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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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NDE is now a relatively mature field and, even though accurate characterization of hidden flaws may still pose a challenge, the last century has been marked with the most incredible progress.
Performance metrics of lithium-ion batteries can be extracted from the analysis of electrode microstructures nanoscale imaging. The characterization workflow can involve a challenging particle identification, or instance segmentation, step. In this work, we propose a new identification method based on an original transformation: a sphere-size-based local dilation followed by a concavity-based local erosion, that is local morphology closing. The new transformation is much more efficient than the global morphology closing, with correct identification achieved with only 1.7 % dilation volume and 2.6 % erosion volume on a test geometry, compared to 39.2 % and more than 50 %, respectively, with its global counterpart. The new method has been then benchmarked versus other identification algorithms (watershed and pseudo coulomb repulsive field) on a real electrode microstructure with equal or better segmentation achieved.
Cellulose in plant cell walls are synthesized as crystalline microfibrils with diameters of 3–4 nm and lengths of around 1–10 μm. These microfibrils are known to be the backbone of cell walls, and their multiscale three-dimensional organization plays a critical role in cell wall functions including plant growth and recalcitrance to degradation. The mesoscale organization of microfibrils over a 1–100 nm range in cell walls is challenging to resolve because most characterization techniques investigating this length scale suffer from low spatial resolution, sample preparation artifacts, or inaccessibility of specific cell types. Here, we report a sum frequency generation (SFG) study determining the mesoscale polarity of cellulose microfibrils in intact plant cell walls. SFG is a nonlinear optical spectroscopy technique sensitive to the molecular-to-mesoscale order of noncentrosymmetric domains in amorphous matrices. However, the quantitative theoretical model to unravel the effect of polarity in packing of noncentrosymmetric domains on SFG spectral features has remained unresolved. In this work, we show how the phase synchronization principle of the SFG process is used to predict the relative intensities of vibrational modes with different polar angles from the noncentrosymmetric domain axis. Applying this model calculation for the first time and employing SFG microscopy, we found that cellulose microfibrils in certain xylem cell walls are deposited unidirectionally (or biased in one direction) instead of the bidirectional polarity which was believed to be dominant in plant cell walls from volume-averaged characterizations of macroscopic samples. Here, with this advancement in SFG analysis, one can now determine the relative polarity of noncentrosymmetric domains such as crystalline biopolymers interspersed in amorphous polymer matrices, which will open opportunities to study new questions that have not been conceived in the past.
Since the discovery of two-dimensional transition metal carbides, referred to as MXenes, research efforts targeted their applications in energy storage, as lithium-ion batteries and supercapacitors. This interest is attributable to MXenes’ large volumetric capacitance, high rate handling capability and stable cycling performance, which largely rely on surface chemistry provided by the terminating groups, such as -OH, -O, and -F. However, the atomic-scale characterization of these surface terminations challenges the diffraction methods. Solid-state (SS)NMR spectroscopy, especially 1 H SSNMR, is a promising approach for scrutinizing the surface terminations and the intercalated water in an atomistic-scale, yet only a few SSNMR studies in MXenes have been reported to date offering conflicting results and limited understanding of -OH terminations. Here, we used 1 H SSNMR experiments in concert with the DFT calculations of NMR parameters to identify multiple types of -OH groups, residing on the external and internal surfaces, in a commonly studied MXene, Ti 3 C 2 T x . The study also identifies bulk-like water trapped between the MXene flakes and interfacial water stranded on the surface. Lastly, two-dimensional 1 H -1 H correlation spectra elucidated the water-surface interactions and the mechanism of de-intercalation of water upon annealing.
Accessing the energy density and sustainability of calcium metal batteries requires mastering reversible calcium electrodeposition through electrolyte design. Several electrolytes support reversible, ambient temperature deposition but at utilization and rate too low for practical applications. These challenges stem from solvation structures characterized by either high barriers for cation desolvation or thermodynamic instability, leading to parasitic decomposition of the salt and solvent. The optimal solvation structure for the effective delivery of calcium to the electrode interface is not known. In this work, we show that adding a relatively small amount of a weakly associating calcium salt (calcium carba-closo-dodecaborate) to an otherwise strongly associated solution (calcium borohydride in tetrahydrofuran) produces a surprising population of fully solvent-coordinated Ca2+ cations in the form of solvent-separated ion pairs (SSIPs). We further demonstrate that the formation of these SSIPs beneficially impacts the kinetics and thermodynamics of calcium electrodeposition, revealing the unexpected finding that direct coordination of Ca2+ by the BH4– anion limits the electrodeposition process. These findings reveal how the competition between solvent and anion coordination to Ca2+ affects calcium deposition kinetics and cycling stability, setting the stage for a new calcium electrolyte design based on mixed anion electrolytes.
Rigorous in situ studies of electrocatalysts are required to enable the design of higher performing materials. Nonplatinum group metals for oxygen reduction reaction (ORR) catalysis containing light elements such as O, N, and C are known to be susceptible to both ex situ and in situ oxidation, leading to challenges associated with ex situ characterization methods. We have previously shown that the bulk O content plays an important role in the activity and selectivity of Mo–N catalysts, but further understanding of the role of composition and morphological changes at the surface is needed. Here, we report the measurement of in situ surface changes to a molybdenum nitride (MoN) thin film under ORR conditions using grazing incidence X-ray absorption and reflectivity. We show that the half-wave potential of MoN can be improved by ~90 mV by potential conditioning up to 0.8 V versus RHE. Utilizing electrochemical analysis, dissolution monitoring, and surface-sensitive X-ray techniques, we show that under moderate polarization (0.3–0.7 V vs RHE) there is local ligand distortion, O incorporation, and amorphization of the MoN surface, without changes in roughness. Furthermore, with a controlled potential hold procedure, we show that the surface changes concurrent with potential conditioning are stable under ORR relevant potentials. Conversely, at higher potentials (≥0.8 V vs RHE), the film incorporates O, dissolves, and roughens, suggesting that in this higher potential regime, the performance enhancements are due to increased access to active sites. Density functional theory calculations and Pourbaix analysis provide insights into film stability and O incorporation as a function of potential. These findings coupled with in situ electrochemical surface-sensitive X-ray techniques demonstrate an approach to studying nontraditional surfaces in which we can leverage our understanding of surface dynamics to improve performance with the rational, in situ tuning of active sites.
We report controlling site disorder in ternary and multinary compounds enables tuning optical and electronic properties at fixed lattice constants and stoichiometries, moving beyond many of the challenges facing binary alloy systems. Here, we consider possible enhancements to energy-related applications through the integration of disorder-tunable materials in devices such as light-emitting diodes, photonics, photovoltaics, photocatalytic materials, batteries, and thermoelectrics. However, challenges remain in controlling and characterizing disorder. Focusing primarily on II–IV–V 2 materials, we identify three metrics for experimentally characterizing cation site disorder. Complementary to these experiments, we discuss simulation methods to understand disordered materials. Nonidealities, such as off-stoichiometry and oxygen incorporation, can occur while synthesizing metastable disordered materials. While nonidealities may seem undesirable, we describe how if harnessed they could provide another knob for tuning disorder and subsequently properties. To illustrate the effects of disorder on device-relevant properties, we provide case examples of disordered materials and their potential in device applications.
Ni and nitrogen-doped carbons are selective catalysts for CO 2 reduction to CO (CO 2 R), but the hypothesized NiNx active sites are challenging to probe with traditional characterization methods. Here, we synthesize 61 Ni-enriched model catalysts, termed 61 NiPACN, in order to apply 61 Ni Mössbauer spectroscopy using synchrotron radiation ( 61 Ni-SR-MS) to characterize the structure of these atomically dispersed NiNx sites. First, we demonstrate that the CO 2 R results and standard characterization techniques (SEM, PXRD, XPS, XANES, EXAFS) point to the existence of dispersed Ni active sites. Then, 61 Ni-SR-MS reveal significant internal magnetic fields of ~5.4 T, which is characteristic of paramagnetic, high-spin Ni 2+ , in the 61NiPACN samples. Finally, theoretical calculations for a variety of Ni-N x moieties confirm that high-spin Ni 2+ is stable in non-planar, tetrahedrally distorted geometries, which results in calculated isotropic hyperfine coupling that is consistent with 61 Ni-SR-MS measurements.
Abstract Microbial biosynthetic gene clusters (BGCs) encoding secondary metabolites are thought to impact a plethora of biologically mediated environmental processes, yet their discovery and functional characterization in natural microbiomes remains challenging. Here we describe deep long-read sequencing and assembly of metagenomes from biological soil crusts, a group of soil communities that are rich in BGCs. Taking advantage of the unusually long assemblies produced by this approach, we recovered nearly 3,000 BGCs for analysis, including 712 full-length BGCs. Functional exploration through metatranscriptome analysis of a 3-day wetting experiment uncovered phylum-specific BGC expression upon activation from dormancy, elucidating distinct roles and complex phylogenetic and temporal dynamics in wetting processes. For example, a pronounced increase in BGC transcription occurs at night primarily in cyanobacteria, implicating BGCs in nutrient scavenging roles and niche competition. Taken together, our results demonstrate that long-read metagenomic sequencing combined with metatranscriptomic analysis provides a direct view into the functional dynamics of BGCs in environmental processes and suggests a central role of secondary metabolites in maintaining phylogenetically conserved niches within biocrusts.
We report dynamics play significant roles in chemistry and biochemistry-molecular motions impact both large- and small-scale chemical reactions in addition to biochemical processes. In many systems, including heterogeneous catalysts, the characterization of dynamics remains a challenge. The most common approaches involve the solid-state NMR measurement of anisotropic interactions, in particular 2 H quadrupolar coupling and 1 H–X dipolar coupling, which generally require isotope enrichment. Due to the high sensitivity of 1 H NMR, 1 H chemical shift anisotropy (CSA) is a particularly enticing, and underexplored, dynamics probe. We carried out 1 H CSA and 1 H– 13 C dipolar coupling measurements in a series of model supported complexes to understand how 1 H CSA can be leveraged to gain dynamic information for heterogeneous catalysts. Mathematical descriptions are given for the dynamic averaging of the CSA tensor, and its dependence on orientation and asymmetry. The variability of the orientation of the tensor in the molecular frame, in addition to its magnitude and asymmetry, negatively impacts attempts to extract quantitative dynamic information. Nevertheless, 1 H CSA measurements can reveal useful qualitative insights into the motions of a particularly dilute site, such as from a surface species.
Detection of light signals is crucial to a wide range of particle detectors. In particular, efficient detection of vacuum ultraviolet (VUV) light will provide new opportunities for some novel detectors currently being developed, but is technically challenging. In this paper, we characterized the performance of Hamamatsu VUV4 silicon photomultipliers (SiPMs) for detecting VUV argon scintillation light without wavelength shifting. Using a customized cryogenic amplifier design, we operated two models of VUV4 SiPMs inside liquid argon and thoroughly examined their direct sensitivities to liquid argon scintillation. In addition to describing their cryogenic performance, we measured a photon detection efficiency of 14.7 +1.1 -2.4 % and 17.2 +1.6 -3.0 % at 128 nm for these two VUV4 models for operation at 4 V of overvoltage, with the main uncertainty arising from the SiPM reflectivity for VUV light.
Abstract Chemical exfoliation is an attractive approach for the synthesis of graphene due to its low cost and simplicity. However, challenges still remain in the characterization of solution-processed graphene, in particular with atomic resolution. Through this work we demonstrate the x-ray pair distribution function as a novel approach to study solution-processed graphene or other 2D materials with atomic resolution, directly in solution, produced by liquid-phase and electrochemical exfoliations. The results show the disappearance of long-range atomic correlations, in both cases, confirming the production of single and few-layer graphene. In addition, a considerable ring distortion has been observed as compared to graphite, irrespective of the solvent used: the normal surface angle to the sheet of the powder sample should be less than 6°, compatible with ripples formation observed in suspended graphene. We attribute this effect to the interaction of solvent molecules with the graphene nanosheets.
GaAs is well known for its extremely high electron mobility and direct band gap. Owing to the technological advances in silicon-based technology, GaAs has been limited to niche areas. This paper discusses the application of GaAs in molecular electronics and spintronics as a potential field for considering this amazing but challenging material. GaAs is challenging because its surface is characterized by a high density of surface states, which precludes the utilization of this semiconducting material in mainstream devices. Sulfur(S)-based passivation has been found to be significantly useful for reducing the effect of dangling bonds and was researched thoroughly. GaAs applications in molecular spintronics and electronics can benefit significantly from prior knowledge of GaAs and S interactions because S is a popular functional group for bonding molecular device elements with different semiconductors and metals. In this article, the problem associated with the GaAs surface is discussed in a tutorial form. A wide variety of surface passivation methods has been briefly introduced. We attempted to highlight the significant differences in the S-GaAs interactions for different S passivation methods. We also elaborate on the mechanisms and atomic-scale understanding of the variation in surface chemistry and reconstruction due to various S passivation methods. It is envisioned that GaAs and thiol-terminated molecule-based novel devices can exhibit innovative device characteristics and bring the added advantage of S-based passivation.
Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself.
Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the “dark matter” within microbiomes that maintain healthy humans and environments.
Perovskite oxynitrides, such as SrNbO 2 N, have been shown to be promising materials for photoanodes in tandem photoelectrochemical cells, due to their suitable bandgap and charge transport. However, thin film synthesis and characterization of oxynitride perovskites are challenging due to high processing temperatures that are incompatible with available substrates. In this work, we report on reduced synthesis temperatures of SrNbO 2 N perovskite thin films on Si substrates across a range of chemical compositions. Polycrystalline thin films with perovskite crystal structure are obtained by sputtering at ambient temperature and annealing at 550-600 degrees C. The perovskite structure has a relatively broad range of cation composition between 50 and 60% Sr with varying O/N ratio according to Rutherford backscattering spectrometry. The maximum photocurrent density was obtained at 55 cation % of Sr, which is slightly Sr-rich compared to the nominal SrNbO 2 N stoichiometry. This work shows the importance of considering cation and anion composition in studying oxynitride perovskites for solar fuel applications.
Spacecraft material behavior plays a very important role in space missions. Spacecraft immersed in plasma get charged by absorbing plasma particles and by emitting electrons from spacecraft surfaces via photoelectron and secondary electron emission. Spacecraft charging depends heavily on material properties such as work function, secondary electron yield, dielectric constant, and electric conductivity among other. Material properties are typically assumed to be static in charging models. However, it is well known that this is not the case in space. This makes spacecraft charging predictions very challenging. Material properties are well characterized before the spacecraft is put in orbit through characterization in the lab under clean conditions. However, once in space, material properties change due to the harsh and very dynamic space environment. We present a new capability to predict material behavior in space from first-principles modeling. The ongoing effort seeks to couple material models, density functional theory (DFT) and molecular dynamic (MD) codes, with environment models, plasma kinetic codes. This preliminary study will show results of surface charging as a function of material work function, dielectric constant, and conductivity.
Graphite is widely used as moderator in many fission reactors and other criticality systems. The understanding of its short- and long-term behavior in fission environments is crucial not only for the operation and optimization of existing reactors but also for the efficient design and safe deployment of future advanced reactors. It is thus of utmost importance to incorporate in nuclear data libraries accurate evaluations of the interaction of thermal neutrons with graphite used in applications. This is however a complex task as there are challenges in defining the detailed characterizations of the studied material, appropriate modeling, and optimal validation suite. This will only be achieved by the concerted effort of experts of all relevant areas.