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At least 613 records · Page 34

Defect-mediated selective hydrogenation of nitroarenes on nanostructured WS 2

Transition metal dichalcogenides (TMDs) are well known catalysts as both bulk and nanoscale materials. Two-dimensional (2-D) TMDs, which contain single- and few-layer nanosheets, are increasingly studied as catalytic materials because of their unique thickness-dependent properties and high surface areas. Here, colloidal 2H-WS 2 nanostructures are used as a model 2-D TMD system to understand how high catalytic activity and selectivity can be achieved for useful organic transformations. Free-standing, colloidal 2H-WS 2 nanostructures containing few-layer nanosheets are shown to catalyze the selective hydrogenation of a broad scope of substituted nitroarenes to their corresponding aniline derivatives in the presence of other reducible functional groups. Microscopic and computational studies reveal the important roles of sulfur vacancy-rich basal planes and tungsten-terminated edges, which are more abundant in nanostructured 2-D materials than in their bulk counterparts, in enabling the functional group selectivity. At tungsten-terminated edges and on regions of the basal planes having high concentrations of sulfur vacancies, vertical adsorption of the nitroarene is favored, thus facilitating hydrogen transfer exclusively to the nitro group due to geometric effects. At lower sulfur vacancy concentrations on the basal planes, parallel adsorption of the nitroarene is favored, and the nitro group is selectively hydrogenated due to a lower kinetic barrier. These mechanistic insights reveal how the various defect structures and configurations on 2-D TMD nanostructures facilitate functional group selectivity through distinct mechanisms that depend upon the adsorption geometry, which may have important implications for the design of new and enhanced 2-D catalytic materials across a potentially broad scope of reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Characterization of Mk-IV and EBR-II X441A Metallic Fuel Pins for the THOR-C-2 and THOR-M-TOP-1 Experiments: Results from Post-Transient Neutron Tomography of THOR-C-2 Capsule and Pre-transient Non-Destructive and Destructive Examination of DP 36 and DP 40 (Rev.1)

Current interest in sodium-cooled fast reactor designs, such as TerraPower’s Natrium Reactor, has highlighted the need for advanced-reactor fuel technology development. A Fuel Safety Research and Development (FSRD) program for metallic fast reactor fuels has been created to achieve comprehensive safety testing within the re-commissioned Transient Reactor Test (TREAT) facility at the Idaho National Laboratory. Despite over 60 years of metallic fuel irradiation, uncertainties exist in the performance of the fuel system, particularly under anticipated operational occurrences and severe accident scenarios. Throughout historical testing within the Experimental Breeder Reactor (EBR)-II and the Fast Flux Test Facility, fuel behavior has demonstrated benign response to transient reactor conditions; however, accurate predictions of failure thresholds rely heavily on fuel composition and irradiation history. In advancing the FSRD program, two planned transient heating experiments are at various stages of completion. The Temperature Heat sink Overpower Response (THOR)-C-2, fueled with an unirradiated Mk IV U-10Zr pin, has undergone transient irradiation in TREAT and post-transient three-dimensional neutron tomography. Additionally, pre-transient characterization of test and sibling U-19Pu-10Zr pins for THOR-M-TOP-1 was evaluated by both non-destructive and destructive methods. The test and sibling pins were selected from previously irradiated EBR-II experiment X441A. Both pins underwent visual examination, precise gamma spectrometry, two-dimensional neutron radiography, and element contact profilometry while the sibling pin was additionally subjected to sectioning and optical microscopy. THOR-C-2 radiography captured the fuel and cladding relocation during the intermediate transient at the top and bottom of the THOR capsule, allowing key features to be linked to the pin’s measured thermal response. For THOR M TOP 1, a solid baseline for steady-state behavior has been established. No anomalous features were identified in either the test or sibling pin. Defining characteristics and features were recorded for further comparisons.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Selecting Meteorological Input for the Global Modeling Initiative Assessments

The Global Modeling Initiative (GMI) science team has developed a three dimensional chemistry and transport model (CTM) to evaluate the impact of the exhaust of supersonic aircraft on the stratosphere. An important goal of the GMI is to test modules for numerical transport, photochemical integration, and model dynamics within a common framework. This work is focussed on the dependence of the overall assessment on the wind and temperature fields used by the CTM. Three meteorological data sets for the stratosphere were available to GMI: the National Center for Atmospheric Research Community Climate Model (CCM2), the Goddard Earth Observing System Data Assimilation System (GEOS-DAS), and the Goddard Institute for Space Studies general circulation model (GISS-2'). Objective criteria were established by the GMI team to evaluate which of these three data sets provided the best representation of trace gases in the stratosphere today. Tracer experiments were devised to test various aspects of model transport. Stratospheric measurements of long-lived trace gases were selected as a test of the CTM transport. This presentation describes the criteria used in grading the meteorological fields and the resulting choice of wind fields to be used in the GMI assessment. This type of objective model evaluation will lead to a higher level of confidence in these assessments. We suggest that the diagnostic tests shown here be used to augment traditional general circulation model evaluation methods.

Strahan, Susan↗

A robust two-dimensional zirconium-based luminescent coordination polymer built on a V-shaped dicarboxylate ligand for vapor phase sensing of volatile organic compounds

Here we report herein a new two-dimensional zirconium-based luminescent coordination polymer Zr 6 (sdba) 4 (μ 3 -O) 4 (μ 3 - OH) 4 (HCOO) 2 (OH) 2 (H 2 O) 2 (1) [sdba = 4,4'-sulfonyldibenzoate] exhibiting selective fluorescence responses towards a variety of volatile organic compounds upon exposure in the vapor phase. Having a unique two-dimensional signal response towards aromatic molecules, including but not limited to nitroaromatic explosives, it is capable of identification of a diverse set of analytes. In addition, compound 1 shows a remarkably high sensitivity toward acetone vapors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Assembly of Robust Bacterial Microcompartment Shells Using Building Blocks from an Organelle of Unknown Function

Bacterial microconnpartnnents (BMCs) sequester enzymes from the cytoplasmic environment by encapsulation inside a selectively permeable protein shell. Bioinformatic analyses indicate that many bacteria encode BMC clusters of unknown function and with diverse combinations of shell proteins. The genome of the halophilic myxobacterium Haliangium ochraceum encodes one of the most atypical sets of shell proteins in terms of composition and primary structure. We found that microconnpartnnent shells could be purified in high yield when all seven H. ochraceum BMC shell genes were expressed from a synthetic operon in Escherichia coll. These shells differ substantially from previously isolated shell systems in that they are considerably smaller and more homogeneous, with measured diameters of 39 2 nm. The size and nearly uniform geometry allowed the development of a structural model for the shells composed of 260 hexagonal units and 13 hexagons per icosahedral face. We found that new proteins could be recruited to the shells by fusion to a predicted targeting peptide sequence, setting the stage for the use of these remarkably homogeneous shells for applications such as three-dimensional scaffolding and the construction of synthetic BMCs. Our results demonstrate the value of selecting from the diversity of BMC shell building blocks found in genomic sequence data for the construction of novel compartments. (C) 2014 Elsevier Ltd. All rights reserved.

Lassila, JK↗

A New Hole Transport Material for Efficient Perovskite Solar Cells With Reduced Device Cost

To realize commercialization of perovskie solar cell (PVSC) technology, it is essential to reduce the device costs while maintaining high power conversion efficiencies (PCEs). So far, the high cost of the most commonly used hole selective material, 2,2′,7,7′‐Tetrakis (N,N‐di‐p‐methoxyphenylamino)‐9,9′‐spirobifluorene (spiro‐OMeTAD), for high‐PCE PVSCs presents a significant obstacle for device cost reduction. In this work, the synthesis and characterization of a new spiro‐OMeTAD derivative hole selective material, 2,6,14‐tris(5′‐(N,N‐bis(4‐methoxyphenyl)aminophenol‐4‐yl)‐3,4‐ethylenedioxythiophen‐2‐yl)‐triptycene (TET) is reported. TET features a three‐dimensional structure consisting of a triptycene core and triarylamine arms linked by 3,4‐ethylenedioxythiophene, facilitating efficient hole transport. Planar PVSCs using TET hole selective layers (HSLs) achieved high fill factors of over 81% and steady‐state efficiencies of up to 18.6%, comparable with that (19.0%) of PVSC using spiro‐OMeTAD HSL. Importantly, the hereby reported efficient PVSCs can be produced with very thin TET HSLs (about 30 nm). Considering the lower laboratory synthesis and purification cost ($123 vs. $500 g −1 ) and thinner HSL (30 vs. 200 nm), the cost for TET on a unit area of one device is 25 times lower than that for high‐purity spiro‐OMeTAD. The device with TET HSL shows good stability under continuous illumination. Therefore, this work makes a significant step forward toward the commercialization of the emerging PVSC technology.

Sun, Yuhao↗

MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

59 BASIC BIOLOGICAL SCIENCES↗

Gel-Derived Amorphous Bismuth–Nickel Alloy Promotes Electrocatalytic Nitrogen Fixation via Optimizing Nitrogen Adsorption and Activation

To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH 3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. Herein we developed a three-dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower fre-energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH 3 production with a yield rate of 17.5 μg h -1 mg cat -1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. Furthermore, this work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Studies of the influence of heat pipe model and coupling strategy in heat pipe microreactor simulations using Sockeye

This work demonstrates various modeling approaches for thermally coupling heat pipes to a graphite monolithic block in a microreactor context, accounting for the presence of a gap. We show that heat pipe model selection has a significant impact on the transient behavior of a three-dimensional coupled microreactor assembly problem by comparing results obtained using different heat pipe models from the heat pipe code Sockeye. Additionally, we show that the choice of coupling strategy has significant consequences for both accuracy and performance. Furthermore, we found that our heat flux transfer strategy exhibited greater accuracy than our temperature transfer strategy, even when comparing a loose coupling of the heat flux transfer strategy to a tight coupling of the temperature transfer strategy. Although this research uses only theoretical test problems, it provides important insights in modeling thermal fluids phenomena in heat pipe microreactors.

Direwolf↗

Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate–adsorbate lateral interactions. Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. Here, the mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.

36 MATERIALS SCIENCE↗

Electronically Coupled 2D Polymer/MoS 2 Heterostructures

Emergent quantum phenomena in electronically coupled two-dimensional heterostructures are central to next-generation optical, electronic, and quantum information applications. Tailoring electronic band gaps in coupled heterostructures would permit control of such phenomena and is the subject of significant research interest. Two-dimensional polymers (2DPs) offer a compelling route to tailored band structures through the selection of molecular constituents. However, despite the promise of synthetic flexibility and electronic design, fabrication of 2DPs that form electronically coupled 2D heterostructures remains an outstanding challenge. Here, we report the rational design and optimized synthesis of electronically coupled semiconducting 2DP/2D transition metal dichalcogenide van der Waals heterostructures, demonstrate direct exfoliation of the highly crystalline and oriented 2DP films down to a few nanometers, and present the first thickness-dependent study of 2DP/MoS 2 heterostructures. In this work, control over the 2DP layers reveals enhancement of the 2DP photoluminescence by two orders of magnitude in ultrathin sheets and an unexpected thickness-dependent modulation of the ultrafast excited state dynamics in the 2DP/MoS 2 heterostructure. These results provide fundamental insight into the electronic structure of 2DPs and present a route to tune emergent quantum phenomena in 2DP hybrid van der Waals heterostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Graphene nanoribbons initiated from molecularly derived seeds

Abstract Semiconducting graphene nanoribbons are promising materials for nanoelectronics but are held back by synthesis challenges. Here we report that molecular-scale carbon seeds can be exploited to initiate the chemical vapor deposition (CVD) synthesis of graphene to generate one-dimensional graphene nanoribbons narrower than 5 nm when coupled with growth phenomena that selectively extend seeds along a single direction. This concept is demonstrated by subliming graphene-like polycyclic aromatic hydrocarbon molecules onto a Ge(001) catalyst surface and then anisotropically evolving size-controlled nanoribbons from the seeds along $$\left\langle 110\right\rangle$$ 110 of Ge(001) via CH 4 CVD. Armchair nanoribbons with mean normalized standard deviation as small as 11% (3 times smaller than nanoribbons nucleated without seeds), aspect ratio as large as 30, and width as narrow as 2.6 nm (tunable via CH 4 exposure time) are realized. Two populations of nanoribbons are compared in field-effect transistors (FETs), with off-current differing by 150 times because of the nanoribbons’ different widths.

36 MATERIALS SCIENCE↗

Optoelectronic simulation of GaAs solar cells with angularly selective filters

We discuss the influence of angularly selective filters on thin film gallium arsenide solar cells. For this reason, the detailed balance model was refined to fit our needs with respect to Auger recombination, reflection, transmission, and realistic absorption. For calculating real systems, an approach was made to include optical effects of angularly selective filters into electron-hole dynamic equations implemented in PC1D, a one dimensional solar cell calculation tool. With this approach, we find a relative V{sub oc} increase of 5% for an idealized 100 nm GaAs cell, including Auger recombination.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Charge transfer dynamics and interlayer exciton formation in MoS 2 /VOPc mixed dimensional heterojunction

Mixed-dimensional van der Waals heterojunctions involve interfacing materials with different dimensionalities, such as a 2D transition metal dichalcogenide and a 0D organic semiconductor. These heterojunctions have shown unique interfacial properties not found in either individual component. Here, we use femtosecond transient absorption to reveal photoinduced charge transfer and interlayer exciton formation in a mixed-dimensional type-II heterojunction between monolayer MoS 2 and vanadyl phthalocyanine (VOPc). Selective excitation of the MoS 2 exciton leads to hole transfer from the MoS 2 valence band to VOPc highest occupied molecular orbit in ~710 fs. On the contrary, selective photoexcitation of the VOPc layer leads to instantaneous electron transfer from its excited state to the conduction band of MoS 2 in less than 100 fs. Further, this light-initiated ultrafast separation of electrons and holes across the heterojunction interface leads to the formation of an interlayer exciton. These interlayer excitons formed across the interface lead to longer-lived charge-separated states of up to 2.5 ns, longer than in each individual layer of this heterojunction. Thus, the longer charge-separated state along with ultrafast charge transfer times provide promising results for photovoltaic and optoelectronic device applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Numerical Simulation and Experimental Comparison of System Analysis Module 1D Mixing Model for Cold Shock Transients in the Gallium Thermal-Hydraulic Mixing Facility

Abstract Liquid metals are being investigated as coolants in many advanced reactor designs because of their high thermal conductivity and effectiveness at high temperatures. However, they often pose challenges to reactor operation and safety because of the complex thermal mixing and stratification in the plenum of pool-type reactor designs. The advanced system analysis code System Analysis Module (SAM) currently under development at Argonne National Laboratory aims to develop and implement thermal mixing models to accurately capture these complex thermal fluid behaviors. In this study, the SAM thermal mixing model was compared against experimental data from the Gallium Thermal-Hydraulic Experiment facility, a scaled liquid metal test facility that uses gallium as a surrogate fluid to investigate the stratification and thermal mixing of low-Prandtl-number fluids in the upper plenum of a liquid metal-cooled reactor. Two cold shock transient cases were used: one with stable stratified flow (Ri = 32) and one with stronger thermal mixing (Ri = 0.5). The resultant temperatures were then compared with the experimental temperatures over the entire plenum to assess the ability of the mixing models to capture the thermal behavior and to better correspond mixing parameters to various flow scenarios. Generally, the zero-dimensional mixing model was more capable of capturing the bulk temperature of the component modeled assuming that an accurate mass flow rate was provided, but it was inherently unable to capture thermal gradients in space. The one-dimensional mixing model was capable of capturing that the thermal gradients provided accurate selection of the mixing coefficients. Further, the temperature at the outlet junction was compared over time for each of the mixing models with the recorded experimental temperature. The implemented mixing models demonstrated the ability to effectively capture the overall thermal behavior for stronger mixing scenarios but struggled with more stably stratified flows. It was found that a system analysis code's covering of the entire range of different operating conditions still remains a challenging task, and it is suggested that further model and closure improvements are necessary to accurately capture complex thermal mixing and stratification phenomena.

stratification↗

Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features.

79 ASTRONOMY AND ASTROPHYSICS↗

Generative diffusion model surrogates for mechanistic agent-based biological models

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

97 MATHEMATICS AND COMPUTING↗

Electrochemical Amyloid β Immunosensor Based on Ti 3 C 2 T x MXene Nanosheets

Alzheimer’s disease (AD) is the most common neurodegenerative disease and a major health-care problem. More than 55 million people in the world, including 6.7 million Americans, are living with AD. As AD is characterized by production and deposition of β-amyloid peptide (Aβ) in the brain, Aβ is considered as a potential biomarker for diagnosing and monitoring the progression of AD. Nanomaterials-based electrochemical immunosensor is one of the most promising methods for early diagnosis and therapeutic of AD, due to its highly selective, sensitive, and label-free advantages. The graphene-like two-dimensional (2D) MXene nanosheets have shown large surface area, good conductivity, biocompatibility, high ion transport, and low diffusion barrier, attracting attention in recent years to use it as the nanomaterial substrate for biosensor fabrication. In this paper, we presented a Ti 3 C 2 T x MXene nanosheets-based electrochemical immunosensor to detect trace Aβ for early diagnosis of AD.

59 BASIC BIOLOGICAL SCIENCES↗