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At least 1,387 records · Page 77

Chemical Imaging of Atmospheric Particles

Airborne particles are very dynamic and highly reactive components of the Earth’s atmosphere. Their high surface area and water content provide a unique reaction environment for multiphase chemistry that continually modifies particle composition and properties that consequently impact air quality as well as concentrations of gas-phase species. By absorbing and scattering solar and terrestrial radiation, particles directly influence the planet’s radiative balance. Their indirect effects include modifying the nucleation, lifetime, and physical properties of clouds. Due to the sensitivity of the atmospheric environment to all these variables, fundamental studies of chemical transformations of atmospheric particles, their sources, continuously evolving composition, and physical properties are of highest research priority. Accurate descriptions of particles and their effects in the atmosphere require comprehensive information not only on the particle-type populations and their size distributions and concentrations, but also on the diversity and the spatial heterogeneity of chemical components within individual particles. Developments and applications of modern chemical imaging approaches for off-line characterization of atmospheric particles have been at the forefront of modern experimental studies and have resulted in a transformative impact in atmospheric chemistry and physics. This Account presents a synopsis of recent advances in chemical imaging of atmospheric particles collected on substrates during field and laboratory experiments. The unique advantage of chemical imaging methods is that they simultaneously provide two analytical measurements: imaging of particles to assess variability in their individual sizes and morphology, as well as particle specific speciation of their composition and spatial heterogeneity of different chemical components within individual particles. We also highlight analytical chemistry approaches that enable chemical imaging of particles with different levels of elemental and molecular specificity, including applications of multimodal methodologies where the same or similar groups of particles are probed by two or more complementary techniques. These approaches provide unique experimental insights on the nature and sources of particles, understanding their physical properties, atmospheric reactivity, and transformations. Chemical imaging data provide unique experimental input for atmospheric models that simulate aging and changes in particle-type populations, internal composition, and their associated optical and cloud forming properties. Here, we highlight applications of chemical imaging in selected recent studies, discuss their existing limitations, and forecast future research directions for this area.

54 ENVIRONMENTAL SCIENCES↗

Thermodynamic description of molten salt systems: KCl-LiCl-NaCl and KCl-LiCl-NdCl 3

The mixture of KCl and LiCl has been used as electrolyte in the electrorefining process to recover uranium from used nuclear fuels due to the low melting point. However, lanthanides and sodium in the reactor waste continuously dissolve into it and thus alter its thermodynamic properties. To understand the thermodynamic behavior evolution of the electrolyte with the accumulation of impurities, thermodynamic modeling for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and four constituent binary systems in the entire composition space was performed using the CALPHAD (CALculation of PHAse Diagrams) approach. The ionic liquid was described by the two-sublattice model, where neutral species were introduced to consider short-range ordering (SRO) within the melt, whereas the solid solution was modeled based on the Compound Energy Formalism. Literature data on phase equilibria and thermochemical properties were critically evaluated and used during the optimization of thermodynamic parameters for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and their subsystems. The calculated phase diagrams and mixing enthalpies are in good agreement with the experimental data. The thermodynamic modeling for the KCl-LiCl-NdCl 3 system was carried out for the first time. To fill the gap in experimental measurement, enthalpy of mixing for the KCl-LiCl-NdCl 3 melt was estimated using the surrounded-ion model. These data then served as critical inputs for thermodynamic optimization. Furthermore, the present study can provide insights into thermodynamic property evolution of the electrolyte and solubility limit of various impurities during the electrorefining process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

A three-dimensional interconnected polymer/ceramic composite as a thin film solid electrolyte

In this work we introduce an approach to fabricate a solid composite electrolyte film that is thin, ionically conductive, and mechanically robust with good potential for manufacturability, in the application of lithium metal batteries. First a doped-lithium aluminum titanium phosphate ceramic thin film with thickness of ~25 μm is formed by aqueous spray coating, a scalable process. The film is partially sintered to form a three-dimensionally interconnected structure with a dense backbone. It is then backfilled with a crosslinkable poly(ethylene oxide) (PEO)-based polymer electrolyte. We report the composite has very high ceramic loading of 77 wt% (61 vol%) and an ionic conductivity of 3.5 × 10 -5 S/cm at 20 °C with an activation energy of 0.43 eV. The main ion transport pathway is through the ceramic network, predicted by modelling and verified by experiments. Owing to the interconnected structure of the ceramic, the composite electrolyte exhibits much improved mechanical strength.

25 ENERGY STORAGE↗

Economic competitiveness of pultruded fiber composites for wind turbine applications

Pultrusion manufacturing of fiber reinforced polymers has been shown to yield some of the highest mechanical properties for unidirectional composites, having a high degree of fiber alignment with consistent performance. Pultrusions offer a low-cost manufacturing approach for producing unidirectional composites with a constant cross-section and are used in many applications, including spar caps of wind turbine blades. However, as an intermediate processing step for wind blades, the additional cost of manufacturing pultrusions must be accompanied by sufficient increases in mechanical performance and system benefits. Wind turbine blades are manufactured using vacuum-assisted resin transfer molding with infused unidirectional fiberglass or carbon pultrusions for the spar cap. Infused fiberglass composites are among the most cost-effective structural materials available and replacing this material in the cost-driven wind industry has proven challenging, where infused fiberglass spar caps are still the predominant material system in use. To evaluate alternative material systems in a pultruded composite form, it is necessary to understand the costs for this additional manufacturing step which are shown to add 33%–55% on top of the material costs. A pultrusion cost model has been developed and used to quantify cost sensitivities to various processing parameters. The mechanical performance for pultruded composites is improved versus resin-infusion manufacturing with a 17% increase in design strength at a constant fiber volume fraction, but also enables higher achievable fiber volume fractions. The cost-specific mechanical performance is compared as a function of processing parameters for pultruded composites to identify the opportunities for alternative material and manufacturing approaches for wind turbine spar caps. Finally, four materials are compared in a representative wind turbine blade model to assess the performance of pultruded carbon fiber systems and pultruded fiberglass relative to infused fiberglass, where the pultruded systems produce lower weight blades with various cost distinctions.

42 ENGINEERING↗

The Contribution of Proton-Donor pKa on Reactivity Profiles of [FeFe]-hydrogenases

The [FeFe]-hydrogenases are enzymes that catalyze the reversible activation of H 2 coupled to the reduction–oxidation of electron carriers. Members of the different taxonomic groups of [FeFe]-hydrogenases display a wide range of preference, or bias, for H 2 oxidation or H 2 production reactions, despite sharing a common catalytic cofactor, or H-cluster. Identifying the properties that control reactivity remains an active area of investigation, and models have emerged that include diversity in the catalytic site coordination environments and compositions of electron transfer chains. The kinetics of proton-coupled electron transfer at the H-cluster might be expected to be a point of control of reactivity. To test this hypothesis, systematic changes were made to the conserved cysteine residue that functions in proton exchange with the H-cluster in the three model enzymes: CaI, CpII, and CrHydA1. CaI and CpII both employ electron transfer accessory clusters but differ in bias, whereas CrHydA1 lacks accessory clusters having only the H-cluster. Changing from cysteine to either serine (more basic) or aspartate (more acidic) modifies the sidechain pKa and thus the barrier for the proton exchange step. The reaction rates for H 2 oxidation or H 2 evolution were surveyed and measured for model [FeFe]-hydrogenases, and the results show that the initial proton-transfer step in [FeFe]-hydrogenase is tightly coupled to the control of reactivity; a change from cysteine to more basic serine favored H 2 oxidation in all enzymes, whereas a change to more acidic aspartate caused a shift in preference toward H 2 evolution. Overall, the changes in reactivity profiles were profound, spanning 10 5 in ratio of the H 2 oxidation-to-H 2 evolution rates. The fact that the change in reactivity follows a common trend implies that the effect of changing the proton-transfer residue pKa may also be framed as an effect on the scaling relationship between the H-cluster di(thiolmethyl)amine (DTMA) ligand pKa and E m values of the H-cluster. Experimental observations that support this relationship, and how it relates to catalytic function in [FeFe]-hydrogenases, are discussed.

59 BASIC BIOLOGICAL SCIENCES↗

Warpage-Resistant, Under-Extrusion-Free, High-Surface-Quality Additive Manufacturing Process for Polyethylene-Based Composite Radiation Shielding Material

Polyethylene (PE) is one of the best shielding materials for primary space radiation due to its high hydrogen content. For effective secondary neutron shielding, boron-rich fillers are incorporated to enhance performance. The semicrystalline nature and high thermal expansion coefficient of PE impede its adoption for in situ additive manufacture in space via the fused deposition modeling (FDM) 3D printing. Here, we developed an optimized PE blend to mitigate the effects of under-extrusion and warpage. Guided by studies on extrusion and warpage, we developed an optimal set of printing parameters for the proposed PE blend. The optimum PE blend─both in its pure form and when doped with fillers─has been tested on different FDM printers. The printed structures exhibit high and uniform density, smooth surfaces, no warpage, and competitive mechanical properties. The FDM-printed plates demonstrate efficient shielding from thermal neutrons, predicted via modeling and confirmed experimentally using extended Q-range small-angle neutron scattering.

additive manufacturing↗

Heat transfer optimization of uo 2 -mo fuel using genetic algorithms

Two genetic algorithm (GA) methods were applied to thermal finite element models to optimize the heat transfer efficacy of a UO 2 -Mo composite fuel pellet with typical pressurized water reactor fuel geometry. Mo additions to UO 2 have been shown to increase the thermal conductivity, thus reducing centerline temperatures and temperature gradients. Previous studies evaluated uniformly dispersed Mo or continuous Mo internal geometries (e.g., fins, plates, discs) that were selected using engineering intuition. The current study uses two different implementations of the same GA to optimize Mo placement and minimize the fuel temperature with the only constraint being a maximum 10% Mo volume fraction. One approach superimposed Mo line elements onto the monolithic UO 2 pellet model, and the other converted entire UO 2 volume elements to Mo. The former method generated 1D heat transfer connections between nodes, whereas the latter method allowed for the formation of 3D structures. Features of the optimal fuel design produced by the GAs included dispersed Mo near the centerline that shifted the peak fuel temperature outward by 0.6 mm, Mo chains in the high-heat-flux region in the mid-to-outer radial zone, and a large continuous structure that spanned the full radius and height of the pellet and accounted for 87.7 % of the total Mo in the pellet. Analysis of this design indicates that the optimal Mo configuration is a balance between creating continuous heat transfer pathways and optimally dispersing Mo to minimize the heat transfer distance through UO 2 . This architecture ultimately produced an effective thermal conductivity of 11.3 W/m·K under the assumed boundary conditions. This result is higher than any previous values from the literature. In conclusion, potential fabrication methods and challenges are discussed in addition to the implications on fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Surface Chemistry and Reactions for Bimetallic Au Catalysis (Final Report)

Gold-based catalysts offer tremendous opportunities for developing new technologies for selective oxidation reactions that directly use gas-phase oxygen as a benign reagent and produce either no byproducts or water as the only byproduct. These processes are urgently needed and critical for achieving the sustainable production of chemicals. While recent studies demonstrate that the catalytic properties of gold can be significantly enhanced by addition of other metals, the development of these catalysts with sufficient performance for commercialization is hindered by a lack of fundamental understanding of how the catalytic properties of gold can be controlled and tailored. This project addressed this need by synergistically combining experimental and computational studies to establish fundamental composition-structure-activity relationships for gold-based catalysts for several industrially important reactions. Results from surface spectroscopy, atomic imaging and thermal desorption experiments were combined with molecular models and reaction mechanisms from quantum chemical calculations to link observable reaction rates to the structure and composition of surfaces at the molecular level. The results were used to develop improved catalyst formulations and identify optimized reaction conditions for gold-based catalysts. For broader impacts, the developed methodology was extended to other metallic catalysts, such as silver, platinum-molybdenum and nickel-tin, for improved catalytic efficiency and sustainable production of specialty and commodity chemicals.

36 MATERIALS SCIENCE↗

Volumetric analysis and mesh generation of real and artificial microstructural geometries

Producing a viable finite element mesh of realistic microstructural structural geometry is a critical step in analyzing the thermo-mechanical behavior of complex multi-material composites. Advancements in imaging technology such as micro computed tomography have allowed modelers to access high resolution mesoscale geometries for direct numerical simulation. However, converting from voxel based 3D images to usable finite element meshes has been challenging. A robust method including algorithms and software scripts for generating finite element meshes from 3D imaged microstructures is presented in this paper. It includes a routine for inserting cohesive elements around material interfaces to enable modeling of interface properties including delamination and damage. The algorithms and procedures presented in this method leverage currently available software packages for processing surface based geometry into volume based meshes. In addition to converting real geometry from physical imaging systems, algorithms for producing numerically generated and statistically equivalent microstructural geometry are also included. These artificial microstructures can be a valuable resource for modelers when physical specimens do not exist or are limited in quantity: Method establishes a workflow from voxel data to viable finite element mesh including interface information, and Includes a method for synthetic geometry generation based on metrics from real microstructures.

36 MATERIALS SCIENCE↗

LiNi0.8Mn0.1Co0.1O2 Thin Films Prepared by Polymer-Assisted Deposition for the Study of Cathode-Electrolyte Interphases in Lithium-Ion Batteries

High-nickel layered oxide cathodes such as LiNi0.8Mn0.1Co0.1O2 (NMC811) are critical for next-generation lithium-ion batteries (LIBs) due to their superior energy density and reduced reliance on cobalt. However, many Ni-rich cathodes suffer from rapid capacity fade and structural instability originating from complex interfacial reactions at the cathode-electrolyte interface. Traditional composite electrodes exhibit degradation mechanisms that are challenging to quantitatively understand due to additives, including binders and carbon black. In this study, we demonstrate a new synthesis approach for binder- and additive-free NMC811 thin films using polymer-assisted deposition (PAD). PAD-NMC811 are model thin-film cathodes for investigating interfacial phenomena that can be obscured in composite cathodes. Structural and chemical characterization by X-ray diffraction, soft X-ray absorption spectroscopy, and atomic force microscopy show that PAD-NMC811 films possess high phase purity, crystallinity, chemical homogeneity, and morphological uniformity. Electrochemical analyses using cyclic voltammetry and galvanostatic cycling revealed electrochemical behavior consistent with that of composite electrodes, along with a moderate capacity fade indicative of cathode-electrolyte interphase (CEI) formation. Our findings illustrate the effectiveness of PAD synthesis of thin films tailored for detailed mechanistic studies, which offer critical insights into CEI evolution and cathode degradation pathways.

25 ENERGY STORAGE↗

Nodal Capacity Expansion Modeling with ReEDS: A Case Study of the RTS-GMLC Test System

Test systems play an important role in power systems analysis and are used extensively for a wide range of purposes like reliability analysis, production cost modeling, studying the impacts of load shifting, and many more. These test systems have continued to evolve over the years to keep pace with changing system compositions due to technology and policy changes. In this work, we apply the Regional Energy Deployment System (ReEDS) model, a large-scale capacity expansion model (CEM), on the nodal RTS-GMLC system to perform capacity expansion of the bus-level test system. This work both demonstrates that a large-scale CEM can successfully be applied to nodal systems, and that the CEM can allow a test system to be evolved to meet desired criteria. The process allowed the coupling of CEM data not available in the test system (such as capital costs) with test system data to provide plausible system evolutions in line with scenario specifications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A machine learning model for predicting the minimum miscibility pressure of CO 2 and crude oil system based on a support vector machine algorithm approach

CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.

02 PETROLEUM↗

Thermally driven surface phase separation in intermetallic alloys

Intermetallic compounds are widely recognized for their high-temperature phase stability and resistance to composition and structural changes. However, we reveal athermally activated bulk-to-surface mass exchange mechanism that drives surface phase separation, resulting in the formation of surface precipitates with distinct composition andstructure from the bulk matrix. Using the archetypal β-NiAl system, we show that asymmetries in vacancy formation energies between Ni and Al atoms induce preferential Ni segregation to the surface, forming Ni-rich γ'-Ni 3 Al precipitates. By integrating in-situ electron microscopy, synchrotron X-ray absorption spectroscopy and first-principles computational modeling, we establish a direct mechanistic connection between bulk thermal defect dynamics, surface compositional evolution, and phase segregation behavior. This bulk-surface coupling mechanism can be a driver of surface phase separation in multicomponent alloys under thermal stress. In conclusion, these results refine the thermodynamic boundaries of intermetallic stability and provide insights into managingthe performance and durability of intermetallic alloys for demanding high-temperature applications.

36 MATERIALS SCIENCE↗

Rapid Construction of Validated Chemistry Models for Advanced Biofuels (Co-Optima Final Report)

Computer methods were developed and demonstrated that make it possible to predict the combustion and pyrolysis chemistry of proposed biofuels, even before the biofuels are ever manufactured or experimentally tested. The methods are based on first-principles calculations of reaction rates and equilibria. A large number of complicated calculations are required, so we developed a computer workflow that automates many of the steps. With the chemical structure of the biofuel and the system conditions (temperature, pressure, compositions, etc.) as the input, the computer methods can construct a detailed kinetic model with refined parameters without intense human intervention. The automation of model construction makes it practical for a single engineer to make the predictions in a reasonable time period. The accuracy of the computer predictions was tested by measuring the time-profiles of various reaction intermediates (carbon monoxide, carbon dioxide, water, formaldehyde, methane, and ethylene) formed as the biofuels pyrolyze or ignite using sophisticated laser shock tube methods. Several biofuels, including methyl propyl ether, cyclopentanone, butyl acetate isomers, etc., were carefully studied to demonstrate the validity of the proposed computational and experimental methodology. This research project opens up the possibility for assessing some proposed biofuels on the computer without expensive experimental campaigns. The new computer methods are open source and well-documented, allowing a knowledgeable modeler to easily access the developed methods and implement them in their work routine. Moreover, they are also convenient and powerful, promising for developing the fuel chemistry models needed for use in engine simulators, facilitating co-optimization of new engine designs with new fuels.

09 BIOMASS FUELS↗

The Path towards Plasma Facing Components: A Review of State-of-the-art in W-Based Refractory High-Entropy Alloys

Developing advanced materials for plasma-facing components (PFCs) in fusion reactors is a crucial aspect for achieving sustained energy production. Tungsten (W) - based refractory high-entropy alloys (RHEAs) have emerged as promising candidates due to their superior radiation tolerance and high-temperature strength. This review paper will focus on recent advancements in W-based RHEA research, particularly emphasizing the key role of modelling using machine learning (ML) in the stage of discovery by predicting properties for each composition and expediting the identification of optimal RHEA compositions with desired properties. Additionally, the application of additive manufacturing (AM) techniques for fabricating W-based RHEAs is explored, highlighting their advantages for rapid prototyping and multi-compositional sample production in a high throughput manner. The review critically evaluates the current understanding of mechanical properties relevant to PFC applications, including high-temperature strength and ductility. Furthermore, the radiation tolerance of W-based RHEAs under irradiated conditions is discussed. Finally, the validity of current AM-manufactured W-based RHEAs as PFC materials is assessed, and key challenges and opportunities for future research are identified. This review aims to provide a comprehensive overview of W-based RHEAs for fusion applications and their potential to guide the development and validation of advanced refractory high entropy alloys.

Hatler, Caleb [University of Wisconsin-Madison]↗

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗

Cation uptake by fine aggregate in hardened cement mortar and its effect on electrical properties

Electrical resistivity tests can be used to evaluate the transport properties of concrete and provide a durability assessment. However, the electrical resistivity is largely dependent on the pore solution composition and recent work suggests that some aggregates have the capacity for cation uptake. This study first aims to provide further evidence for adsorption of cations on aggregate surfaces, without formation of reaction products (e.g. alkali-silica reaction). Secondly, hardened mortar samples were prepared using a fine aggregate with a high alkali affinity and a non-reactive fine aggregate as a control. The electrical resistivity of mortars was measured, and the pore solution of these mortars was obtained through high-pressure extraction. The effect of aggregate moisture dilution on the pore solution was decoupled by using a pore partitioning model. The results indicate that aggregate minerology can influence the pore solution composition through cation uptake. Specific minerals of minor quantity, like biotite, may be responsible for cation exchange. While adsorbed cations strongly affected pore solution and formation factor measurement, the bulk resistivity measurements on hardened mortar were only marginally influenced. Research on other implications of similar aggregate interactions with pore solutions are an intriguing area for future research.

Alkali-aggregate reaction↗

A physics-based model of cladding wastage layer formation rate

In this report, lower length scale simulations to inform an engineering-scale model of fuel-cladding chemical interaction (FCCI) conducted under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY22 are described. An overall strategy for the implementation of the BISON model is described, and the past and current lower length scale work on FCCI formation is put into the context of this overall strategy. Density functional theory and kinetic Monte Carlo simulations are used to determine the diffusion coefficient of neodymium in iron. Density functional theory calculations are also used to parameterize a parabolic approximation for the dependence of free energy on composition for the intermetallic compound Fe 17 Nd 2 . A phase-field model of the Fe-Nd system was developed that includes the random solid solution body-centered cubic phase and the intermetallic Fe 17 Nd 2 . The model was used to simulate growth of the intermetallic layer in a diffusion couple and the results were compared to experiment. The model’s prediction of the parabolic growth constant is within 40 % of the experimental value. The model is also used to simulate the formation of a grain boundary attack layer that is observed in experiment. The simulations show enhanced Nd concentration along the grain boundaries, but do not clearly show formation of a Fe 17 Nd 2 layer

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗