Biokinetic Models Estimating Rapid Diffusion Using a Priori Knowledge of Flow Rates and Compartment Volumes [Slides]
Abstract not provided.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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Results for “Diffusion rate”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Mechanistic nuclear fuel performance modeling of uranium nitride
Uranium mononitride (UN) is a nuclear fuel candidate for advanced reactor designs and an alternative being considered for light water reactors due to its higher thermal conductivity and uranium density than UO 2 . As with any nuclear fuel, swelling and fission gas release are important factors for safety, while also being some of the hardest phenomena to predict with a high degree of confidence. Getting a grasp on the gas swelling behavior and release is crucial to lower the barrier for UN utilization. An accelerated swelling rate at high temperatures observed experimentally, sometimes referred to as “breakaway swelling,” further complicates the prediction of fuel performance of UN. A mechanistic model has been developed using a multiscale approach to describe the intragranular and intergranular fission gas behavior. Lower-length-scale calculations have been employed to inform models of the gas and self-diffusion behavior, resolution rate, and bubble shape. Leveraging previous work on high burnup UO 2 , two populations of intragranular bubbles are considered; small bulk bubbles and larger bubbles located along dislocations. The dislocation bubbles were found to be crucial to the overall swelling behavior, and the breakaway swelling transition was associated with the transition in the gas atom diffusion mechanism from an irradiation-induced athermal diffusion regime at lower temperatures to an intrinsic thermal equilibrium regime at higher temperatures, accelerating the growth of the dislocation bubbles. Similarly, the threshold for fission gas release was associated with the grain boundary vacancy diffusivity surpassing the gas atom diffusivity at sufficiently high temperatures, allowing the over-pressurized grain boundary bubble to grow in size and interconnect. Using thermo-mechanical models with the fission gas model, two integral fuel pin assessment cases were simulated. Finally, this work demonstrates the ability of a multiscale approach to accelerate the understanding of advanced fuel forms when experimental data is limited.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Creep deformation and cavitation in an additively manufactured Al-8.6Cu-0.4Mn-0.9Zr (wt%) alloy
Creep deformation and cavitation were investigated at 300 ºC in both tension and compression for an additively manufactured Al-8.6Cu-0.5Mn-0.9Zr (wt%) alloy in the as-fabricated state and after various aging treatments (aging at 300 °C/200 h or 350 °C/24 h and overaging at 400 °C/200 h). Creep mechanisms at 300 °C were determined by relating the measured creep response to corresponding microstructural and X-ray computed tomography observations. In compression, alloys in the as-fabricated and two aging conditions exhibited similarly high creep resistance. Overaging (400 °C/200 h) led to substantial coarsening of intragranular θ-Al2Cu precipitates and an expected drop in their Orowan strengthening contribution. In tension, minimum strain rates comparable to those in compression were obtained at any given stress; however, upon accumulation of some plastic strain in the matrix, creep cavities started to form, leading to accelerated tertiary stage creep deformation and rupture. Cavitation occurred exclusively along melt pool boundaries due to locally enhanced diffusion enabled by (i) large grain-boundary area in adjacent fine-grained zones and (ii) localization of creep strain in nearby heat-affected zones. Although cavity growth was initially diffusion-controlled, its rate was determined by matrix creep rate, consistent with constrained cavity growth mechanisms. This study reveals how microstructural complexities induced by the additive manufacturing process affect the creep and cavitation behavior of Al-Cu-Mn-Zr alloys. The underlying creep and cavitation mechanisms uncovered in this study point to pathways that improve the high-temperature properties of additively manufactured alloys.
36 MATERIALS SCIENCE↗
Phase-field modeling of solid-state metathesis reactions with the charge neutrality constraint
In this work, we present a phase-field model that captures the evolution of ionic concentrations and phase fractions during solid-state metathesis (SSM) reactions where diffusion limits the rate of transformation. The evolution of the mole fraction of each ion is obtained via governing equations that describe the reduction of a free energy, which includes an energy landscape with local minima located at compositions corresponding to stable products. We utilized two Lagrange multipliers to impose constraints of electroneutrality as well as on the sum of mole fractions, which were then eliminated to derive set of two partial differential equations that describe the dynamics of the mole fraction evolution. From these governing equations, the expressions for effective mobilities for the cations and the anions were obtained. We first study the effect of mobilities of ions on the reaction kinetics, using a simple model considering the ions with an identical absolute value of charge numbers. The simulation results show that the overall characteristic mobility, defined as the sum of the two effective ionic mobilities, provides an excellent measure of the rate at which reaction progresses and that the ratio of the effective mobilities of the anions and the cations signifies the manner by which the reaction progresses. We then generalize the model to consider ions with different charge numbers and tuned the mobility of ions based on their diffusion coefficients reported in the literature and experimental data from a thin-film experiment for the synthesis of FeS 2 to demonstrate the capability of the model to predict the phase evolution during SSM reactions. In particular, the simulation predicts nonplanar phase evolution, which is recently observed in thin-film reactions for the synthesis of FeS 2 via transmission electron microscopy. The approach can serve as a basis for models for phase transformations in other multiphase ionic mixtures, such as in all-solid-state batteries and in ionic liquids.
36 MATERIALS SCIENCE↗
Accelerated molecular dynamics simulations of dislocation climb in nickel
We report the mechanical behavior of materials operating under high temperatures is strongly influenced by creep mechanisms such as dislocation climb, which is controlled by the diffusion of vacancies. However, atomistic simulations of these mechanisms have traditionally been impractical due to the long time scales required. To overcome these time scale challenges, we use Parallel Trajectory Splicing (ParSplice), an accelerated molecular dynamics method, to simulate dislocation climb in nickel. We focus on modeling the activity of a vacancy near a jog on an edge dislocation in order to observe vacancy pipe diffusion and vacancy absorption at the jog. From rigorously constructed trajectories encompassing more than 2000 vacancy absorption events over a simulation time of more than 4μs at 900 K, a comprehensive sampling of available atomistic mechanisms is collated and analyzed further with molecular statics calculations. We estimate average rates for pipe diffusion and vacancy absorption into the jog using data from the dynamic and static calculations, finding very good agreement. Our results strongly suggest that the dominant mechanism for vacancy absorption by jogs is via biased diffusion to the dislocation core followed by fast pipe diffusion to the jog.
36 MATERIALS SCIENCE↗
Kinetic model describing self-limiting CO 2 diffusion in supported amine adsorbents
A reaction–diffusion shrinking core model describing the decay in diffusivity of supported amine sorbents upon CO 2 sorption under both simulated direct air capture and point source capture conditions is described. The decay in CO 2 diffusivity is associated with crosslinking in the aminopolymer samples and general pore blockage in the amino-silane derived samples, which occurs as CO 2 is adsorbed. The model is used to extract four kinetic parameters that govern the CO 2 uptake kinetics and working capacity: an apparent reaction rate constant, an initial effective diffusivity, and two dimensionless decay parameters. Ideally, an initially reaction limited system would allow for direct determination of the intrinsic reaction rate constant; however, sorption experiments suggest mass transfer resistances related to gas mixing, external boundary layers and intraparticle diffusion are present. Reaction rate constants are determined and agree well with theoretical values predicted with the Eyring equation parameterized using density functional theory energies from literature sources. The kinetic performance is expressed as the average effective diffusivity as a function of average conversion, which can be correlated to the dispersion of sorption sites on the support and the morphology of the active sorbent phase. Four supports are impregnated or grafted with amines, SBA-15, single-walled zeolite nanotubes (ZNT), Syloid SiO 2 , and γ-Al 2 O 3 . Due to its pore structure, γ-Al 2 O 3 supported amines can be modeled at the μm scale or at the nm scale, where the shell balance is on the μm-sized macroporous particle aggregate or on the nm-sized amine film on the surface of the Al 2 O 3 nanoparticles, which comprise the spherical particle aggregates. Faster diffusion rates are maintained under 400 ppm rather than 10% CO 2 due to a slower reaction rate giving a slower decay in diffusivity. In conclusion, this work provides a first principles kinetic analysis of CO 2 sorption where previous models are semi-empirical and use arbitrary kinetic parameters.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mass transfer coefficients, kL, and air-CO2 ingassing rates in 3.4 m2 and 1-acre raceway ponds.
The US DOE 2016 Billion Ton Update (Langholtz 2016) projected that CO2 in flue-gas from power plants or similar sources would limit U.S. algal biofuel potential to under 5 billion gallons gasoline equivalent (gge) per year, due to lack of sufficient land, water and other requirements near such flue gas sources. 2nd generation carbon capture technologies are proposed to overcome this constraint (Davis 2017), and could expand this resource potential nearly 10-fold, assuming CO2 costs near the future, 2025 DOE NETL, goal of $40/metric ton CO2. An alternative pathway is the direct uptake of air CO2 into the algal ponds through an increase in the air-CO2 transfer rate resulting from the reaction of CO2 with hydroxide ions. However, such a ‘chemical enhancement’ in mass transfer depends on a high pH in the culture, and, by extension, the ability of microalgae to maintain high rates of carbon assimilation under such conditions. Abiotic experiments characterized the air-CO2 mass-transfer rate in 1-acre and 3.4 m2 raceway ponds as a function of pH, with rates approaching 10 g C/m2-day at pH 12 in the brackish water tested, equivalent to a biomass productivity potential of near 20 g AFDW/m2-day (@ 0.47 g C/g AFDW). The mass transfer rate was found to be nearly independent of the mass-transfer coefficient, indicating that at turbulence levels achievable in typical raceway ponds, the system is reaction rate, rather than diffusion limited. In biotic trials with an unknown green microalga, biomass productivity in ponds receiving CO2 from only direct surface air-CO2 exchange averaged 5 +/- 0.5 g AFDW/m2-day in August - September central California conditions, about half-that of experimental controls fertilized with supplemental CO2. pH in the air-only treatment reached a maximum of 10.5, supporting a model predicted air-CO2 ingassing rate between 2-3 g C/m2-day, consistent with the observed biomass carbon assimilation rate. Results suggest that unique alkaliphilic strains are required if reliant on direct in-pond air-CO2 transfer for algal inorganic carbon supply, and that reaching an economically viable biomass productivity will require strains that thrive at pH 11 and above.
09 BIOMASS FUELS↗
PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction
Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.
Johnson, Matthew Sean [Sandia National Laboratorie↗
Dynamics of deuterium retention and desorption from plasma-facing materials in fusion reactor-relevant conditions
Hydrogen isotopes retention and desorption during and after discharges in fusion devices are still not well understood due to the complex device conditions and limitations of in-situ diagnostics and measurements. Here, we simulated well-diagnosed recent experiments at the DIII-D facility to benchmark our ITMC-DYN integrated package of modeling deuterium diffusion, retention, and desorption during and after D discharge irradiation. Modeling results were compared with detail experimental data of D desorption fluxes for various irradiation conditions. We predicted the temporal evolution of free and trapped D distribution in tungsten (W) plasma-facing material (PFM). Effects of key parameters namely diffusion coefficient, recombination rate, trapping energies against different defect types, were examined in these simulations. Existing experimental data of these parameters in literature varies significantly which makes it harder to identify key mechanisms and physics responsible for hydrogen isotope retention and desorption. The purpose of this work is to accurately simulate recent well-diagnosed reactor experiments given the uncertainties in such parameters and identify mechanisms responsible for the retention and desorption. We implemented the best identified diffusion, recombination, and trapping parameters in ITMC-DYN package that integrate both various collisional and thermal processes. We predicted, for example, that sample cooling between discharges in DIII-D operations can significantly affect the spatial distribution of trapped D in W under reactor irradiation conditions. Correct prediction of desorption spectra from samples irradiated during 10 DIII-D discharges showed that up to 35% of D can be retained in high binding energy defects such as vacancy clusters or voids.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Non-universal stellar initial mass functions: large uncertainties in star formation rates at z ≈ 2–4 and other astrophysical probes
ABSTRACT We explore the assumption, widely used in many astrophysical calculations, that the stellar initial mass function (IMF) is universal across all galaxies. By considering both a canonical broken-power-law IMF and a non-universal IMF, we are able to compare the effect of different IMFs on multiple observables and derived quantities in astrophysics. Specifically, we consider a non-universal IMF that varies as a function of the local star formation rate, and explore the effects on the star formation rate density (SFRD), the extragalactic background light, the supernova (both core-collapse and thermonuclear) rates, and the diffuse supernova neutrino background. Our most interesting result is that our adopted varying IMF leads to much greater uncertainty on the SFRD at $z \approx 2-4$ than is usually assumed. Indeed, we find an SFRD (inferred using observed galaxy luminosity distributions) that is a factor of $\gtrsim 3$ lower than canonical results obtained using a universal IMF. Secondly, the non-universal IMF we explore implies a reduction in the supernova core-collapse rate of a factor of $\sim 2$, compared against a universal IMF. The other potential tracers are only slightly affected by changes to the properties of the IMF. We find that currently available data do not provide a clear preference for universal or non-universal IMF. However, improvements to measurements of the star formation rate and core-collapse supernova rate at redshifts $z \gtrsim 2$ may offer the best prospects for discernment.
79 ASTRONOMY AND ASTROPHYSICS↗
Understanding and controlling water-organic co-transport in amorphous microporous materials
The transport of molecules in microporous material is a significant and active area of research in separation applications. The movement of vapor/liquid molecules in a high-loading condition in microporous spaces is especially challenging to interrogate. This project aims to understand and control the transport of complex water and organic solvent mixtures in varying structures of microporous carbon molecular sieve (CMS) and activated carbon membranes. Modeling of transport behavior in such carbonaceous samples using computational modeling is difficult due to their amorphous structure. Therefore, this research intends to study the transport mechanism of water-organic mixtures by experimentally revealing fundamental transport properties, such as guest sorption amounts as well as diffusion and permeation rates. Considering the different size and guest-host affinity of water and organic solvent molecules, the objective of this research is to understand the structural conditions within the ultramicropores and micropores that generate different types of molecular transport and selection mechanisms within such complex systems. Carbonaceous materials are developed using tailored pyrolysis techniques to pyrolyze polymeric precursors, including polyvinylidene chloride (PVDC),polyvinylidene fluoride (PVDF), polymer of intrinsic microporosity (PIM) -1, and fully aromatic polyamide. The initial stage of the research will focus on the microscopic diffusion and sorption studies of pure component water, xylene isomers, and n,n- dimethylformamide within various carbons. Additional structural investigation on CMS, such as gas physisorption and neutron scattering studies, will be executed to gain deeper insight into these structure-transport relationships. Scanning electron microscopy and X-ray photoelectron microscopy are also used to further characterize these materials. The research will progress to probe mutual diffusion of complex water-organic mixtures within various CMS microstructures. Competitive sorption and permeation studies of the binary mixtures in different microstructural CMS will be followed. The obtained binary mixture transport parameters were used to predict binary water-organic mixtures transport.
36 MATERIALS SCIENCE↗
Impact of grain boundary and surface diffusion on predicted fission gas bubble behavior and release in UO 2 fuel
In this work, we quantify the impact of grain boundary (GB) and surface diffusion on fission gas bubble evolution and fission gas release in UO 2 nuclear fuel using simulations with a hybrid phase field/cluster dynamics model. Here, we begin with a comprehensive literature review of uranium vacancy and xenon atom diffusivity in UO 2 through the bulk, along GBs, and along surfaces. In our model we represent fast GB and surface diffusion using a heterogeneous diffusivity that is a function of the order parameters that represent bubbles and grains. We find that the GB diffusivity directly impacts the rate of gas release via GB transport, and that the GB diffusivity is likely below 104 times the lower value from Olander and van Uffelen. We also find that the surface diffusivity impacts bubble coalescence and mobility, and that the bubble surface diffusivity is likely below 10 -4 times the value from Zhou and Olander.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Statistical distributions for transient transport
Here, this paper introduces the use of statistical distributions based on transport differential equations for clear distinction of transport modes within transient kinetic experiments. More specifically, novel techniques are developed for the transient data obtained through the Temporal Analysis of Products (TAP) reactor and are applicable to experiments where pulse response into a gas flow is used. The methodology allows distinguishing between two domains of diffusion transport in heterogeneous catalytic systems, i.e., Knudsen and non-Knudsen diffusion, using statistical fingerprints, and finding the transition domain. Two distribution parameters were obtained that directly result in coefficients that correspond to the concentration and the rate of transport. Using a linear relationship between the rate and concentration coefficients, Knudsen diffusion is revealed when the rate of transport is constant and non-Knudsen diffusion is confirmed when the rate of transport coefficient is a function of the concentration coefficient. As a result, accurate transport information can be extracted from experimental data even in the presence of comprising instrument drift or noise particularly when analyzing higher pressure pulse responses with complex transport. This enables more direct investigation of experiments influenced by gas-phase reactions.
TAP reactor↗
1-D coupled surface flow and transport equations revisited via the physics-informed neural network approach
The de Saint-Venant equation (SVE) and advection–diffusion equation (ADE) are commonly employed to solve solute transport problems in surface water. In this work, we propose a mesh-free method based on the physics-informed neural network (PINN) to solve the one dimensional (1-D) SVE, ADE, and the coupled SVE and ADE (SVE-ADE) under various initial and boundary conditions. The PINN model extends the architecture of deep neural networks (DNNs) with implementation of loss function, which are additionally subject to constraints imposed by the physical laws of SVE and ADE, along with their initial and boundary conditions. In such a manner, PINNs can be quickly steered to the true solution while obeying the physical laws. The results of PINN model are compared with the analytical and/or numerical solutions under various conditions to investigate its accuracy and efficiency in solving the SVE, ADE, and SVE-ADE. Our results indicate PINN can accurately simulate the shock wave morphology and avoid numerical dissipation in unsteady flow condition. The PINN method outweighed traditional numerical methods in several aspects, including its ability to function with small amounts of data, no grid discretization, and random selection of sampling points, etc. Additionally, the PINN method is also suitable for solving inverse problems with sparse and noisy data. With 1% noise and 2000 initial and boundary condition points (N u ), the errors of the estimated flow rate (v) and diffusion coefficient (D) are 0.003% and 0.105%, respectively, which indicate the accuracy and robustness of the proposed method. Finally, our results indicate the capability and robustness of the proposed PINN methodology for solving multi-physics problems, irrespective of the presence of sparse and noisy data in the training dataset.
54 ENVIRONMENTAL SCIENCES↗
Study of the water dynamics near hydrophilic, nanostructured CuO surfaces by quasielastic and inelastic neutron scattering
We have used quasielastic and inelastic neutron scattering to investigate the structure, dynamics, and phase transitions of water interacting with superhydrophilic CuO surfaces that not only possess a strong affinity for water but also a “grass-like” topography that is rough on both micro- and nanoscales. Here, we report quasielastic neutron scattering (QENS) measurements on two samples differing in water content at five temperatures below 280 K. The QENS spectra show water undergoing two different types of diffusive motion near the CuO surfaces: a “slow” translational diffusion occurring on a nanosecond time scale and a faster rotational motion. Further from the surfaces, there is “fast” translational diffusion comparable in rate to that of bulk supercooled water and the rotational motion occurring in the interfacial water. Analysis of the QENS spectra supports wetting of water to the CuO blades as seen in electron microscopy images. In addition, we observe an anomalous temperature dependence of the QENS spectra on cooling from 270 to 230 K with features consistent with a liquid–liquid phase transition. We suggest that the solvent-like properties of the coexisting bulk-like water in our CuO samples are a significant factor in determining the temperature dependence of the interfacial water’s dynamics. Our results are compared with those obtained from two well-studied substrate classes: (1) silicas that contain ordered cylindrical nanopores but have weaker hydrophilicity and (2) nanoparticles of other transition-metal oxides, such as TiO 2 , which share the strong hydrophilicity of our samples but lack their porosity.
36 MATERIALS SCIENCE↗
Speciation mapping of the oxidation layer on aged uranium dioxide using scanning transmission x-ray spectromicroscopy
In this study, UO 2 was aged in humid air and prepared as a thin section using a focused ion beam (FIB) instrument. The specimen was measured using synchrotron radiation spectromicroscopy techniques at the Beamline 11.0.2 STXM end station of the Advanced Light Source (ALS). Non-negative matrix factorization (NMF) methods were used to identify and map three component x-ray absorption near-edge structure (XANES) spectra in the oxygen K-edge data, revealing a surface layer of U 4 O 9 with a thickness of 206 ± 21 nm, and the bulk of the sample remaining as UO 2 . Uranium N 4,5 -edge XANES spectromicroscopy supports these results. The diffusion-controlled parabolic rate constant for UO 2 oxidation to U 4 O 9 was calculated from the observed layer thickness and compared to literature values. Complementary transmission electron microscopy (TEM) was used to image the sample and identify the phases present in various regions, confirming the STXM results.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Solvent-Dependent Dynamics of Cellulose Nanocrystals in Process-Relevant Flow Fields
Flow-assisted alignment of anisotropic nanoparticles is a promising route for the bottom-up assembly of advanced materials with tunable properties. While aligning processes could be optimized by controlling factors such as solvent viscosity, flow deformation, and the structure of the particles themselves, it is necessary to understand the relationship between these factors and their effect on the final orientation. In this study, we investigated the flow of surface-charged cellulose nanocrystals (CNCs) with the shape of a rigid rod dispersed in water and propylene glycol (PG) in an isotropic tactoid state. In situ scanning small-angle X-ray scattering (SAXS) and rheo-optical flow-stop experiments were used to quantify the dynamics, orientation, and structure of the assigned system at the nanometer scale. The effects of both shear and extensional flow fields were revealed in a single experiment by using a flow-focusing channel geometry, which was used as a model flow for nanomaterial assembly. Due to the higher solvent viscosity, CNCs in PG showed much slower Brownian dynamics than CNCs in water and thus could be aligned at lower deformation rates. Moreover, CNCs in PG also formed a characteristic tactoid structure but with less ordering than CNCs in water owing to weaker electrostatic interactions. The results indicate that CNCs in water stay assembled in the mesoscale structure at moderate deformation rates but are broken up at higher flow rates, enhancing rotary diffusion and leading to lower overall alignment. Albeit being a study of cellulose nanoparticles, the fundamental interplay between imposed flow fields, Brownian motion, and electrostatic interactions likely apply to many other anisotropic colloidal systems.
36 MATERIALS SCIENCE↗