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Results for “chemical dynamics”
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Dynamical tuning of the chemical potential to achieve a target particle number in grand canonical Monte Carlo simulations
We present a method to facilitate Monte Carlo simulations in the grand canonical ensemble given a target mean particle number. The method imposes a fictitious dynamics on the chemical potential, to be run concurrently with the Monte Carlo sampling of the physical system. Corrections to the chemical potential are made according to time-averaged estimates of the mean and variance of the particle number, with the latter being proportional to thermodynamic compressibility. Here, we perform a variety of tests, and in all cases find rapid convergence of the chemical potential—inexactness of the tuning algorithm contributes only a minor part of the total measurement error for realistic simulations.
Modelling the Sensitivity of Yukon River Biogeochemical Dynamics to Environmental and Chemical Drivers: Implications for Dissolved Organic Carbon
Riverine dissolved organic carbon (DOC) is a critical biogeochemical component that transmits information from Arctic soils to the Arctic Ocean, significantly influencing carbon dynamics in this unique ecosystem. As DOC travels downstream, it undergoes transformations that alter its composition and fate. The Yukon River serves as an effective testbed for modelling these dynamics, offering sufficient scale to capture key biogeochemical processes while having a simpler hydrology than other major Arctic rivers, as well as long-term DOC observational data for model validation. To investigate DOC transformations during transit in the Yukon River, we adapted our Arctic Riverine Organic Macromolecular Model by applying regional-specific parameterisations. Our model simulates the transport and transformation of 15 organic macromolecules, including CDOM (coloured dissolved organic matter), proteins, polysaccharides, lipids, lignin phenols, and humic substances. Initial DOC concentrations were derived from observed soil organic carbon stocks in the surrounding watershed, while chemical transformations and hydrological dynamics were modelled along the river's course. Sensitivity and uncertainty analyses were conducted using a Monte Carlo approach under two experimental setups. Results revealed that variability in DOC and CDOM concentrations at the river mouth were predominantly driven by initial DOC concentration (~70% of variability explained) and dilution at confluence points (~10%). The refractory fraction of DOC explained 21%–88% of the variability in 14 macromolecular concentrations and ranked in the top five sensitive parameters for all outputs when a uniform parameter distribution was assumed. However, when a more likely variability was applied to this parameter, its influence on DOC and CDOM decreased. Given that refractory DOC accounts for ~80% of total DOC in Arctic Rivers, this suggests that most DOC resists degradation and retains its chemical composition during transport to the coastal environment. River velocity, which determines residence time, explained 8%–47% of the variability in protein, polysaccharide, lipid, pigments, and lignin phenols at the river mouth. In contrast, chemical turnover times contributed only 1%–5% to output variability. Our findings underscore the need for improved land-specific headwater observations, including seasonal soil moisture and lateral transport dynamics that control the initial tributary-specific DOC inputs. With accelerated permafrost thaw and increasing river discharge, extending our model to other Arctic River systems and seasons will enhance understanding of Arctic riverine carbon fluxes and their contributions to the Arctic Ocean.
A study of the general dynamics of the physical-chemical systems in mammals Final report
Dynamic aspects of temperature regulating system, cardiovascular system, hormonal system, central nervous system, and behavior in mammals
RR Lyrae stars and the distant Galactic halo - Distribution, chemical composition, kinematics, and dynamics
Published observational data on 29 outer-Galactic-halo RR Lyr stars (Saha, 1984, and Saha and Oke, 1984) and from other surveys are analyzed to characterize the mass distribution, chemical composition, kinematics, and dynamics of the outer Galaxy (up to galactocentric radius R = 40 kpc). The results are presented in graphs and tables and discussed. Findings reported include a rapid fall-off of RR Lyr density at R = 25 kpc, Fe/H varying from -1.0 to -2.2 (with two stars at -0.5), no net Fe/H gradient in the far halo, and Galactic mass (from dynamical computations) about 3 x 10 to the 11th solar mass within R = 25 kpc.
Challenges in the study of chemistry and photochemistry at air–water interfaces: Toward in situ monitoring of reaction kinetics with spectroscopic techniques
Air–water interfaces, including those on droplet surfaces, have been the subject of many recent experiments due to their propensity for unique chemistry. Here, in this study, an overview of some recent advancements in understanding interfacial reaction kinetics is provided, highlighting non-surface-specific methods—such as mass spectrometry—compared with the advantages of surface-specific techniques—such as reflection–absorption spectroscopy, sum frequency generation, and photoelectron spectroscopy. This Perspective discusses the information depth and time constraints of common surface-specific spectroscopic methods that need to be addressed to monitor interfacial chemistry in situ and uses a few key examples from the literature as case studies. It concludes by advocating for the continued development of advanced spectroscopic methods to further investigate interfacial chemistry, underscoring the need for interdisciplinary collaboration to bridge the gap between molecular-level insights and macroscopic observations in future research.
Machine Learning Enabled Advanced Electron Tomography for Resolving Chemical Inhomogeneity and Materials Dynamics in Lithium-Ion Battery Electrodes
The objective of this project is to develop machine learning-assisted electron microscopy, together with three-dimensional, cryogenic, and in-situ imaging techniques, for resolving chemical inhomogeneity and materials dynamics in lithium battery electrodes and interfaces. The project aims to expand the spatial, temporal, and dimensional resolution of transmission electron microscopy and to enable quantitative analysis of beam-sensitive battery materials.
Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics
The chemical equilibrium between self-ionized and molecular water dictates the acid–base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)–based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid–base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H 2 O) cells. The reversible work to separate the hydroxide and hydronium to a distance S is found to converge for simulation cells containing more than 500 H 2 O, and a distance of ∼ 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.
Research and investigation of gas dynamic lasers
Chemical mixing laser investigations were conducted (1) to investigate the properties of a bimolecular exchange laser system pumped by the H + Cl2 yields HCl(v) + Cl reaction, initiated by arc-dissociated H2, with lasing occurring between wavelengths of 3.4 and 4.0 microns, and (2) to establish the feasibility of an atom recombination-transfer laser employing recombination of arc-dissociated nitrogen with subsequent transfer of vibrational energy to CO2 for lasing at 10.6 microns. One-dimensional analytical results indicate higher results should be obtained with up to v = 3 to 2 transitions participating. Diagnostic and analytical results show that the reaction mechanism during mixing, a back reaction of HCl(v) with H atoms, reaction of Cl with H2(v), moderately fast V-V, V-T processes, and possible HCl(o) initial contaminant level may explain the low performance. N2-CO2 thermal mixing laser studies were extended to measure the efficiency of transfer of recombination energy in such a nonequilibrium N2 source to 10.6 microns optical energy. The low level of efficiency suggests that V-T decay processes may prevent vibrational energy freezing until much lower temperatures are achieved and that trapping of energy in long-lived electronic excitation of N2 may be a factor.
Chemistry in dynamically evolving clouds
A unified model of chemical and dynamical evolution of isolated, initially diffuse and quiescent interstellar clouds is presented. The model uses a semiempirically derived dependence of the observed cloud temperatures on the visual extinction and density. Even low-mass, low-density, diffuse clouds can collapse in this model, because the inward pressure gradient force assists gravitational contraction. In contrast, previous isothermal collapse models required the low-mass diffuse clouds to be unrealistically cold before gravitational contraction could start. Theoretically predicted dependences of the column densities of various atoms and molecules, such as C and CO, on visual extinction in diffuse clouds are in accord with observations. Similarly, the predicted dependences of the fractional abundances of various chemical species (e.g., CO, H2CO, HCN, HCO(+)) on the total hydrogen density in the core of the dense clouds also agree with observations reported to date in the literature. Compared with previous models of interstellar chemistry, the present model has the potential to explain the wide spectrum of chemical and physical properties of both diffuse and dense clouds with a common formalism employing only a few simple initial conditions.
A study of the general dynamics of the physical-chemical systems in mammals Final report
General dynamics of mammalian temperature regulating system, cardiovascular system, hormonal system, central nervous system, and behavior
Detailed Studies on the Structure and Dynamics of Reacting Dusty Flows at Normal and Microgravity
Dusty reacting flows are of particular interest for a wide range of applications. Inert particles can alter the flammability and extinction limits of a combustible mixture. Reacting particles can release substantial amount of heat and can be used either for power generation or propulsion. Accumulation of combustible particles in air can result in explosions which, for example, can occur in grain elevators, during lumber milling and in mine galleries. Furthermore, inert particles are used as flow velocity markers in reacting flows, and their velocity is measured by non-intrusive laser diagnostic techniques. Despite their importance, dusty reacting flows have been less studied and understood compared to gas phase as well as sprays. The addition of solid particles in a flowing gas stream can lead to strong couplings between the two phases, which can be of dynamic, thermal, and chemical nature. The dynamic coupling between the two phases is caused by the inertia that causes the phases to move with different velocities. Furthermore, gravitational, thermophoretic, photophoretic, electrophoretic, diffusiophoretic, centrifugal, and magnetic forces can be exerted on the particles. In general, magnetic, electrophoretic, centrifugal, photophoretic, and diffusiophoretic can be neglected. On the other hand, thermophoretic forces, caused by steep temperature gradients, can be important. The gravitational forces are almost always present and can affect the dynamic response of large particles. Understanding and quantifying the chemical coupling between two phases is a challenging task. However, all reacting particles begin this process as inert particles, and they must be heated before they participate in the combustion process. Thus, one must first understand the interactions of inert particles in a combustion environment. The in-detail understanding of the dynamics and structure of dusty flows can be only advanced by considering simple flow geometries such as the opposed-jet, stagnation-type. In such configurations the imposed strain rate is well characterized, and the in-depth understanding of the details of the physico-chemical processes can be systematically obtained. A number of computational and experimental studies on spray and particle flows have been conducted in stagnation-type configurations. Numerically, the need for a hybrid Eulerian-Lagrangian approach has been identified by Continillo and Sirignano, and the use of such approach has allowed for the prediction of the phenomenon of droplet flow reversal. Gomez and Rosner have conducted a detailed study on the particle response in the opposed-jet configuration, and the particle thermophoretic diffusivities were determined experimentally. Sung, Law and co-workers have conducted numerical studies on the effect of strain rate and temperature gradients on the dynamics of inert particles, as a way of understanding potential errors in experimental LDV data that may arise from thermophoretic forces. This investigation is a combined experimental and numerical study on the details of reacting dusty flows. The specific tasks are: (1) Experimental determination of laminar flame speeds, and extinction strain rates of dusty flows at normal- and micro-gravity as functions of the particle type, particle initial diameter, particle initial number density, and gas phase chemical composition; (2) Detailed numerical simulation of the experiments. Results are compared with experiments and the adequacy of theoretical models is assessed; and (3) Provision of enhanced insight into the thermo-chemical coupling between the two phases.
Using Dissipative Particle Dynamics to Model Effects of Chemical Reactions Occurring within Hydrogels
Computational models that reveal the structural response of polymer gels to changing, dissolved reactive chemical species would provide useful information about dynamically evolving environments. However, it remains challenging to devise one computational approach that can capture all the interconnected chemical events and responsive structural changes involved in this multi-stage, multi-component process. Here, we augment the dissipative particle dynamics (DPD) method to simulate the reaction of a gel with diffusing, dissolved chemicals to form kinetically stable complexes, which in turn cause concentration-dependent deformation of the gel. Using this model, we also examine how the addition of new chemical stimuli and subsequent reactions cause the gel to exhibit additional concentration-dependent structural changes. Through these DPD simulations, we show that the gel forms multiple latent states (not just the “on/off”) that indicate changes in the chemical composition of the fluidic environment. Hence, the gel can actuate a range of motion within the system, not just movements corresponding to the equilibrated swollen or collapsed states. Moreover, the system can be used as a sensor, since the structure of the layer effectively indicates the presence of chemical stimuli.
Dynamical electron correlation and the chemical bond. I. Covalent bonds in AH and AF (A = B–F)
Dynamical electron correlation has a major impact on the computed values of molecular properties and the energetics of molecular processes. This study focused on the effect of dynamical electron correlation on the spectroscopic constants ($R$ e , $ω$ e , $D$ e ), and potential energy curves, Δ$E$($R$), of the covalently bound AH and AF molecules, A = B–F. The changes in the spectroscopic constants (Δ$R$ e , Δ$ω$ e , Δ$D$ e ) caused by dynamical correlation are erratic and, at times, even surprising. These changes can be understood based on the dependence of the dynamical electron correlation energies of the AH and AF molecules as a function of the bond distance, i.e., Δ$E$ DEC ($R$). At large $R$, the magnitude of Δ$E$ DEC ($R$) increases nearly exponentially with decreasing $R$, but this increase slows as $R$ continues to decrease and, in many cases, even reverses at very short $R$. The changes in Δ$E$ DEC ($R$) in the region around $R$ e were as unexpected as they were surprising, e.g., distinct minima and maxima were found in the curves of Δ$E$ DEC ($R$) for the most polar molecules. The variations in Δ$E$ DEC ($R$) for $R$ ≲ $R$ e are directly correlated with major changes in the electronic structure of the molecules as revealed by a detailed analysis of the spin-coupled generalized valence bond wave function. In conclusion, the results reported here indicate that we have much to learn about the nature of dynamical electron correlation and its effect on chemical bonds and molecular properties and processes.
Solar and chemical power systems.
Dynamic conversion of solar and chemical energy, discussing power conditioning, Brayton-cycle system with inert gas and spacecraft batteries
Airborne lidar stratospheric ozone and aerosol investigations
The objectives are to study the distribution of ozone (O3) and aerosols across the polar regions during the winter and spring periods and to relate these observations to chemical and dynamical processes that can contribute to the chemical perturbation of the polar stratosphere and the possible destruction of O3. The distribution and characteristics of stratospheric aerosols and polar stratospheric clouds (PSCs) are required to understand heterogeneous chemical processes that can lead to O3 depletion, and observation of O3 variations are important in the direct detection of O3 depletion and in tracing atmospheric dynamics. An airborne Differential Absorption Lidar (DIAL) system is operated in a zenith mode from the NASA DC-8 aircraft to obtain data on the large scale spatial variability of O3, and aerosol/PSC's in the lower stratosphere from about 11 to 23 km for O3, and 11 to 28 km for aerosols. The variability of O3 and aerosols/PSCs is studied in relation to chemical processes that can produce O3 depletion and to dynamics in the lower stratosphere that transport gases and aerosols inside the vortex and in some cases, across the edge of the vortex.
Computational fluid dynamics analysis of char conversion in Sandia’s pressurized entrained flow reactor
Design and analysis of practical reactors utilizing solid feedstocks rely on reaction rate parameters that are typically generated in lab-scale reactors. Evaluation of the reaction rate information often relies on assumptions of uniform temperature, velocity, and species distributions in the reactor, in lieu of detailed measurements that provide local information. This assumption might be a source of substantial error, since reactor designs can impose significant inhomogeneities, leading to data misinterpretation. Spatially resolved reactor simulations help understand the key processes within the reactor and support the identification of severe variations of temperature, velocity, and species distributions. In this work, Sandia’s pressurized entrained flow reactor is modeled to identify inhomogeneities in the reaction zone. Tracer particles are tracked through the reactor to estimate the residence times and burnout ratio of introduced coal char particles in gasifying environments. The results reveal a complex mixing environment for the cool gas and particles entering the reactor along the centerline and the main high-speed hot gas reactor flow. Furthermore, the computational fluid dynamics (CFD) results show that flow asymmetries are introduced through the use of a horizontal gas pre-heating section that connects to the vertical reactor tube. Computed particle temperatures and residence times in the reactor differ substantially from the idealized plug flow conditions typically evoked in interpreting experimental measurements. Furthermore, experimental measurements and CFD analysis of heat flow through porous refractory insulation suggest that for the investigated conditions (1350 °C, <20 atm), the thermal conductivity of the insulation does not increase substantially with increasing pressure.
Toward a machine-guided approach to energetic material discovery
In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources.