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Adjacent oxygen adatoms and lattice iron atoms on magnetite surfaces serve as dual Lewis sites that oxidize methanol to formaldehyde.
Abstract not provided
Amorphous thin film materials and heterogenized molecular catalysts supported on electrode and other functional interfaces are widely investigated as promising catalyst formats for applications in solar and electrochemical fuels catalysis. However the amorphous character of these catalysts and the complexity of the interfacial architectures that merge charge transport properties of electrode and semiconductor supports with discrete sites for multi-step catalysis poses challenges for probing mechanisms that activate and tune sites for catalysis. This minireview discusses advances in soft X-ray spectroscopy and high-energy X-ray scattering that provide opportunities to resolve interfacial electronic and atomic structures, respectively, that are linked to catalysis. This review discusses how these techniques can be partnered with advances in nanostructured interface synthesis for combined soft X-ray spectroscopy and high-energy X-ray scattering analyses of thin film and heterogenized molecular catalysts. Further, these combined approaches enable opportunities for the characterization of both electronic and atomic structures underlying fundamental catalytic function, and that can be applied under conditions relevant to device applications.
SUMMARY Characterizing explosion sources and differentiating between earthquake and underground explosions using distributed seismic networks becomes non-trivial when explosions are detonated in cavities or heterogeneous ground material. Moreover, there is little understanding of how changes in subsurface physical properties affect the far-field waveforms we record and use to infer information about the source. Simulations of underground explosions and the resultant ground motions can be a powerful tool to systematically explore how different subsurface properties affect far-field waveform features, but there are added variables that arise from how we choose to model the explosions that can confound interpretation. To assess how both subsurface properties and algorithmic choices affect the seismic wavefield and the estimated source functions, we ran a series of 2-D axisymmetric non-linear numerical explosion experiments and wave propagation simulations that explore a wide array of parameters. We then inverted the synthetic far-field waveform data using a linear inversion scheme to estimate source–time functions (STFs) for each simulation case. We applied principal component analysis (PCA), an unsupervised machine learning method, to both the far-field waveforms and STFs to identify the most important factors that control variance in the waveform data and differences between cases. For the far-field waveforms, the largest variance occurs in the shallower radial receiver channels in the 0–50 Hz frequency band. For the STFs, both peak amplitude and rise times across different frequencies contribute to the variance. We find that the ground equation of state (i.e. lithology and rheology) and the explosion emplacement conditions (i.e. tamped versus cavity) have the greatest effect on the variance of the far-field waveforms and STFs, with the ground yield strength and fracture pressure being secondary factors. Differences in the PCA results between the far-field waveforms and STFs could possibly be due to near-field non-linearities of the source that are not accounted for in the estimation of STFs and could be associated with yield strength, fracture pressure, cavity radius and cavity shape parameters. Other algorithmic parameters are found to be less important and cause less variance in both the far-field waveforms and STFs, meaning algorithmic choices in how we model explosions are less important, which is encouraging for the further use of explosion simulations to study how physical Earth properties affect seismic waveform features and estimated STFs.
Graphene-based interconnects are considered promising replacements for traditional copper (Cu) interconnect due to their great electric properties. In this article, an interconnect-memory co- design framework is developed to efficiently optimize various graphene-based interconnect technologies. Four interconnect materials and heterogeneous design schemes are benchmarked against their traditional Cu counterparts to optimize large cache-level SRAM performance in terms of delay and energy per access, energy-delay product (EDP), and energy-delay-area product (EDAP). Here, a large design space exploration is performed based on realistic subarray design and device technology. Various interconnect- and array-level design parameters are studied to quantify the true potential of graphene-based wires for optimal memory performance.
Background: Metal-containing resists entered the mainstream semiconductor industry process flow to mitigate the low absorbance of extreme ultraviolet (EUV) radiation by thin films of organic resists that lead to poor sensitivity and their inability to handle rigors of development and etching conditions. Aim: The long and rich history of using metal-containing resists in electron beam lithography can offer interesting lessons, pointers, and insights to the relatively newcomer EUV lithography, which is slightly over a decade old. Approach: Electron beam lithography has been enjoying a considerable amount of freedom in the choice of resist materials for close to 50 years; especially the use of metal-containing resists to attain not only single digit nanometer resolution, higher sensitivity, and etch resistance but also lower line-edge roughness. Here, we make a comprehensive historical review of the progress made in the patterning of metal-containing resists in electron beam lithography and derive insights that can be potentially useful in EUV patterning. Perspectives: Small molecular weight resists are proven to be crucial for achieving higher resolution with low line-edge roughness. Simplifying process flow by reducing etch-stack-layers is conceivable with metal-containing resists, along with direct-patterning of functional materials for heterogeneous integration. Efficient contact hole patterning at tighter pitches may be incumbent on progress in positive-tone resist research.
Resolving chemical species at the nanoscale is of paramount importance to many scientific and technological developments across a broad spectrum of disciplines. Hard x-rays with excellent penetration power and high chemical sensitivity are suitable for speciation of heterogeneous (thick) materials. Here, we report nanoscale chemical speciation by combining scanning nanoprobe and fluorescence-yield x-ray absorption near-edge structure (nano-XANES). First, the resolving power of nano-XANES was demonstrated by mapping Fe(0) and Fe(III) states of a reference sample composed of stainless steel and hematite nanoparticles with 50-nm scanning steps. Nano-XANES was then used to study the trace secondary phases in lithium iron phosphate (LFP) particles. We observed individual Fe-phosphide nanoparticles in pristine LFP, whereas partially (de)lithiated particles showed Fe-phosphide nanonetworks. These findings shed light on the contradictory reports on Fe-phosphide morphology in the literature. Nano-XANES bridges the capability gap of spectromicroscopy methods and provides exciting research opportunities across multiple disciplines.
Two models describe solvent transport through swollen, nonporous membranes. The pore-flow model, based on fluid mechanics, works for porous membranes, whereas the solution-diffusion model invokes molecular diffusion to treat nonporous membranes. Both approaches make valid arguments for swollen polymer membranes, but they disagree in their predictions of intramembrane pressure and concentration profiles. Using a fluid-solid model that treats the solvent and membrane matrix as separate phases, we show both classical models to be valid, to represent complementary approaches to the same phenomenon, and to make identical predictions. The fluid-solid model clarifies recent reverse osmosis measurements; provides a predictive and mechanistic basis for empirical high-pressure limiting flux phenomena, in quantitative agreement with classic measurements; and gives a framework to treat nonporous but mechanically heterogeneous membrane materials.
Our research program has combined experimental and computational methods to study a range of cluster models for heterogeneous catalytic materials. The focus of our studies has been transition metal oxide and sulfide clusters in non-traditional oxidation states, and their chemical and physical interactions with water, to model steps in the electrocatalytic or photocatalytic hydrogen evolution reaction (HER). In the closing year of this project, we have also begun to explore addition systems in preparation for future grant proposals.
Nonlocal models provide accurate representations of physical phenomena ranging from fracture mechanics to complex subsurface flows, where traditional partial differential equations fail to capture effects caused by long-range forces at the microscale and mesoscale. However, the application of nonlocal models to problems involving interfaces such as multimaterial simulations and fluid-structure interaction, is hampered by the lack of a rigorous nonlocal interface theory needed to support numerical developments. In this paper, we use an energy-based approach to develop a mathematically rigorous nonlocal interface theory which provides a physically consistent extension of the classical perfect interface PDE formulation. Numerical examples validate the proposed framework and demonstrate the scope of our theory.
Nonlocal models provide accurate representations of physical phenomena ranging from fracture mechanics to complex subsurface flows, settings in which traditional partial differential equation models fail to capture effects caused by long-range forces at the microscale and mesoscale. However, the application of nonlocal models to problems involving interfaces, such as multimaterial simulations and fluid-structure interaction, is hampered by the lack of a physically consistent interface theory which is needed to support numerical developments and, among other features, reduces to classical models in the limit as the extent of nonlocal interactions vanish. In this paper, we use an energy-based approach to develop a formulation of a nonlocal interface problem which provides a physically consistent extension of the classical perfect interface formulation for partial differential equations. Numerical examples in one and two dimensions validate the proposed framework and demonstrate the scope of our theory.
A hallmark of the scientific process since the time of Newton has been the derivation of mathematical equations meant to capture relationships between observables. As the field of mathematical modeling evolved, practitioners specifically emphasized mathematical formulations that were predictive, generalizable, and interpretable. Machine learning’s ability to interrogate complex processes is particularly useful for the analysis of highly heterogeneous, anisotropic materials where idealized descriptions often fail. As we move into this new era, we anticipate the need to leverage machine learning to aid scientists in extracting meaningful, but yet sometimes elusive, relationships between observed quantities.
Earth materials are heterogeneous across a range of spatial scales, but the resolvability of small structures is limited by sparse data coverage, noise, bandlimitedness, and other difficulties. In practice, heterogeneities below a certain size cannot be recovered from seismic data except through statistical medium descriptions, which even then can be difficult to uniquely determine. To improve the characterization of such heterogeneities, we develop a novel supervised machine learning (ML) model that provides insight about the recoverability of statistical medium properties from elastic waveform data and succeeds despite cycle-skipping and other challenges well known from elastic waveform inversion. We demonstrate the approach using random media generated by superimposing self-affine random variations on homogeneous and layered background structures. After training on sparsely-recorded, high-frequency waveforms from hundreds of different random medium realizations, we show the ability of our ML model to recover correlation lengths and other statistical properties of interest to near-surface and crustal seismology, among other fields. For frequency passbands and spatial offsets encountered in seismology, Gaussian correlation lengths and the amplitude of the random variations relative to the background model are recovered even in challenging scenarios involving unknown medium parameters, complex crustal structures, and low signal-to-noise ratio. In comparison, von Kármán correlation lengths, which are related to larger-wavelength variations of the medium than Gaussian correlation lengths, are not as well recovered. These results provide one of the first and most systematic investigations of the recoverability of statistical properties of heterogeneities below the resolution limit of deterministic seismic tomography, and suggest practical ML strategies for high-frequency waveform seismology.
Oak Ridge National Laboratory (ORNL) hosts two world-leading slow neutron sources, the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR), and is currently developing the technical design for a Second Target Station (STS) for the SNS. Upon completion of the STS project, ORNL will be uniquely positioned to optimize each of its three neutron sources, the SNS First Target Station (FTS), the STS, and HFIR, in a complementary way. Among the essential aspects of a re-imagined FTS and the current STS design are high-brightness parahydrogen moderators—moderators which are optimized for high per-unit-area neutron brightness rather than integrated-across-large-area neutron intensity. The high-brightness moderators proposed for the STS will, for the brightness metric, significantly outperform the coupled moderators currently on the FTS for appropriately optimized neutron beamlines, provided the moderating hydrogen is converted to near-equilibrium levels of parahydrogen (approximately 99.8% at 20 K). The original FTS moderators, by contrast, were conservatively designed to be relatively insensitive to the exact ortho:para ratio, with a consequent loss in performance. As a result, a redesign of the FTS moderators assuming fully converted parahydrogen could result in significant performance improvements on the FTS coupled moderators, and more consistent performance over time for all hydrogen moderators. This “parahydrogen problem” is a long-standing challenge for the effective implementation of hydrogen cold moderators at high-power neutron sources. In addition, the development of new moderator concepts, whether based on previously unused materials, structured heterogeneous arrays, or even simply on changes in overall shape and size is significantly restricted at a large-scale production facility intended to use the resulting neutron beams. Accordingly, moderators for production neutron sources are often designed in a very conservative, low-risk fashion, even though this compromises the absolute neutronic performance. Advanced moderator concepts worthy of study include features that could not be tested without redesigning and redeploying the entire existing reflector, shielding, and neutron beamline installation, making such development efforts far more expensive than building a stand-alone test facility.
Natural gas flaring is a significant challenge for oil and gas producers. One viable option to mitigate flaring is the conversion of natural gas into valuable chemicals. Applying microwave energy to heterogenous catalyst materials offers advantages in terms of higher rates and product selectivities in comparison to traditional thermal systems which can be designed for on-demand chemical production in modular reactor systems. Methane dehydroaromatization (MDHA) produces transportable, liquid aromatic products directly from natural gas, which is ideal for remote production but is hindered by low thermodynamic yields and rapid deactivation. The stability and performance of the Mo/ZSM5 catalyst is often determined by the state and nature of Mo in the catalyst, which in turn is dictated by the temperature and gaseous environment during catalyst activation. In this study, the Mo/HZSM5 catalyst was activated under three different heating modes (thermal, microwave E-field, and microwave H-field) and four different gas environments (CO, He, CH4+H2, and H2). All the activated catalysts were characterized using different tools and performance evaluated in MDHA.
Beamline 11.3.1 at the Advanced Light Source is a tender/hard (6–17 keV) x-ray bend magnet beamline recently re-purposed with a new full-field, nanoscale transmission x-ray microscope. The microscope is designed to image composite and porous materials possessing a submicrometer structure and compositional heterogeneity that determine materials’ performance and geologic behavior. The theoretical and achieved resolutions are 55 and <100 nm, respectively. The microscope is used in tandem with a <25 nm eccentricity rotation stage for high-resolution volume imaging using nanoscale computed tomography. The system also features a novel bipolar illumination condenser for the illumination of an ~100 μm spot of interest on the sample, followed by a phase-type zone plate magnifying objective of ~52 µm field of view and a phase detection ring. The zone plate serves as the system objective and magnifies the sample with projection onto an indirect x-ray detection system, consisting of a polished single crystal CsI(Tl) scintillator and a range of high-quality Plan Fluorite visible light objectives. The objectives project the final visible light image onto a water-cooled CMOS 2048 × 2048-pixel 2 detector. Here, in this paper, we will discuss the salient features of this instrument and describe early results from imaging the internal three-dimensional microstructure and nanostructure of target materials, including fiber-reinforced composites and geomaterials.
Reactive burn models for propagating detonation waves in an explosive are based on the ZND (Zel'dovich-Von Neumann-Doring) theory. A key property of the ZND theory, which leads to self-sustaining detonation waves, is a sonic point relative to the front in the reaction zone. Moreover, burn models assume the explosive is a homogeneous material. This assumption is used for plastic-bonded explosives, even though they are heterogeneous materials. The heterogeneities are accounted for only with an empirical burn rate. Furthermore, the reaction-zone width can be less than the length scale of heterogeneities. This raises the question of the effect of heterogeneities on the reaction zone. In particular, whether the reaction zone can be steady and have a well defined sonic point. To examine this question we focus on PBX 9501 since there is data on the grain-binder heterogeneities and on the reaction-zone velocity time history of a steady planar detonation wave. Using pseudo-mesoscale 1-D simulations, we examine the effect of a detonation wave transversing explosive and binder segments of random length. The simulations show that the reaction zone is quasi-steady with only short wavelength small amplitude variations in the detonation wave pressure and speed. The main effect of the heterogeneities is to introduce noise in the pressure and velocity fields that propagates into the Taylor wave behind the detonation. This is consistent with the scatter in the reaction-zone measurements for PBX 9501.
Porous materials are typically heterogeneous and they contain large variations of micro-/nano-pore structures, causing complicated behaviors. In continuum models, most mechanical properties of porous materials are estimated based on porosity, while the variations of micro/nano structures are ignored. That could be problematic as the microscopic heterogeneity may affect the mechanical response of porous materials. Thus, understanding micro/nano heterogeneity impact has been the focus in many scientific and engineering subjects. In the present study, we investigated the effect of nanopore structure (including pore shape and orientation) as well as porosity on mechanical properties of amorphous silica (a-SiO 2 ). The pore sizes in our simulations are comparable to the corresponding ones observed in a-SiO 2 based materials. We found that the existing of nanopores strongly influences Young’s modulus (E) and critical energy release rate (G IC ). These properties decrease with increasing porosity. Importantly, the impact of nanopores was characterized by structural parameters of porous materials. In addition to dependency on porosity, Young’s modulus also was found to vary as a function of potential energy per atom, which highly depends on nanopore shape. Furthermore, critical energy release rate was found to increase with increasing ligament length (also known as pore wall thickness). Finally, the results highlighted the importance of nanopore structures, which must be taken into account when studying fracture mechanisms in porous materials. Based on our findings, it was proposed that mechanical properties of porous materials can be controlled by nano-engineering pore structures.