Sas-temper: Software for fitting small-angle scattering data that provides automated reproducibility characterization
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97 MATHEMATICS AND COMPUTING↗
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Shortwave Array Spectroradiometer-Hemispheric (SAS-He): design and evaluation
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54 ENVIRONMENTAL SCIENCES↗
Enhancing the inherent catalytic activity and stability of TiO 2 supported Pt single-atoms at CeO x –TiO 2 interfaces
Single-atoms (SAs) with atomically coordinated reaction centers are considered the next generation of catalysts that can exhibit exceptional catalytic efficiency. However, the general concern about thermodynamic vulnerabilities of SAs questions their practical value. Moreover, whether the inherent catalytic nature of SAs is superior compared with that of larger nanoparticles is still under debate. Here, we address two controversies by a comparative study using two catalysts: Pt/TiO 2 and Pt/CeO x –TiO 2 . Based on a hierarchical study of density functional theory, time-resolved catalysis performance test, in situ infrared spectroscopy, and operando X-ray absorption spectroscopy, we could unveil the catalytic nature of Pt-SAs and their stability. By utilizing the heterogeneous interface formed between TiO 2 supporting particles and CeO x clusters formed on the surface of TiO 2 , we preferentially synthesized Pt-SAs pinned at the CeO x –TiO 2 interfaces on CeO x –TiO 2 hybrid-oxide supports. The strong electronic coupling between the Pt-SAs and the Ce ions at the CeO x –TiO 2 interfaces enhanced the catalytic activity toward CO oxidation of Pt-SAs and improved the long-term stability under CO oxidation conditions. The CO oxidation activity of Pt-SAs stabilized at CeO x –TiO 2 was improved by 13.5 times at 200 °C compared with the Pt-SAs on TiO 2 . The results present how to easily improve the activity and stability of Pt-SAs using a simple interface control method. Moreover, we demonstrate that the catalytic activity and the stability of Pt-SAs can be monitored through the chemical state of the interfaces. Finally, our study provides comprehensive understanding about the catalytic nature as well as a novel strategy toward applications of Pt-SAs, enabling sustainable use of Pt in heterogeneous catalysts.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Design Principles of Single Atoms on Carbons for Lithium–Sulfur Batteries
The study of lithium-sulfur (Li-S) batteries has generated various rationally designed cathodes and modified separators. However, the shuttle of soluble lithium polysulfides (LiPSs) and the sluggish kinetics of transformation of LiPSs to Li2S2/Li2S still hinder the achievement of long-life cycling and high-rate of Li-S batteries. Even though several kinds of nanocatalysts have been studied, the catalytic effects for Li-S batteries are not ideal. Single atoms (SAs) with high surface free energy are found to serve as both anchoring and electrocatalytic centers for LiPSs. Furthermore, atomically dispersed metal catalyst on carbon provide conductive and flexible host for dielectric sulfur. Moreover, SAs as the uniform lithiophilic sites can reduce the nucleation overpotential and ensure uniformly plating of Li. In this review, the latest strategies for preparation of SAs supported on carbons are provided on the application of Li-S batteries, including cathode, modified separator and Li metal anode. In addition, it also discusses how SAs with high catalytic activity can help build better Li-S batteries and the design principles for SAs. Finally, the future directions of SAs in energy storage applications is proposed.
25 ENERGY STORAGE↗
Predicting tungsten erosion and leakage properties for the new V-shaped small angle slot divertor in DIII-D
Impurity transport modeling of the new tungsten (W)-coated, V-shaped small angle slot (SAS) divertor in the DIII-D tokamak was conducted using the SOLPS-ITER plasma edge code package and the DIVIMP impurity tracking code. The inboard baffle of the current SAS divertor will be shifted closer to the outboard baffle, creating a V-corner at the slot vertex. In addition, the outboard baffle will be coated with 10–15 μm of W for experiments studying high-Z sourcing and leakage in a closed divertor. Modeling of the 'SAS-VW' divertor predicts that these changes to the inner baffle will reduce W gross erosion by 40× relative to the existing SAS divertor when the outer strike point (OSP) is at the V-corner and the ion B × ∇B drift is towards the divertor, driven primarily by significant cooling near the slot vertex. Most W erosion in SAS-VW is expected to occur near the slot entrance, which may pose a higher risk to core contamination than W eroded deeper in the slot. Furthermore, adding a new sheath-based prompt redeposition model increases the sensitivity of redeposition estimates to near-target plasma conditions and may provide more accurate predictions of net erosion. Moving the OSP outboard from the slot vertex ~4 cm onto the W-coated region yields a 40× increase in the gross erosion rate and a 50% decrease in the core leakage fraction. Thus slight variations in strike point location may counteract the potential benefits of the tightly-baffled V slot on minimizing erosion. This impurity transport modeling provides useful guidance for future experiments on the SAS-VW divertor focused on high-Z erosion/redeposition, scrape-off layer transport, and core leakage.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Materials Data on AsS by Materials Project
SAs crystallizes in the triclinic P1 space group. The structure is one-dimensional and consists of two arsenic compounds molecules; one As4S3 cluster; one SAs cluster; one SSAs cluster; one As3S4 ribbon oriented in the (1, 0, 0) direction; and two SAs ribbons oriented in the (1, 0, 0) direction. In the As4S3 cluster, there are four inequivalent As2+ sites. In the first As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.26 Å) and one longer (2.27 Å) As–S bond lengths. In the second As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.28 Å. In the third As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.28 Å) and one longer (2.29 Å) As–S bond lengths. In the fourth As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.26 Å. There are three inequivalent S2- sites. In the first S2- site, S2- is bonded in a 3-coordinate geometry to two As2+ atoms. In the second S2- site, S2- is bonded in a bent 120 degrees geometry to two As2+ atoms. In the third S2- site, S2- is bonded in a bent 120 degrees geometry to two As2+ atoms. In the SAs cluster, there are two inequivalent As2+ sites. In the first As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.31 Å. In the second As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.32 Å. There are two inequivalent S2- sites. In the first S2- site, S2- is bonded in a distorted water-like geometry to one As2+ and one S2- atom. The S–S bond length is 2.09 Å. In the second S2- site, S2- is bonded in a distorted water-like geometry to one As2+ and one S2- atom. In the SSAs cluster, there are two inequivalent As2+ sites. In the first As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.30 Å) and one longer (2.35 Å) As–S bond lengths. In the second As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.29 Å) and one longer (2.35 Å) As–S bond lengths. There are four inequivalent S2- sites. In the first S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the second S2- site, S2- is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.05 Å) and one longer (2.06 Å) S–S bond lengths. In the third S2- site, S2- is bonded in a distorted water-like geometry to one As2+ and one S2- atom. In the fourth S2- site, S2- is bonded in a distorted water-like geometry to one As2+ and one S2- atom. In the As3S4 ribbon, there are three inequivalent As2+ sites. In the first As2+ site, As2+ is bonded in a trigonal non-coplanar geometry to three S2- atoms. There are a spread of As–S bond distances ranging from 2.25–2.36 Å. In the second As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.26 Å) and one longer (2.33 Å) As–S bond lengths. In the third As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.28 Å. There are four inequivalent S2- sites. In the first S2- site, S2- is bonded in a distorted water-like geometry to one As2+ and one S2- atom. The S–S bond length is 2.04 Å. In the second S2- site, S2- is bonded in a distorted water-like geometry to one As2+ and one S2- atom. In the third S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the fourth S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In one of the SAs ribbons, there are five inequivalent As2+ sites. In the first As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.26 Å) and one longer (2.30 Å) As–S bond lengths. In the second As2+ site, As2+ is bonded in a 3-coordinate geometry to three S2- atoms. There are a spread of As–S bond distances ranging from 2.11–2.86 Å. In the third As2+ site, As2+ is bonded in a distorted T-shaped geometry to three S2- atoms. There are a spread of As–S bond distances ranging from 2.28–2.32 Å. In the fourth As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.31 Å. In the fifth As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.28 Å. There are five inequivalent S2- sites. In the first S2- site, S2- is bonded in a single-bond geometry to one As2+ atom. In the second S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the third S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the fourth S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the fifth S2- site, S2- is bonded in a 3-coordinate geometry to three As2+ atoms. In one of the SAs ribbons, there are six inequivalent As2+ sites. In the first As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.31 Å. In the second As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.27 Å) and one longer (2.34 Å) As–S bond lengths. In the third As2+ site, As2+ is bonded in a distorted trigonal non-coplanar geometry to three S2- atoms. There are a spread of As–S bond distances ranging from 2.28–2.33 Å. In the fourth As2+ site, As2+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.27 Å. In the fifth As2+ site, As2+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.25 Å) and one longer (2.27 Å) As–S bond lengths. In the sixth As2+ site, As2+ is bonded in a trigonal non-coplanar geometry to three S2- atoms. There are a spread of As–S bond distances ranging from 2.28–2.30 Å. There are six inequivalent S2- sites. In the first S2- site, S2- is bonded in a bent 120 degrees geometry to two As2+ atoms. In the second S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the third S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the fourth S2- site, S2- is bonded in a bent 120 degrees geometry to two As2+ atoms. In the fifth S2- site, S2- is bonded in a water-like geometry to two As2+ atoms. In the sixth S2- site, S2- is bonded in a water-like geometry to two As2+ atoms.
36 MATERIALS SCIENCE↗
Capability Enhancements for System-level thermal Hydraulic Modeling of Lead Fast Reactors
This project has focused on the use, assessment, and development of the SAS4A/SASSYS-1 (SAS) safety analysis software. Although SAS was originally intended as a safety analysis tool for Liquid Metal cooled Fast Reactors (LMFRs), which includes both Sodium Fast Reactors (SFRs) and Lead Fast Reactors (LFRs), the vast majority of its recent development and customization has been tailored to SFRs. In general, enhancements that are made to the software for SFRs are applicable to LFRs, however, the fuel composition and corrosive nature of lead requires careful consideration when performing safety analysis of an LFR. In this project an emphasis was placed on closing gaps that are associated with modeling LFRs using SAS. The principal objective of the project was to enhance the ability of SAS as a licensing tool for LFRs. This objective was to be accomplished through three tasks: 1) Enhance the ability of SAS to couple with external software; 2) Improve the underlying physics models in SAS, with priority given to models that are highly relevant to LFRs; 3) Extend the Verification and Validation (V&V) basis of the software
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Small-angle X-ray and neutron scattering
Small-angle scattering (SAS) is a technique that is able to probe the structural organization of matter and quantify its response to changes in external conditions. X-ray and neutron scattering profiles measured from bulk materials or materials deposited at surfaces arise from nanostructural inhomogeneities of electron or nuclear density. Furthermore, the analysis of SAS data from coherent scattering events provides information about the length scale distributions of material components. Samples for SAS studies may be prepared in situ or under near-native conditions and the measurements performed at various temperatures, pressures, flows, shears or stresses, and in a time-resolved fashion. In this Primer, we provide an overview of SAS, summarizing the types of instrument used, approaches for data collection and calibration, available data analysis methods, structural information that can be obtained using the method, and data depositories, standards and formats. Recent applications of SAS in structural biology and the soft-matter and hard-matter sciences are also discussed.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Numerical assessment of the new V-shape small-angle slot divertor on DIII-D
The small-angle slot (SAS) divertor of the DIII-D tokamak, and its upcoming upgrade, the V-shape small-angle slot (SAS-V) divertor, are numerically investigated using the SOLPS-ITER code package, including the effect of particle drifts, for a range of plasma density, heating power, strike point position in the slot, and for both magnetic field directions. The simulations show that the electron temperature near the strike point is reduced in SAS-V compared to SAS, for both magnetic field directions, such that SAS-V achieves divertor detachment at a lower value of the outboard mid-plane separatrix electron density. The detachment threshold is lower because the V-shape focuses recycling neutrals on the V-end, densifying and cooling the plasma in the slot. At sufficiently high density, the V-shape also reduces the radial gradient of the temperature profile at the target, which in turns reduces the radial electric field and the E x B drift velocities, further densifying and cooling the plasma in the slot and leading to detachment. The V-shape effect, however, is reduced for higher heating power. With more heating power, the detachment density increases, reducing the ionization mean free path of recycled neutrals, which therefore become less sensitive to target shape changes. Furthermore, this suggests that in a fusion reactor, where the heating power is high, optimization of the divertor target shape needs to be combined with other strategies to lower the detachment density, such as in-slot injection of low-Z impurities.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Materials Data on As24S25 by Materials Project
AsAs15S17(SAs)8 crystallizes in the triclinic P1 space group. The structure is one-dimensional and consists of one arsenic molecule; three SAs clusters; and one As15S17 ribbon oriented in the (1, 0, 0) direction. In one of the SAs clusters, there are two inequivalent As+2.08+ sites. In the first As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.30 Å. In the second As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.30 Å. There are two inequivalent S2- sites. In the first S2- site, S2- is bonded in a distorted L-shaped geometry to one As+2.08+ and one S2- atom. The S–S bond length is 2.09 Å. In the second S2- site, S2- is bonded in a distorted water-like geometry to one As+2.08+ and one S2- atom. In one of the SAs clusters, there are three inequivalent As+2.08+ sites. In the first As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.31 Å. In the second As+2.08+ site, As+2.08+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.12 Å) and one longer (2.37 Å) As–S bond lengths. In the third As+2.08+ site, As+2.08+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.26 Å) and one longer (2.32 Å) As–S bond lengths. There are three inequivalent S2- sites. In the first S2- site, S2- is bonded in a single-bond geometry to one As+2.08+ atom. In the second S2- site, S2- is bonded in a water-like geometry to two As+2.08+ atoms. In the third S2- site, S2- is bonded in an L-shaped geometry to two As+2.08+ atoms. In one of the SAs clusters, there are three inequivalent As+2.08+ sites. In the first As+2.08+ site, As+2.08+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.32 Å) and one longer (2.33 Å) As–S bond lengths. In the second As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.26 Å. In the third As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.35 Å. There are three inequivalent S2- sites. In the first S2- site, S2- is bonded in a water-like geometry to two As+2.08+ atoms. In the second S2- site, S2- is bonded in a 2-coordinate geometry to one As+2.08+ and one S2- atom. The S–S bond length is 2.09 Å. In the third S2- site, S2- is bonded in a distorted water-like geometry to one As+2.08+ and one S2- atom. In the As15S17 ribbon, there are fifteen inequivalent As+2.08+ sites. In the first As+2.08+ site, As+2.08+ is bonded in an L-shaped geometry to two S2- atoms. There are one shorter (2.25 Å) and one longer (2.43 Å) As–S bond lengths. In the second As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.25 Å. In the third As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.32 Å. In the fourth As+2.08+ site, As+2.08+ is bonded in a distorted trigonal non-coplanar geometry to three S2- atoms. There are a spread of As–S bond distances ranging from 2.07–3.00 Å. In the fifth As+2.08+ site, As+2.08+ is bonded in an L-shaped geometry to two S2- atoms. There are one shorter (2.28 Å) and one longer (2.33 Å) As–S bond lengths. In the sixth As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.29 Å. In the seventh As+2.08+ site, As+2.08+ is bonded in an L-shaped geometry to two S2- atoms. There are one shorter (2.32 Å) and one longer (2.71 Å) As–S bond lengths. In the eighth As+2.08+ site, As+2.08+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.29 Å) and one longer (2.32 Å) As–S bond lengths. In the ninth As+2.08+ site, As+2.08+ is bonded in a distorted water-like geometry to two S2- atoms. There are one shorter (2.30 Å) and one longer (2.38 Å) As–S bond lengths. In the tenth As+2.08+ site, As+2.08+ is bonded in a 3-coordinate geometry to three S2- atoms. There are a spread of As–S bond distances ranging from 2.23–2.81 Å. In the eleventh As+2.08+ site, As+2.08+ is bonded in a distorted L-shaped geometry to two S2- atoms. There are one shorter (2.39 Å) and one longer (2.45 Å) As–S bond lengths. In the twelfth As+2.08+ site, As+2.08+ is bonded in a distorted L-shaped geometry to two S2- atoms. There are one shorter (2.18 Å) and one longer (2.54 Å) As–S bond lengths. In the thirteenth As+2.08+ site, As+2.08+ is bonded in a single-bond geometry to one S2- atom. The As–S bond length is 2.27 Å. In the fourteenth As+2.08+ site, As+2.08+ is bonded in an L-shaped geometry to two S2- atoms. There are one shorter (2.32 Å) and one longer (2.38 Å) As–S bond lengths. In the fifteenth As+2.08+ site, As+2.08+ is bonded in a water-like geometry to two S2- atoms. There are one shorter (2.18 Å) and one longer (2.29 Å) As–S bond lengths. There are seventeen inequivalent S2- sites. In the first S2- site, S2- is bonded in a distorted water-like geometry to one As+2.08+ and one S2- atom. The S–S bond length is 2.03 Å. In the second S2- site, S2- is bonded in an L-shaped geometry to one As+2.08+ and one S2- atom. The S–S bond length is 2.03 Å. In the third S2- site, S2- is bonded in a bent 120 degrees geometry to two As+2.08+ atoms. In the fourth S2- site, S2- is bonded in a 1-coordinate geometry to one As+2.08+ and one S2- atom. The S–S bond length is 2.10 Å. In the fifth S2- site, S2- is bonded in a 2-coordinate geometry to two As+2.08+ atoms. In the sixth S2- site, S2- is bonded in a distorted L-shaped geometry to two As+2.08+ atoms. In the seventh S2- site, S2- is bonded in an L-shaped geometry to two As+2.08+ atoms. In the eighth S2- site, S2- is bonded in a 1-coordinate geometry to two As+2.08+ and one S2- atom. The S–S bond length is 2.07 Å. In the ninth S2- site, S2- is bonded in a distorted bent 120 degrees geometry to one As+2.08+ and one S2- atom. In the tenth S2- site, S2- is bonded in a single-bond geometry to one As+2.08+ atom. In the eleventh S2- site, S2- is bonded in a bent 120 degrees geometry to two As+2.08+ atoms. In the twelfth S2- site, S2- is bonded in a 2-coordinate geometry to two As+2.08+ atoms. In the thirteenth S2- site, S2- is bonded in a distorted trigonal non-coplanar geometry to three As+2.08+ atoms. In the fourteenth S2- site, S2- is bonded in a 2-coordinate geometry to two As+2.08+ atoms. In the fifteenth S2- site, S2- is bonded in a distorted water-like geometry to one As+2.08+ and one S2- atom. In the sixteenth S2- site, S2- is bonded in a 3-coordinate geometry to two As+2.08+ and one S2- atom. In the seventeenth S2- site, S2- is bonded in a distorted bent 120 degrees geometry to one As+2.08+ and one S2- atom.
36 MATERIALS SCIENCE↗
Comparison of excess free energy at an interface according to the applied interpolation scheme for elasticity: A phase-field method
Phase-field modeling is an effective simulation technique for modeling microstructure evolution of elastically anisotropic systems. To introduce the elastic energy contribution in a phase field model, an interpolation scheme is used to define the mechanical properties within the phases and across the continuous interface. Several existing interpolation schemes introduce a potential excess elastic energy at the interface, which undesirable effect on microstructure evolution needs to be evaluated. In this study, we focused on three interpolation schemes including Khachaturyan’ scheme (KHS), Voigt–Taylor’s scheme (VTS), and Steinbach–Apel’s scheme (SAS). Comparisons of these schemes’ performances were performed in three configuration types using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework: bi-crystal, isotropic particle-matrix and anisotropic particle-matrix. The contribution of excess elastic energy on the interface energy as a function of interface width and the computational time to steady-state were evaluated in these three configurations. SAS introduces the lowest excess elastic energy contribution and the VTS has the biggest contribution amongst the considered schemes. Moreover, when modeling precipitation in an anisotropic elastic material, the SAS approach seems to predict more physical convex shapes during growth, making it preferable to KHS and VTS. Finally, as currently implemented, SAS requires the largest computational time and KHS requires the smallest time to reach steady-state amongst the considered schemes.
36 MATERIALS SCIENCE↗
Dense Ru single-atoms integrated with sulfoacids for cellulose valorization to isosorbide
Metal-acid bifunctional catalysts are the unity of two opposites (metal-acid repelling) for cellulosic biomass valorization to isosorbide. It is challenging to improve the selectivity of metal hydrogenation catalysts and their synergism with acids for catalytic hydrolysis and dehydration to achieve substantial isosorbide. Herein, dense Ru single-atoms (10.1 wt% of Ru SAs) are anchored on sulfoacid-functionalized hollow mesoporous carbon shells, designed by assembling silica and 8-hydroxyquinoline-modified chitosan (HQ-CTS) through in situ Stöber templating strategy before pyrolysis and acid treatment. Based on X-ray absorption fine structure and computational modeling results, the structure of Ru SAs is determined as RuN 4 , which is more selective for a transitional glucose hydrogenation to sorbitol than Ru001 of Ru clusters. A lower-energy barrier of 1.21 (0.72) eV is delivered over RuN 4 (Ru001) for glucose hydrogenation (isomerization). These Ru SAs are integrated with sulfoacids (SO 3 H) but resistent against acids, rendering enhanced isosorbide yield in water as compared to Ru clusters, via a one-pot cascade reaction under harsh conditions (220 °C, 6 MPa H 2 ). The elaborately fabricated dense Ru SAs and sulfoacids, achieved by varying the addition time of HQ-CTS during the in situ Stöber templating process, improve the synergism of glucose hydrogenation with cellulose hydrolysis and sorbitol dehydration. In conclusion, this study provides a new idea for rational design of high-performance metal-acid bifunctional catalysts toward one-pot conversion of cellulose to isosorbide.
36 MATERIALS SCIENCE↗
Tungsten–carbon surface evolution and erosion modeling for a small angle slot divertor in DIII-D
Abstract We modeled tungsten–carbon mixed surface evolution, sputtering erosion, and transport for the tungsten coated region of a small angle slot (SAS) divertor design for the DIII-D tokamak. This divertor concept aims to achieve a closed slot dissipative plasma to minimize heat load and surface erosion, and to study high- Z material performance. Our advanced simulations use coupled ITMC-DYN material mixing/response and 3D full kinetic REDEP/WBC erosion/redeposition code packages, with divertor plasma solution from the SOLPS-ITER package with 4 MW power input. The SAS design geometry and resulting in-slot plasma parameters cause significant differences in predicted sputter/transport from a conventional divertor. For 2% C/D incident plasma ratio, an equilibrium mixed C/W surface is attained at ∼30 s of discharge, from wall sputtered carbon transported to the 10 cm long tungsten divertor region. Tungsten remains exposed to the plasma, but the evolved surface composition varies with different C/D ratios. Tungsten is primarily sputtered from the mixed surface by impinging carbon ions in the +1 to +4 charge states, with some self-sputtering. Redeposition of sputtered tungsten to the divertor is significant, ∼80% along the higher plasma temperature attached plasma SAS entrance region, but this is less than the typically near-unity values for a conventional divertor. Plasma-incident carbon is highly backscattered (∼50%) from the mixed surface, with little redeposition (<10%); this helps maintain tungsten in the surface sputter zone. Carbon is mainly sputtered from the mixed surface by D + ions, also with low redeposition (∼10%–30%). Finally, the modeling shows non-zero but low sputtered tungsten current from the divertor to the core plasma direction. These results appear favorable for effective testing of a tungsten-containing SAS divertor in DIII-D, and extrapolation of mixed-material evolution/response findings to the analogous low- Z /high- Z , Be/W, ITER plasma facing system.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Bayesian estimations of orientation distribution functions from small-angle scattering enable direct prediction of mechanical stress in anisotropic materials
Properties of soft materials are influenced by their anisotropic structuring under nonequilibrium fields. Although anisotropic structure-property relationships have been extensively explored theoretically, comparison to experiments requires determination of the microstructural orientation probability distribution function (OPDF) of microstructural elements. Small angle scattering (SAS) measurements encode information about the OPDF, but tools to navigate this connection are incomplete. Here, we develop and validate an explicit framework to link arbitrary OPDFs to SAS measurements. Specifically, we propose, validate, and apply a method, maximum a posteriori scattering inference (MAPSI), whereby the OPDF may be obtained from SAS measurements using a Bayesian estimation method. Using this method, we obtain estimates of the full 3D OPDF for two model semidilute fd-virus (rodlike) dispersions at concentrations that are approximately equal to and twice the overlap concentration. From the OPDF, we calculate its second and fourth moments and compare these to predictions for a dilute suspension of rigid rods and to a recent theory for semidilute suspensions. Finally, we use both the theoretical and measured moments to calculate the stress, both for dilute and semidilute suspensions. These predictions are not only compared to each other, but also to measured values of the shear stress, and point to new insights into the behavior of suspensions of highly elongated particles in the transition between dilute and semidilute behavior. We also use this new framework to provide perspective on the connection between scalar parameterizations of scattering and the OPDF that have frequently been used in the past. Here, the new tools developed in this work provide an unprecedented path toward experimental validation of dynamical theories of rodlike colloids and polymers, and for measurement of nonequilibrium structures and stresses of other complex fluids and soft materials with SAS.
36 MATERIALS SCIENCE↗
Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques
A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.
97 MATHEMATICS AND COMPUTING↗
A Semi-Analytical Solution Approach for Solving Constant-Coefficient First-Order Partial Differential Equations
Simulation and control of many dynamic systems involve solving partial differential equations (PDE). This letter proposes a semi-analytical solution (SAS) approach for fast and high-quality solution of first-order PDEs. The region of interest of the studied PDE is divided into a grid, and an SAS is derived for each grid cell in the form of the multivariate polynomials, of which the coefficients are identified using initial value and boundary value conditions. The solutions are solved in a “time-stepping” manner, i.e. within one time step, the coefficients of the SAS are identified and the initial value of the next time step is evaluated. This approach achieves a significantly larger grid cell than the widely used finite difference method, and thus enhances the computational efficiency significantly. Furthermore, the simulation result on the natural gas pipeline model demonstrates the advantages of SAS in accuracy and computational efficiency.
97 MATHEMATICS AND COMPUTING↗
Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation
With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. Here, the selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.
97 MATHEMATICS AND COMPUTING↗
Small Angle Scattering Data Analysis Assisted by Machine Learning Methods
Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗