. Here, the band gaps of the materials were determined from UV–Vis data and the fluorescence spectrum of Rb 4 Ce 2 (P 2 S 6 )(PS 4 ) 2 was collected. Optical band gaps were estimated to be 2.9 and 2.4 for the Nd and Sm analogues, respectively.

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

Fault Detection on Seismic Structural Images Using a Nested Residual U-Net

Automatic identification of faults on seismic structural images is a challenging yet crucial task in quantitative seismic interpretation. Human picking or attribute-based fault detection methods may misidentify faults on noisy, complex seismic images. In this work, we develop a new automatic fault detection method using a nested residual U-shaped convolutional neural network. Each of the encoders and decoders in this neural network is a residual U-Net, leading to a nested architecture. The final fault map results from the fusion of three fault maps with low, medium, and high fault resolutions. We demonstrate the excellent fault-detection capability of our nested neural network using a series of synthetic and field seismic images. We find that our approach produces clearer and more interpretable fault maps than the current state-of-the-art U-Net fault detection method, particularly on noisy seismic images. Our new automatic fault detection method can facilitate reliable quantitative seismic interpretation on field seismic images.

58 GEOSCIENCES↗
DOE OSTI2023

Spatially confined protein assembly in hierarchical mesoporous metal-organic framework

Immobilization of biomolecules into porous materials could lead to significantly enhanced performance in terms of stability towards harsh reaction conditions and easier separation for their reuse. Metal-Organic Frameworks (MOFs), offering unique structural features, have emerged as a promising platform for immobilizing large biomolecules. Although many indirect methods have been used to investigate the immobilized biomolecules for diverse applications, understanding their spatial arrangement in the pores of MOFs is still preliminary due to the difficulties in directly monitoring their conformations. To gain insights into the spatial arrangement of biomolecules within the nanopores. We used in situ small-angle neutron scattering (SANS) to probe deuterated green fluorescent protein (d-GFP) entrapped in a mesoporous MOF. Our work revealed that GFP molecules are spatially arranged in adjacent nanosized cavities of MOF-919 to form “assembly” through adsorbate-adsorbate interactions across pore apertures. Our findings, therefore, lay a crucial foundation for the identification of proteins structural basics under confinement environment of MOFs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Nunez, Jamie↗
DOE OSTI2023

Watching Excited State Dynamics with Optical and X-ray Probes: The Excited State Dynamics of Aquocobalamin and Hydroxocobalamin

Femtosecond time-resolved X-ray absorption (XANES) at the Co K-edge, X-ray emission (XES) in the Co Kβ and valence-to-core regions, and broadband UV–vis transient absorption are combined to probe the femtosecond to picosecond sequential atomic and electronic dynamics following photoexcitation of two vitamin B12 compounds, hydroxocobalamin and aquocobalamin. Polarized XANES difference spectra allow identification of sequential structural evolution involving first the equatorial and then the axial ligands, with the latter showing rapid coherent bond elongation to the outer turning point of the excited state potential followed by recoil to a relaxed excited state structure. Time-resolved XES, especially in the valence-to-core region, along with polarized optical transient absorption suggests that the recoil results in the formation of a metal-centered excited state with a lifetime of 2–5 ps. In conclusion, this combination of methods provides a uniquely powerful tool to probe the electronic and structural dynamics of photoactive transition-metal complexes and will be applicable to a wide variety of systems.

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

nmrrr : A Reproducible Workflow for Binning and Visualizing NMR Spectra From Environmental Samples

Nuclear magnetic resonance (NMR) spectroscopy is a useful tool for detection and identification of molecular structural information, with increasing applications in environmental sciences. NMR instrument outputs are however heterogeneous and require extensive post-processing, creating barriers to their use and application by non-specialists. Here, we report on a new open-source R package, nmrrr, that processes and visualizes spectral data obtained from one-dimensional solution-state and solid-state NMR experiments; the package also performs relevant calculations commonly applied in natural organic matter communities, such as computing the relative abundance of various functional groups. We document the package's installation, dependencies, and functions; and provide a standard workflow for processing NMR data. This package is currently available on CRAN and GitHub, and community contributions are welcome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Lead compounds for the development of SARS-CoV-2 3CL protease inhibitors

We report the identification of three structurally diverse compounds – compound 4, GC376, and MAC-5576 – as inhibitors of the SARS-CoV-2 3CL protease. Structures of each of these compounds in complex with the protease revealed strategies for further development, as well as general principles for designing SARS-CoV-2 3CL protease inhibitors. These compounds may therefore serve as leads for the basis of building effective SARS-CoV-2 3CL protease inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Femtosecond stimulated Raman spectro-microscopy for probing chemical reaction dynamics in solid-state materials

Femtosecond stimulated Raman spectroscopy (FSRS) is a chemically specific vibrational technique that has the ability to follow structural dynamics during photoinduced processes such as charge transfer on the ultrafast timescale. FSRS has a strong background in following structural dynamics and elucidating chemical mechanisms; however, its use with solid-state materials has been limited. As photovoltaic and electronic devices rely on solid-state materials, having the ability to track the evolving dynamics during their charge transfer and transport processes is crucial. Following the structural dynamics in these solid-state materials will lead to the identification of specific chemical structures responsible for various photoinduced charge transfer reactions, leading to a greater understanding of the structure–function relationships needed to improve upon current technologies. Furthermore, isolating the specific nuclear motions and molecular structures that drive a desired physical process will provide a chemical blueprint, leading to the rational design and fabrication of efficient electronic and photovoltaic devices. In this perspective, we discuss technical challenges and experimental developments that have facilitated the use of FSRS with solid-state samples, explore previous studies that have identified structure–function relationships in charge transfer reactions, and analyze the future developments that will broaden and advance the field.

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

Simulations of vertical displacement oscillatory modes and global Alfvén Eigenmodes in JET geometry

Vertical Displacement Oscillatory Modes (VDOM), with frequency in the Alfvén range, are natural modes of oscillation of magnetically confined laboratory plasmas with elongated cross-section. These axisymmetric modes arise from the interaction between the plasma current, which is in equilibrium with currents flowing in external coils, and perturbed currents induced on a nearby conducting wall. The restoring force exerted by these perturbed currents on the vertical motion of the plasma column leads to its oscillatory behavior. An analytic model for VDOM was proposed based on an idealized 'straight tokamak' equilibrium with uniform equilibrium current density. This article introduces the first numerical simulations of VDOM in a realistic JET tokamak configuration, using the extended-MHD code NIMROD and drawing comparisons with Global Alfvén Eigenmodes (GAE). The results show qualitative agreement with analytic predictions regarding mode frequency and radial structure, supporting the identification of VDOM as a fundamental oscillation mode in tokamak plasmas. VDOM and GAE are modeled in a representative JET discharge, where axisymmetric perturbations with toroidal mode number n = 0 driven unstable by fast ions were observed. The two modes are examined separately using a forced oscillator within the NIMROD code, which enables a comparison of their characteristics and helps identify the experimentally observed mode possibly as a GAE.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
DOE OSTI2024

Identification of Potent and Selective Inhibitors of Acanthamoeba : Structural Insights into Sterol 14α-Demethylase as a Key Drug Target

Acanthamoeba are free-living pathogenic protozoa that cause blinding keratitis, disseminated infection, and granulomatous amebic encephalitis, which is generally fatal. The development of efficient and safe drugs is a critical unmet need. Acanthamoeba sterol 14α-demethylase (CYP51) is an essential enzyme of the sterol biosynthetic pathway. Repurposing antifungal azoles for amoebic infections has been reported, but their inhibitory effects on Acanthamoeba CYP51 enzymatic activity have not been studied. Here, we report catalytic properties, inhibition, and structural characterization of CYP51 from Acanthamoeba castellanii. The enzyme displays a 100-fold substrate preference for obtusifoliol over lanosterol, supporting the plant-like cycloartenol-based pathway in the pathogen. The strongest inhibition was observed with voriconazole (1 h IC 50 0.45 μM), VT1598 (0.25 μM), and VT1161 (0.20 μM). The crystal structures of A. castellanii CYP51 with bound VT1161 (2.24 Å) and without an inhibitor (1.95 Å), presented here, can be used in the development of azole-based scaffolds to achieve optimal amoebicidal effectiveness.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Persistent meanders and eddies lead to quasi-steady Lagrangian transport patterns in a weak western boundary current

The Brazil Current (BC) is a weak western boundary current flowing along the Southwestern Atlantic Ocean. It is frequently described as a flow with intense mesoscale activity and relatively low volume transport between 5.0 and 10.0 Sv. We use a 13-year eddy-resolving primitive-equation simulation to show that the presence of persistent meanders and eddies leads to characteristic quasi-steady Lagrangian transport patterns, aptly extracted through climatological Lagrangian Coherent Structures (cLCS). The cLCS position the surface expression of the BC core along the 2000-m isobath, in excellent visual agreement with high resolution satellite sea-surface temperature and the model Eulerian mean velocity. The cLCS deformation pattern also responds to zonally persistent cross-shelf SSH transition from positive (high) values near coastline to low (negative) values between 200- and 2000-m and back to positive (high) offshore from the 2000-m isobath. Zonally-paired cyclonic and anticyclonic structures are embedded in this transition, also causing the cLCS to deform into chevrons. An efficient transport barrier is identified close to the 200-m isobath confirmed by limited inshore movement of drogued drifters and accurately indicated by an along slope maxima of climatological strength of attraction. We also show that the persistent cyclonic and anticyclonic structures may induce localized cross-shelf transport. Regions of low climatological strength of attraction coincide with large shelves and with stagnant synthetic trajectories. We also show that cLCS accurately depict trajectories initiated at the location of Chevron’s spill (November 2011) as compared to synthetic and satellite-tracked trajectories, and the outline of the oil from that accident. There is also an agreement between the large-scale oil slicks reaching the Brazilian beaches (from August 2019 to February 2020) and the strength of climatological attraction at the coast. Our work also clarifies the influence of persistent mesoscale structures on the regional circulation. The identification and quantitative description of climatological Lagrangian coherent structures is expected to improve the effectiveness of future emergency response to oil spills, contingency planning, rescue operations, larval and fish connectivity assessment, drifter launch strategies, waste pollutant and marine debris dispersion and destination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets

Here, the discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (K 1 ). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (J s > 1 T). The structures with desirable magnetic properties center on (Fe,Co) 2⁢ P. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: Fe 2 ⁢P and Co 2 ⁢P. Our work provides guidance for synthesis. We find Fe 7 ⁢CoP 4 shows the most promise (J s = 1.03T and K 1 = 0.83MJ/m 3 ).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Identifying and tracking bubbles and drops in simulations: A toolbox for obtaining sizes, lineages, and breakup and coalescence statistics

Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Accurate detection of successive breakup and coalescence events requires that the snapshot interval be an order of magnitude smaller than the characteristic breakup and coalescence times to capture these successive events while minimizing the identification of repeated confounding events. Together, these algorithms serve as a toolbox for detailed analysis of two-phase simulations, and enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗