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Dynamical properties of a room temperature ionic liquid: Using molecular dynamics simulations to implement a dynamic ion cage model [plus supplemental information]
The transport behavior of ionic liquids (ILs) is pivotal for a variety of applications, especially when ILs are used as electrolytes. Many aspects of the transport dynamics of ILs remain to be understood. Here, a common ionic liquid, 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (BmimNTf 2 ), was studied with molecular dynamics simulations. The results show that BmimNTf 2 displays typical structural relaxation, subdiffusive behavior, and a breakdown of the Stokes-Einstein diffusion relation as in glass-forming liquids. In addition, the simulations show that the translational dynamics, reorientation dynamics, and structural relaxation dynamics are well described by the Vogel-Fulcher-Tammann equation like fragile glass forming liquids. Building on previous work that employed ion cage models, it was found that the diffusion dynamics of the cations and anions were well described by a hopping process random walk where the step time is the ion cage lifetime obtained from the cage correlation function. Detailed analysis of the ion cage structures indicated that the electrostatic potential energy of the ion cage dominates the diffusion dynamics of the caged ion. The ion orientational relaxation dynamics showed that ion reorientation is a necessary step for ion cage restructuring. The dynamic ion cage model description of ion diffusion presented here may have implications for designing ILs to control their transport behavior.
Diffusion model approach to simulating electron-proton scattering events
Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024
Mathematical model and calculation algorithm of micro and meso levels of separation process of gaseous mixtures in molecular sieves
In the given article, the main thermodynamic aspects of the issue of modeling diffusion transfer in molecular sieves have been formulated. Dissipation function is used as a basic notion. The differential equation, connecting volume flow with the change of the concentration of catchable component has been derived. As a result, the expression for changing the concentration of the catchable component and the coefficient of membrane detecting has been received. As well, the system approach to describing the process of gases separation in ultra porous membranes has been realized and micro and meso-levels of mathematical modeling have been distinguished. The non-ideality of the shared system is primarily taken into consideration at the micro-level and the departure from the diffusion law of Fick has been taken into account. The calculation method of selectivity considering fractal structure of membranes has been developed at the meso level. The calculation algorithm and its software implementation have been suggested.
Analysis of ducted fuel injection at high-pressure transcritical conditions using large-eddy simulations
Ducted fuel injection (DFI) is a proposed fuel injection concept for achieving substantial reductions in emissions. In this concept, the fuel is injected through a coannular duct, resulting in increased fuel-air mixing and minimized formation of soot and other unwanted combustion products. Apart from comprehensive experimental investigations on DFI, so far computational studies have been limited to single-point Reynolds-averaged Navier Stokes simulations. Therefore, the objective of this work is to complement these studies by performing large-eddy simulations using a diffuse-interface method to examine the physical mechanisms and combustion processes of DFI, specifically focusing on the mixing process and the effect of fuel-ducting on combustion and pollutant emissions. To this end, finite-rate chemistry simulations are performed of the DFI configuration corresponding to the Engine Combustion Network Spray A injector at transcritical conditions (n-dodecane fuel, 60 bar pressure and 1000 K temperature chamber conditions). A two-equation soot model is employed for the qualitative analysis of soot emissions. Direct comparisons of averaged and instantaneous flow field results with the Spray A configuration are performed to assess the effect of DFI on the first- and second-stage ignition and soot formation. Compared to the free-spray condition, the results show that the DFI case exhibits a combination of (i) increased mass flow rate and entrained air, (ii) larger pressure drop magnitude and flow velocity, and (iii) a closer-to-stoichiometric mixture composition (both globally and locally), each of which is conjectured to contribute toward reduced soot production.
Hygro-thermo-mechanical modeling of partially saturated air-entrained concrete containing dissolved salt and exposed to freeze-thaw cycles
Highlights: • A model for cyclic freezing of air-entrained concrete containing salt is proposed. • The freeze-thaw hysteresis, also including the effect of dissolved salt, is modeled. • The diffusive and convective flux of salt is coupled to the freeze-thaw processes. • The freeze-thaw response of air-entrained concrete containing salt is reproduced. • Liquid absorption from reservoirs increases with an increased concentration of salt. In cold regions, understanding the freeze-thaw behavior of air-entrained concrete is important for designing durable structures and assessing the remaining service life of existing structures. This study presents a hygro-thermo-mechanical multiphase model that describes the cyclic freeze-thaw behavior of partially saturated air-entrained concrete containing dissolved salt. An equilibrium and a non-equilibrium approach are adopted to model the ice formation, including the freeze-thaw hysteresis, inside the porous network. The model also considers the diffusive and convective transport of the dissolved salt coupled to the freeze-thaw processes. Two examples are presented to verify and highlight the capabilities of the model. The first example shows that the model is capable of reproducing the experimentally observed mechanical response of specimens containing NaC1-solutions of different concentrations. In the second example, a larger absorption of liquid from an external reservoir is obtained with an increasing salt concentration in the reservoir, which is consistent with experimental observations.
Laser-Generated Ag Nanoparticles in Mesoporous TiO 2 Films: Formation Processes and Modeling-Based Size Prediction
Controlling the formation of nanoparticles (NPs) by direct laser writing ensures an efficient way of engineering the optical responses of nanocomposite materials through adjusting laser parameters such as power, focusing, and writing speed. The growth of NPs, photo-oxidation, and reduction affect the light absorption by an ensemble of laser-activated NPs. However, those self-consistent processes are not well understood. Previous studies explained the growth or shrinkage of silver NPs by neglecting heat diffusion. It remained unclear, however, how the NP size can be controlled by the laser writing speed. In this work, based on the coupled calculations of size variation, heat diffusion, and light absorption by an ensemble of NPs, we propose a two-dimensional model taking into account the spatial information of size and temperature. The spatial size distribution is revealed to be nonuniform, leading to the transmission inhomogeneity along the laser written lines. This fact is confirmed by the in situ transmission experiments. The performed study also depicts a novel view in which NPs grow ahead of the laser beam center because of the heat diffusion. The nonlinear growth never stops until it exhausts the majority of the free Ag 0 in the matrix, while the amount of Ag 0 by reduction cannot compensate the consumption. After that, the photo-oxidation dominates the process and finally controls the writing speed dependency of NP's size. Finally, the simulations also show that it is not the activation energy of Ag 0 diffusion, but the ionization efficiency affects the final NP size, which helps to understand how to improve the laser processing of differently prepared samples.
Sticky roots--implications of widespread, cryptic, viral infection of plants in natural and managed ecosystems for soil carbon processing in the rhizosphere
Plants strongly influence soil properties through rhizodeposition, in which exudates diffuse from roots, additional secretions are actively released, and root cells are sloughed into the soil. This contribution by plants of carbon compounds belowground is at the core of soil health, water holding capacity, and the soil carbon storage that pulls carbon dioxide out of the atmosphere. Once in soil, organic matter can bind with minerals such as iron hydroxides, where it can be protected from microbial attack for millenia, preserving very large terrestrial soil carbon pools. However, those same compounds contributed by roots to soil may also destabilize the long-term protective associations of SOM with minerals, making that soil organic matter (SOM) more vulnerable to microbial attack and decomposition. Plant roots thus influence both the buildup and breakdown of soil carbon pools. DOE’s E3SM Land Model (ELM) includes a representation of soil carbon storage on minerals, but the potential vulnerability of SOM–mineral associations to effects of rhizodeposition is not yet represented in ELM. To begin testing for this effect of rhizodeposition on soil carbon storage and decomposition, we worked to develop a novel approach during this TES Exploratory project DE-SC0019142 – we harnessed the power of plant viral infection. We examined whether plant virus infection can serve as a tool to intensify rhizodeposition at the root surface, and therefore possibly intensify mobilization of SOM from minerals making it visible to our analytical techniques. Viral infection is widespread in terrestrial ecosystems; 25-70% of plants have virus infection, yet the influence of such infection on root traits and terrestrial soil carbon dynamics remains largely unexplored. We used two plant hosts: the annual Avena sativa (oats) and the genetically tractable, model grass Brachypodium distachyon. These grasses were infected with the broad host range virus Barley Yellow Dwarf Virus (BYDV) via aphids (Rhopalosiphum padi). BYDV infects at least 150 grass species in agricultural and natural ecosystems, and in previous experiments, oats infected with BYDV had roots that were very sticky to the touch, strongly suggesting that infection altered rhizodeposition. We developed this new experimental approach mostly in a one virus (Barley Yellow Dwarf Virus)–one plant (Avena sativa) system. (Several effects of infection in a Brachypodium-BYDV system were similar in nature to effects on Avena sativa, but were more variable.) In the BYDV-Avena system, we developed protocols for consistently infecting target plants (and avoiding infection of control plants) using aphid caging on leaves. We measured that infected plants exhibited reduced photosynthesis, plant (including root) biomass, and root:shoot ratio, as well as simplified root system architecture. We established procedures for sampling the organic compounds carried specifically in phloem (vascular tissue) of leaves and roots, using aphid stylectomy. We used FTICR-MS, Orbitrap GC-MS, and LC-MS/MS to analyze organic compounds in phloem, liquid around roots of plants grown hydroponically, and pore water around roots in soil, and found differences in the compounds in solution bathing roots when infected and uninfected plants were grown hydroponically. Finally, we synthesized isotopically-labeled mineral–organic matter (MAOM) associations in the lab and developed assays using them in solution and in soil. Assays quantified the extent and rate of mineralization of labeled MAOM that was mobilized by functionally distinct rhizodeposits and then attacked by microbes. Two mechanisms for MAOM mobilization emerged, with distinct dynamics. During “direct” mobilization, rhizodeposits such as the strong ligand oxalic acid could drive rapid dissolution of minerals, mobilizing MAOM. During “indirect” mobilization, rhizodeposits such as the simple sugar glucose did not attack minerals directly but instead intensified microbial activity, which led to mobilization via changes in e.g. pH, Eh, and microbial metabolite production (Li et al. 2021). Mechanistic understanding derived from these data and our ongoing experiments using these techniques will inform future development of ELM. Plant roots not only contribute newly fixed organic compounds to soils, but also root activities can drive mineralization of the carbon and nutrients mobilized off minerals via “indirect” or “direct” mechanisms. Using viral infection as a new tool, ongoing combined experimentation and modeling will explore the strength and larger-scale significance of the cascade of processes from rhizodeposition to MAOM mobilization for soil carbon storage and nutrient cycling in terrestrial ecosystems. And if viral infection leads quite generally to “sticky roots”, our perception of the potential importance of prevalent virus infection in terrestrial landscapes will be transformed.
Measuring thermal diffusivity and gap conductance in uranium nitride and Zircaloy relevant for microreactor applications
Heat transfer across nuclear fuels and structural interfaces is an important factor for evaluating the performance of nuclear power systems. Specifically, heat generated as nuclear fuel fissions must be transported through the cladding material and through the reactor to reach the steam turbine for power generation. As new microreactor designs emerge, maximizing the efficiency of this heat transfer process becomes crucial to make them commercially viable. This article examines thermal diffusivity and gap conductance in uranium nitride (UN) fuel and Zircaloy-4 (Zry4) cladding using light flash analysis (LFA). Thermal diffusivity measurements were made on monolithic UN pellets and Zry4 exposed to carbon at peak operating temperatures of microreactors and show that carbon ingress has a minimal effect on thermal diffusivity when compared with identical materials not exposed to carbon. Evaluation of gap conductance at the UN-Zry4 interface was done using one-dimensional two-layer thermal transport models as a function of applied pressure. Here the results show that increasing pressure on the UN-Zry4 interface leads to gains in gap conductance per unit area in fuel-cladding assemblies at microreactor operating temperatures. While many other variables are expected to influence UN-Zry4 interfacial gap conductance (e.g. contact surface roughness, porosity, localized heating, environmental gas pressure), the work offers a demonstration of using a conventional LFA apparatus to determine this parameter at elevated temperatures.
Communication: Memory effects and active Brownian diffusion
A self-propelled artificial microswimmer is often modeled as a ballistic Brownian particle moving with constant speed aligned along one of its axis, but changing direction due to random collisions with the environment. Similarly to thermal noise, its angular randomization is described as a memoryless stochastic process. Here, we speculate that finite-time correlations in the orientational dynamics can affect the swimmer’s diffusivity. To this purpose, we propose and solve two alternative models. In the first one, we simply assume that the environmental fluctuations governing the swimmer’s propulsion are exponentially correlated in time, whereas in the second one, we account for possible damped fluctuations of the propulsion velocity around the swimmer’s axis. The corresponding swimmer’s diffusion constants are predicted to get, respectively, enhanced or suppressed upon increasing the model memory time. Possible consequences of this effect on the interpretation of the experimental data are discussed.
Unlocking bulk and surface oxygen transport properties of mixed oxide-ion and electron conducting membranes with combined oxygen permeation cell and oxygen probe method
Surface exchange kinetics and bulk diffusion of oxygen are of paramount importance to the activity of oxygen electrocatalysis and performance of electrochemical devices such as fuel cell, metal-air batteries, and oxygen separation membranes. Conventional approaches to obtaining these transport properties are often limited to single property under a specific non-operation related condition. Here we use a combined oxygen permeation cell and oxygen probe methodology to simultaneously attain rates of oxygen surface exchange and bulk conductivity/chemical diffusivity of three representative mixed oxide-ion and electron conductors, namely SrCo 0.9 Ta 0.1 O 3-δ (SCT), La 0.6 Sr 0.4 CoO 3-δ (LSC) and La 0.6 Sr 0.4 FeO 3-δ (LSF), operated under a steady-state oxygen flux. The results explicitly show that SCT exhibit the highest oxide-ion conductivity/chemical diffusivity, fastest rates of surface oxygen exchange kinetics, thus promising to be the best oxygen electrocatalyst. Here, we have also mapped out the distribution of oxygen chemical potential gradient across the membranes and applied B-transport number concept to illustrate the rate-limiting steps in the overall oxygen permeation process.
Synthesis of wafer-scale SWCNT forests with remarkably invariant structural properties in a bulk-diffusion-controlled kinetic regime
Robust synthesis of vertically aligned carbon nanotubes (VACNT) at large scale is required to accelerate deployment of numerous cutting-edge devices to emerging commercial applications. To address this need, this work demonstrates that the structural characteristics of single-walled CNTs (SWCNTs) produced in a growth regime dominated by bulk diffusion of the gaseous carbon precursor are remarkably invariant over a broad range of process conditions (precursor concentration increased up to 30-fold, catalyst substrate area from 1 cm 2 to 180 cm 2 , growth pressure from 20 to 790 mbar, and gas flowrates up to 8-fold). A simple growth kinetics model is derived that accounts for both reactant bulk diffusion and competing byproduct formation in the presence of excess hydrogen, which quantitatively reproduces the experimental data in the entire isothermal parameter space. The model enables critical predictions for process scale-up optimization including a potential 6-fold increase in CNT production rate, a ~90% carbon conversion efficiency in select conditions, and elimination of reaction rate decay observed at high pressures by using a hydrogen-free growth environment. This model-guided opportunity to substantially improve synthesis throughput and efficiency, in conjunction with the remarkably invariant CNT properties, is appealing for reliable VACNT device fabrication at both small and industrial scales.
Mapping structures and dynamics with frequency-correlated diffusion exchange
Understanding molecular motion in diffusion-driven complex environments is critical for designing sustainable materials and improving chemical processes. Here, we introduce a multidimensional nuclear magnetic resonance (NMR) method that captures how molecular populations exchange across different dynamic regimes. By extending the modulated gradient spin-echo technique to include frequency-frequency correlations, our approach reveals diffusion pathways that are otherwise obscured in heterogeneous systems. Implemented on a unilateral NMR magnet, the method eliminates gradient pulsing constraints and accesses dynamics in the kilohertz regime. We apply this technique to swelling and acid-catalyzed deconstruction of cross-linked and linear polymers to observe how structural heterogeneity evolves over time. By linking molecular motion to topology and chemical state, we extract physical metrics such as fractal surface dimensionality and reaction wavefront velocity, properties inaccessible with standard diffusion measurements. This work expands the capabilities of NMR for probing soft matter, with implications for polymer recycling and materials design.
Minimizing particle aggregation in Sm 2 Fe 17 N 3 powders: A CaO-assisted reduction-diffusion approach
We report a novel synthesis of Sm 2 Fe 17 N 3 powders using a CaO-assisted reduction-diffusion (RD) approach. CaO plays a crucial role during mechanochemical processing – acting both as a dispersant and a surface coating agent, which helps to prevent agglomeration due to sintering of Sm 2 Fe 17 particles during the RD step. Together with the added dispersant, the CaO by-product formed during RD can be easily removed in the washing step resulting in fewer aggregated Sm 2 Fe 17 N 3 particles and lower oxygen contamination in the final product. The impact of varying CaO amounts was examined, and synthesis conditions were optimized to achieve phase-pure Sm 2 Fe 17 N 3 powders with less aggregation of magnetic particles. The powders synthesized with addition of 1 wt% CaO as dispersant exhibited the highest hard-magnetic properties: a coercivity (H c ) of 10.7 kOe and a maximum energy product ((BH) max ) of 17.3 MGOe. By densifying the Sm 2 Fe 17 N 3 powders using high-pressure spark plasma sintering, a bulk magnet with a (BH) max of 21.1 MGOe with 88 % of theoretical density was produced. In conclusion, reducing aggregation of the Sm 2 Fe 17 N 3 increases coercivity and remanence of these magnets.
The Weakly Nonlinear Magnetorotational Instability in a Local Geometry
The magnetorotational instability (MRI) is a fundamental process of accretion disk physics, but its saturation mechanism remains poorly understood despite considerable theoretical and computational effort. We present a multiple-scales analysis of the non-ideal MRI in the weakly nonlinear regime—that is, when the most unstable MRI mode has a growth rate asymptotically approaching zero from above. Here, we develop our theory in a local, Cartesian channel. Our results confirm the finding by Umurhan et al. that the perturbation amplitude follows a Ginzburg–Landau equation. We further find that the Ginzburg–Landau equation will arise for the local MRI system with shear-periodic boundary conditions, when the effects of ambipolar diffusion are considered. A detailed force balance for the saturated azimuthal velocity and vertical magnetic field demonstrates that, even when diffusive effects are important, the bulk flow saturates via the combined processes of reducing the background shear and rearranging and strengthening the background vertical magnetic field. We directly simulate the Ginzburg–Landau amplitude evolution for our system, and demonstrate the pattern formation our model predicts on long scales of length- and timescales. We compare the weakly nonlinear theory results to a direct numerical simulation of the MRI in a thin-gap Taylor Couette flow.
Experiments and numerical modeling of surrogate droplet combustion for a 93 octane gasoline calibration fuel
Gasoline is the world’s most prolific liquid transportation fuel for ground transportation vehicles. Yet, a fundamental understanding of its burning characteristics is still lacking. Complicating this matter is the highly multicomponent nature of gasoline (comprised of hundreds of chemical species) and the seasonable variability of its composition due to variations in the crude oil feedstocks used in refining. The need to bring order to this complex problem has led to development of calibration fuels (Tier II in presented study) which on average will provide repeatable metrics that replicate performance of refined gasoline for various engine designs. This study presents results of experiments using the configuration of an isolated droplet to study the combustion dynamics of calibration fuel and three different surrogates (S4, S5, S6) for the calibration fuel. Droplet burning is under microgravity conditions, where gas transport is determined entirely by fuel evaporation in order to promote spherical symmetry in the burning process. The initial droplet diameters ranged between 0.55 mm and 0.60 mm. The droplets were spark-ignited and their burning histories were recorded by digital video cameras to obtain quantitative data. For each fuel, the evolutions of droplet, flame, and soot-shell diameters were measured to calculate the burn rates, flame-droplet diameter ratios (FSR) and soot-droplet diameter ratios. Additionally, a detailed numerical modeling is presented. This simulation includes multicomponent phase equilibrium, unsteady gas and liquid transport, radiative heat transfer, internal liquid diffusion and detailed combustion chemistry of different fuels during their burning process. Experiment results show that the calibration fuel and surrogates have a close burn rates and FSR, and S5 presents a relatively similar results with calibration fuel. Predictions from the numerical simulation using the surrogate are compared with experiment measurements, which showed a good agreement with the experiment results, including the droplet burning rate and FSR.
Random Walks With Tweedie: A Unified View of Score-Based Diffusion Models [In the Spotlight]
We present a concise derivation for several influential score-based diffusion models that relies on only a few textbook results. Diffusion models have recently emerged as powerful tools for generating realistic, synthetic signals—particularly natural images—and often play a role in state-of-the-art algorithms for inverse problems in image processing. While these algorithms are often surprisingly simple, the theory behind them is not, and multiple complex theoretical justifications exist in the literature. Here, in this study, we provide a simple and largely self-contained theoretical justification for score-based diffusion models that is targeted towards the signal processing community. This approach leads to generic algorithmic templates for training and generating samples with diffusion models. We show that several influential diffusion models correspond to particular choices within these templates and demonstrate that alternative, more straightforward algorithmic choices can provide comparable results. This approach has the added benefit of enabling conditional sampling without any likelihood approximation.