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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 163 records · Page 9

Using machine learning to predict the correlation of spectra using SDSS magnitudes as an improvement to the Locus Algorithm

The Locus Algorithm is a new technique to improve the quality of differential photometry by optimising the choices of reference stars. At the heart of this algorithm is a routine to assess how good each potential reference star is by comparing its SDSS magnitude values to those of the target star. In this way, the difference in wavelength-dependent effects of the Earth’s atmospheric scattering between target and reference can be minimised. This paper sets out a new way to estimate the quality of each reference star using machine learning. A random subset of stars from SDSS with spectra was chosen. For each one, a suitable reference star, also with a spectrum, was chosen. The correlation between the two spectra in the SDSS r band (between 550 nm and 700 nm) was taken to be the gold-standard measure of how well they match up for differential photometry. The five SDSS magnitude values for each of these stars were used as predictors. A number of supervised machine learning models were constructed on a training set of the stars and were each evaluated on a testing set. The model using Support Vector Regression had the best performance of these models. It was then tested on a final, hold-out, validation set of stars to get an unbiased measure of its performance. With an R 2 of 0.62, the SVR model presents enhanced performance for the Locus Algorithm technique.

79 ASTRONOMY AND ASTROPHYSICS↗

Portable Distortion Free Large Solid Angle Coverage Detector

Neutron single crystal diffractometers require large solid angle coverage for optimum performance. This can be achieved by tiling flat detectors in a cylindrical or spherical geometry, but results in large gaps in detector coverage and parallax distortion. The detector edges exhibit degraded resolution, distortion, and gamma rejection. Since the detector edge regions are a significant fraction of the detector active area, they must be removed from the experimental data set, requiring extra beam time to collect enough analysis data. A spherical detector with a continuous surface would effectively address this issue while eliminating most boundary ‘dead’ areas. Here we report on the development of a novel hemispherical shaped neutron detector using seamlessly tiled readout modules to form the desired shape. The heart of the detector is a specially developed curved neutron scintillator coupled to high resolution silicon photomultiplier (SiPM) Anger cameras via custom made fiber optic tapers (FOTs). The detector has been assembled and initial tests have been conducted at the High Flux Isotope Reactor (HFIR) beamlines at Oak Ridge National Laboratory (ORNL). Here, in this work, we describe details of the scintillator design, fabrication and characterization, evaluation of individual detector modules, the details of the detector design implementation, and evaluation of the assembled detector at ORNL beamlines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Do we owe our existence to gravitational waves?

Two heavy elements essential to human biology are thought to have been produced by the astrophysical r-process, which occurs in neutron-rich environments: iodine is a constituent of thyroid hormones that affect many physiological processes including growth and development, body temperature and heart rate, and bromine is essential for tissue development and architecture. Collisions of neutron stars (kilonovae) have been identified as sources of r-process elements including tellurium, which is adjacent to iodine in the periodic table, and lanthanides. Neutron-star collisions arise from energy loss due to gravitational-wave emission from binary systems, leading us to suggest that gravitational waves have played a key role in enabling human life by producing iodine and bromine. We propose probing this proposal by searching in lunar material for live 129 I deposited by a recent nearby kilonova explosion.

Human biochemistry↗

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol –1 , and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

59 BASIC BIOLOGICAL SCIENCES↗

Defect-Driven Oxide Transformations and the Electrochemical Interphase

The redox reaction pathway is crucial to the sustainable production of the fuels and chemicals required for a carbon-neutral society. Our society is becoming increasingly dependent on devices using batteries and electrolyzers, all of which rely on a series of redox reactions. The overall properties of oxide materials make them very well suited for such electrochemical and catalytic applications due to their associated cationic redox properties and the static site-adsorbate interactions. As these technologies have matured, it has become apparent that defect-driven redox reactions, defect-coupled diffusion, and structural transformations that are both time- and rate-dependent are also critical materials processes. This change in focus, considering not only redox properties but also more complex, dynamic behaviors, represents a new research frontier in the molecular sciences as they are strongly linked to device operation and degradation and lie at the heart of various phenomena that take place at electrochemical interfaces. Fundamental studies of the structural, electronic, and chemical transformation mechanisms are key to the advancement of materials and technological innovations that could be implemented in various electrochemical systems. In this Account, we focus on recent studies and advances in characterizing and understanding the dynamic redox evolution and structural transformations that take place in model perovskites and layered oxides under reactive conditions and correlate them with degradation mechanisms and operations in electrolyzers and batteries. We show that the dynamic evolution of oxygen vacancies and cationic migration in the surface or bulk occurs at the solid-liquid interface, using a combination of different synchrotron-based Xray spectroscopies and scattering probes. Detailed redox-structure-reactivity correlation studies show how defects and diffusion processes can be tailored to drive various physical and chemical transformations in electrolyzers and batteries. We also highlight a strong correlation between oxygen redox reactivity and structural reorganization in both model thin films and particles, helping to bridge the gap between fundamental studies of the reaction mechanism and device applications. Finally, on the basis of these findings, we discuss strategies to probe and tune the redox reactivity and structural stability of the redox-active oxide interphase toward devising efficient pathways for energy and chemical harvesting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ensembles of Metastable States Govern Heterogeneous Catalysis on Dynamic Interfaces

Heterogeneous catalysis is at the heart of chemical industry. Being able to tune and design efficient catalysts for processes of interest is of an utmost importance, and for this, the molecular-level understanding of heterogeneous catalysts is the first step, and indeed a prime focus of the modern catalysis research. For a long time, a single most thermodynamically stable structure of the catalytic interface attained in reaction conditions had been envisioned as the reactive phase. However, some catalytic interfaces continue to undergo structural dynamics in the steady state, triggered by high temperatures, pressures, and binding and changing reagents. Among particularly dynamic interfaces are such widely-used catalysts as crystalline and amorphous surfaced supporting (sub-)nano metallic clusters. Recently, it became clear that this dynamic fluxionality causes the supported clusters to populate many distinct structural and stoichiometric states in catalytic conditions. Hence, the catalytic interface should be viewed as an evolving statistical ensemble of many (not one) structures. As such, every member in the ensemble contributes to the properties of the catalyst differently, and in proportion to its probability to be populated. This new notion flips the established paradigm and calls for new theory, modeling approaches, operando measurements, and updated design strategies. The statistical ensemble nature of surface-supported sub-nano cluster catalysts can be exemplified by oxide-supported and adsorbate-covered Pt, Pd, Cu, CuPd clusters, catalytic toward oxidative and non-oxidative dehydrogenation. They have access to a variety of 3D and quasi-2D shapes. The compositions of their thermal ensembles are dependent on the cluster size, leading to size-specific catalytic activities and the famous “every atom counts” phenomenon. The support and adsorbates affect catalyst structures, and state of the reacting species causes the ensemble to change in every reaction intermediate. The most stable member of the ensemble dominates the thermodynamic properties of the corresponding intermediate, whereas the kinetics can be determined by more active but less populated metastable catalyst states, and that suggests that many earlier studies might have overlooked the actual active sites. Both effects depend on the relative time-scales of catalyst restructuring and reaction dynamics. The catalyst may routinely operate off-equilibrium. Ensemble phenomena lead to surprising exceptions from established rules of catalysis, such as scaling relations, and Arrhenius behavior. Catalyst deactivation is also an ensemble property, and its extent of mitigation can be predicted through the new paradigm. These findings were enabled by advances in theory, such as global optimization and subsequent utilization of multiple local minima, and pathways sampling, as well as operando catalyst characterization. The fact that the per-site and per-species resolution is needed for the description and predicting of catalyst properties gives theory the central role in catalysis research, as most experiments provide ensemble-average information and cannot detect the crucial minority species that may be responsible for the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Rietveld Refinement for Macromolecular Powder Diffraction

Powder X-ray diffraction (PXRD) has been employed extensively for the structural characterization of materials, quantitative analysis of multicomponent mixtures, phase identification, texture, and microstructural analysis. At the heart of all those lies the Rietveld method, which has revolutionized the use of powder diffraction for materials studies. Initially applied to minerals and inorganics, the Rietveld method has progressively been used for more complex materials, for instance, zeolites and pharmaceuticals. A major advance of the method came some 20 years ago, when the first protein structure was successfully refined. Because of the sheer complexity of macromolecules, several new approaches and algorithms had to be pioneered or adapted from macromolecular single-crystal diffraction experiments, thus constituting macromolecular PXRD a quite unique field of study. This review aims to provide necessary elements of theory and application of structure solution and refinement via the Rietveld method for macromolecular PXRD data. Here, practical explanations and highlighted case studies are also presented.

36 MATERIALS SCIENCE↗

Electrical Shock During Phone Assisted Troubleshooting of Laboratory Equipment

We report researchers have the potential to be exposed to a wide variety of hazards inherent to the equipment they use and maintain. When equipment does not function as expected, researchers sometimes reach out to their vendors for assistance. Early diagnostic or troubleshooting interactions between researcher and vendor are often conducted over the telephone and can lead to researchers performing work outside of their area of expertise and exposure to unknown hazards. This type of interaction significantly contributed to an incident where during diagnostic activities a researcher accidentally contacted, and discharged, a capacitor in an X-ray diffraction instrument. While this incident did not produce a serious injury, if the capacitor discharge path had occurred hand-to-hand across the heart, a serious injury may have been possible.

47 OTHER INSTRUMENTATION↗

Influence of Polymorphism on the Electronic Structure of Ga 2 O 3

The search for new wide-band-gap materials is intensifying to satisfy the need for more advanced and energy-efficient power electronic devices. Ga 2 O 3 has emerged as an alternative to SiC and GaN, sparking a renewed interest in its fundamental properties beyond the main β-phase. Here, three polymorphs of Ga 2 O 3 , α, β, and ε, are investigated using X-ray diffraction, X-ray photoelectron and absorption spectroscopy, and ab initio theoretical approaches to gain insights into their structure–electronic structure relationships. Valence and conduction electronic structure as well as semicore and core states are probed, providing a complete picture of the influence of local coordination environments on the electronic structure. State-of-the-art electronic structure theory, including all-electron density functional theory and many-body perturbation theory, provides detailed understanding of the spectroscopic results. The calculated spectra provide very accurate descriptions of all experimental spectra and additionally illuminate the origin of observed spectral features. This paper provides a strong basis for the exploration of the Ga 2 O 3 polymorphs as materials at the heart of future electronic device generations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Function, Structure, and Regulation of Nitrogen Fixation-like Metalloproteins for Nitrogen, Energy, Carbon, and Sulfur Metabolism

Nitrogenases (N 2 ases) and nitrogen fixation-like (NFL) systems play distinct roles in nitrogen, carbon, sulfur, and energy metabolism based on their fundamental differences in structure and metallocofactor identity. As new NFL systems have recently been identified and characterized, striking parallels and differences compared to N 2 ase structure, catalysis, and regulation have emerged. NFL systems use metallocofactors that span from simple [4Fe-4S] clusters to complex clusters akin to FeMo-co, previously only thought to occur in N 2 ase. This review describes the present state of knowledge on the function, structure, catalytic mechanisms, and regulation of NFL systems that perform distinct biological roles across all three domains of life. Recent advancements in N 2 ase spectroscopic techniques for probing metallocofactor structure and electronic states guide current and future work on how each NFL system catalyzes its specific biological reaction(s). Key knowledge gaps and needed areas of research for uncovering the specific metallocofactors and structural motifs that are at the heart of NFL system reaction specificity, along with how these systems are regulated, are discussed.

Bacteria↗

Perspectives on van der Waals Density Functionals: The Case of TiS 2

The van der Waals interaction is of foundational importance for a wide variety of physical systems. In particular, van der Waals forces lie at the heart of potential device technologies that may be realized from the functional organization of layered two-dimensional (2D) nanomaterials. Furthermore, for intermediate to large-scale applications modeling, van der Waals density functionals have become the de facto choice for first-principles calculations. In particular, the vdW-DF family of functionals have provided a systematic approach to this theoretically challenging problem. While much progress has been made, there remains room for improvement in the microscopic description of vdW forces from these density functionals. In this work, we compute benchmark results for the binding energy and the electronic density response to binding in TiS2 via accurate diffusion quantum Monte Carlo calculations. We compare these benchmark data to results obtained from local, semilocal, and van der Waals functionals. In particular, we gauge the quality of the original vdW-DF/vdW-DF2 functionals, as well as updated variants such as vdW-DF-C09, vdW-DF-optB88, vdW-DF-optB86b, and vdW-DF2-B86R. We find a close relationship between the accuracy of predicted interlayer separation distances and binding energies for TiS 2 , with the vdW-DF-optB88 functional performing very well in terms of both quantities. In general, the more recently developed functionals are systematic improvements over older ones. However, when considering the response of the electron density to binding, we find that local-density approximation (LDA) and PBEsol generally outperform the vdW-DF functionals in describing the interlayer charge accumulation with vdW-DF-C09 variants performing the best overall.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enhanced Strong-Field Ionization and Fragmentation of Methanol Using Noncommensurate Fields

Electron-initiated chemistry with chemically relevant electron energies (10–200 eV) is at the heart of several high-energy processes and phenomena. To probe these dissociation and fragmentation reactions with femtosecond resolution requires the use of femtosecond lasers to induce ionization of the polyatomic molecules via electron rescattering. Here, we combine noncommensurate fields with intensity-difference spectra using methanol as a model system. Experimentally, we find orders of magnitude enhancement in several product ions of methanol when comparing coherent vs incoherent combinations of noncommensurate fields. This approach not only mitigates multiphoton ionization and multicycle effects during ionization but also enhances tunnel ionization and electron rescattering energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

C 1 Chemistry on Metal Carbide Nanoparticles: Boosting the Conversion of CO 2 and CH 4

The studies described in this Perspective show that transition metal carbide (TMC) nanoparticles can be very useful for the activation of three molecules located at the heart of C 1 chemistry: H 2 , CH 4 , and CO 2 . They also can play a major role in the trapping and conversion of two major greenhouse gases. A combination of experiment and theory has shed light on the physical and chemical properties of these systems, which can be very different from those of bulk carbides. Molecular clusters of these compounds, which can be inserted inside the cages of zeolites or carbon nanotubes, have unsaturated metal and carbon atoms that frequently work in a cooperative way when dealing with hard-to-activate molecules, such as CH 4 and CO 2 . These molecular clusters can evolve into nanoparticles of small to medium size (<15 nm) that have unique carbon/metal ratios and structures not seen in the bulk metal carbides. Even when their structures are cuts from bulk lattices, the TMC nanoparticles have corner or edge atoms that are active for the cleavage and conversion of C–H and C–O bonds. In this paper, we cover experimental and theoretical studies with well-defined metal carbide nanoparticles prepared by different methods, free and supported on diverse substrates. The Perspective ends with a discussion of current challenges and potential applications.

03 NATURAL GAS↗

Laser-Induced Photoreduction of Iron(III) Oxide Nanoparticles Enhanced by the Presence of Organic Chromophores

Light-induced electron transfer between chromophoric organic matter and Fe(III)-oxides lies at the heart of aqueous Fe(II) fluxes in the photic zone of natural systems. Understanding this photoreductive dissolution process is also essential for developing water purification techniques based on this class of materials. Previously, optical transient absorption spectroscopy (TAS) measurements revealed that sub-picosecond relaxation times of photo-excited rhodamine B (RhB) dye increased when sorbed onto hematite nanoparticles (HNPs), consistent with electron transfer to the oxide. Here, in the present study, we exploit time-resolved X-ray absorption spectroscopy (XAS) at the Fe K-edge to follow the Fe oxidation state for this same process to (i) confirm that RhB photoexcitation leads to interfacial electron transfer and Fe reduction; and (ii) quantify the lifetime of injected electrons as a function of solution conditions. Regardless of RhB dye availability and pH, direct band gap photoexcitation of HNPs yields an Fe(II)-like small polaronic absorption signature with a lifetime of ~ 1 ns, an order of magnitude longer than previously reported. However, when RhB is present at low pH under conditions where dye favorably interacts with the positively charged hematite surface, a second relaxation process approaching microsecond timescales is observed that likely represents back-reaction with the photoexcited adsorbed dye. At pH above neutral, the efficiency of the interfacial electron transfer is diminished by weaker interaction between sorbed dye and particle surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Prediction of Multiple Hydrogen Ligation at a Vanadium(II) Site in a Metal–Organic Framework

Densifying hydrogen in a metal-organic framework (MOF) at moderate pressures can circumvent challenges associated with high-pressure compression. The highly tunable structural and chemical composition in MOFs affords vast possibilities to optimize binding interactions. At the heart of this search are the nanoscale characteristics of molecular adsorption at the binding site(s). Using density functional theory (DFT) to model binding interactions of hydrogen to the exposed metal site of cation-exchanged MFU-4 l , we predict multiple hydrogen ligation of H 2 at the first coordination sphere of V(II) in V(II)-exchanged MFU-4 l . We find that the strength of this binding between the metal site and H 2 molecules can be tuned by altering the halide counterion adjacent to the metal site and that the fluoride containing node affords the most favorable interactions for high-density H 2 storage. In conclusion, using energy decomposition analysis, we delineate electronic contributions that enable multiple hydrogen ligation and demonstrate its benefits for hydrogen adsorption and release at modest pressures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗