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Polymer-coated nanoparticles in synovial fluid and hyaluronic acid solutions diffuse faster than expected based on bulk viscosity.
Cycle benefits of rotating detonation engines show up to five percentage points of efficiency gain for low-pressure ratio engines. An optimal integration between the combustor and the turbine needs to be guaranteed to realize this potential gain. The rotating detonation combustor (RDC) exhausts transonic flow with shocks rotating at frequencies ranging from a few to tens of kilohertz depending on the number of present waves. Hence, the turbine design requires precise knowledge of the fluctuations and losses downstream of the combustor. This paper focuses on the quantification of fluctuations and losses for accelerating and diffusing passages. The analysis of the combustor is performed via reactive unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. The unsteady RANS equations are solved via CFD++ from Metacomp with a one-step reaction mechanism for an H2-air mixture. The resolving of the boundary layer is achieved with a structured mesh of around 36 million cells. Inlet pressure of 10 bar and two different back pressures are applied to the combustor to model the interconnection with downstream turbines. Finally, we present and assess a methodology to reduce the computational time to model these passages ten times.
Abstract In this paper, a diffuser is used to integrate a transonic high-pressure turbine with a rotating detonation combustor (RDC). The paper focuses on the required design modifications to the turbine endwalls (EW) to enable high efficiency, while preserving the airfoil blade-to-blade geometry. The main challenge is the stator passage unstarting, due to the high inlet Mach number. First of all, steady Reynolds Averaged Navier Stokes simulations were performed to compare the efficiency of turbines with constant-radius EWs to turbines with axisymmetric EWs. A modified EW design prevented the unstarting of the stator passage, enabling a significant gain in performance. Afterward, the influence on the turbine efficiency and damping due to the unsteadiness from the diffuser-like fluctuations of the RDC was evaluated with unsteady Reynolds Averaged Navier Stokes simulations with a mixing plane approach (MPA). Full unsteady simulations were carried out on selected inlet conditions and compared to the mixing plane results. This parametric study provides turbine designers with recommended diffusion rates along the vane EWs. Additionally, we provide guidance on the upstream diffuser design, specifically the required damping and outlet Mach number.
Abstract Ionene – ionic liquid (IL) composites are promising materials for CO 2 separation, yet a molecular‐level understanding of their structure and its impact on CO 2 speciation, solubility, rotation, and diffusivity remains unclear. Herein, using multimodal nuclear magnetic resonance (NMR), time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), atomic force microscopy (AFM), and molecular dynamics (MD) simulations, we reveal that the composites contain IL‐rich domains extending across hundreds of nanometres within the ionene matrix, and these bicontinuous domains span the entire membrane depth. CO 2 also absorbs into the ionene matrix, with the distribution between two CO 2 species varying with temperature and time. The rotational correlation times of these two species are on the timescale of 0.1 and 1 ns, respectively. As IL content increases, the ionic domains expand, resulting in higher CO 2 solubility due to enhanced molecular dynamics and increased free volume in both ionene backbones and IL‐rich regions. Although CO 2 diffusion in the membranes is an order of magnitude slower than in bulk IL, the activation energy for CO 2 diffusion remains comparable. Ionene‐IL composites represent a promising platform for designing CO 2 separation membranes, offering enhanced CO 2 diffusion and selectivity through IL‐rich domains, and increased CO 2 solubility and mechanical integrity from the ionene matrix.
For this study, mixtures of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in diglyme (G2), triglyme (G3) and tetraglyme (G4) at solvate ionic liquid (SIL) concentrations were investigated using classical molecular dynamics (cMD) simulations with a physically motivated force-field specifically developed for modeling these systems. The structural and dynamical properties of the mixtures were computed and analyzed. Lithium solvation shells, radial distribution functions, and X-ray structure factors were studied across the different SIL systems. Translational diffusion and rotational relaxation times were also evaluated, exhibiting similar trends with increasing glyme chain length. The results are consistent with experimental data and in good agreement with previous computational studies on G3 and G4. These findings validate the accuracy of the force field in modeling glyme systems and its use for describing the [Li(G2) 4/3 ][TFSI] mixture. Additionally, the thermal and electrochemical stability of these electrolytes were systematically examined. The thermal stability appears to be governed by cooperative interactions among glyme molecules, while the electrochemical stability is primarily influenced by Li + -anion interactions, which vary significantly with glyme chain length. Overall, the study sheds light on the crucial role of the anion in these glyme-based SILs and offers valuable insights into Li + -glyme systems at SIL concentrations, highlighting their promise as potential Li-ion battery electrolytes.
We investigate the temperature dependence of nuclear quantum effects (NQEs) on structural and dynamic properties of liquid water by training a neural network force field using first-principles molecular dynamics (FPMD) based on the strongly constrained and appropriately normed meta-generalized gradient approximation exchange-correlation approximation. The FPMD simulation based on density functional theory has become a powerful computational approach for studying a wide range of condensed phase systems. However, its large computational cost makes it difficult to incorporate NQEs in the simulation and investigate temperature dependence of various properties. To circumvent this difficulty, we use an artificial neural network model and employ the thermostatted ring polymer MD approach for studying the temperature dependence of NQEs on various properties. The NQEs generally bring the radial distribution functions closer to the experimental measurements. Translational diffusivity and rotational dynamics of water molecules are both slowed down by the NQEs. The competing inter-molecular and intra-molecular quantum effects on hydrogen bonds, as discussed by Habershon, Markland, and Manolopoulos [J. Chem. Phys. 131(2), 024501 (2019)], can explain the observed temperature dependence of the NQEs on the dynamical properties in our simulation.
The flexible degrees of freedom in metal–organic frameworks (MOFs) can have significant effects on guest molecule behavior. However, in the majority of studies applying molecular simulations to MOFs, the framework is assumed to be rigid in order to minimize computational cost. Here, in this work, we assess the significance of this assumption on a representative example of methane uptake and diffusion in UiO-66. We introduce an open-source code to modify MOFs through functionalization and linker rotation and we perform Grand Canonical Monte Carlo and molecular dynamics simulations of methane in each of the functionalized and linker-rotated derivatives of UiO-66. We find that linker rotation moderately influences methane uptake and significantly influences methane diffusion. Our assessment provides ranges of property values that serve as measures of uncertainty of these two properties associated with linker rotation. We further determine that void volume fraction and minimum pore size are the features that govern methane uptake and diffusion, respectively. These findings illustrate the impact of linker rotation on MOFs and provide design principles to guide future investigations.
In rotating detonation engines the turbine inlet conditions may be transonic with unprecedented unsteady fluctuations. To ensure an acceptable engine performance, the turbine passages must be suited to these conditions. This article focuses on designing and characterizing highly diffusive turbine vanes to operate at any inlet Mach number up to Mach 1. First, the effect of pressure loss on the starting limit is presented. Afterward, a multi-objective optimization with steady RANS simulations, including the endwall and 3D vane design is performed. Compared to previous research, significant reductions in pressure loss and stator-induced rotor forcing are obtained, with an extended operating range and preserving high flow turning. Finally, the influence of the inlet boundary layer thickness on the vane performance is evaluated, inducing remarkable increases in pressure loss and downstream pressure distortion. Employing an optimization with a thicker inlet boundary layer, specific endwall design recommendations are found, providing a notable improvement in both objective functions.
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In this work, we develop implicit rank-adaptive schemes for time-dependent matrix differential equations. The dynamic low rank approximation (DLRA) is a well-known technique to capture the dynamic low rank structure based on Dirac–Frenkel time-dependent variational principle. In recent years, it has attracted a lot of attention due to its wide applicability. Our schemes are inspired by the three-step procedure used in the rank adaptive version of the unconventional robust integrator (the so called BUG integrator) (Ceruti et al. in BIT Numer Math 62(4):1149–1174, 2022) for DLRA. First, a prediction (basis update) step is made computing the approximate column and row spaces at the next time level. Second, a Galerkin evolution step is invoked using an implicit solves for the small core matrix. Finally, a truncation is made according to a prescribed error threshold. Since the DLRA is evolving the differential equation projected on to the tangent space of the low rank manifold, the error estimate of the BUG integrator contains the tangent projection (modeling) error which cannot be easily controlled by mesh refinement. This can cause convergence issue for equations with cross terms. To address this issue, we propose a simple modification, consisting of merging the row and column spaces from the explicit step truncation method together with the BUG spaces in the prediction step. In addition, we propose an adaptive strategy where the BUG spaces are only computed if the residual for the solution obtained from the prediction space by explicit step truncation method, is too large. Here, we prove stability and estimate the local truncation error of the schemes under assumptions. We benchmark the schemes in several tests, such as anisotropic diffusion, solid body rotation and the combination of the two, to show robust convergence properties.
Nanoconfinement alters the thermodynamics, dynamics, and kinetics of fluids hosted in nanoscale solid nanopores to an extent that depends on the characteristics of the confining space and the chemistry of the confined fluids. Confinement-induced alterations in the phase behavior of confined energetic fluids under high pressure or low temperature are highly relevant to subsurface and subsea phenomena such as fluid flow in porous media, hydrate formation and dissociation, and gas storage capacity. Although extensive efforts have been directed toward understanding the phase behavior of confined fluids, the role of solid-liquid interfaces in the phase transitions of organic liquids has not been resolved yet. Here, we explore the onset and growth of benzene crystallization confined in 6 nm sized SBA-15 silica nanopores in the temperature range 300-200 K using in situ extended range small-angle and wide-angle X-ray scattering (SAXS/WAXS) measurements and atomistic classical molecular dynamics (MD) simulations. The crystallization onset of confined benzene depresses to 265 K compared to the freezing point of bulk benzene ( ~278 K), followed by the continuous growth of the emerged crystals in the pore space with complete crystallization at 200 K. The orientation of the emerged benzene crystals is dominated by parallel (π-π stacking) and perpendicular (T-shape stacking) orientations along the cylindrical pore radius and pore length, respectively. The onset of benzene crystals occurs heterogeneously on the pore surface and grows continuously toward the pore center. Further, confined benzene undergoes a dynamical crossover from fragile to strong dynamics behavior, inferred from the rotational and translational diffusion. The insights provided by this study have significant implications for the phase transitions of confined organic liquids that are relevant to a wide range of applications in the biological, geological, environmental, and chemical fields.
Interfacial anodic degradation in graphitic materials under fast charging conditions causes severe performance loss and safety hazard in lithium ion batteries. We present a novel method for minimizing the growth of these aging mechanism by application of an external magnetic field. Under magnetic field, paramagnetic lithium ions experience a magnetohydrodynamic force, which rotates the perpendicularly diffusing species and homogenizes the ionic transport. This phenomenon minimizes the overpotential hotspots at the anode/separator interface, consequently reducing SEI growth, lithium plating, and interfacial fracture. In situ electrochemical measurements indicate an improvement in capacity for lithium cobalt oxide/graphite pouch cell (20 mAh) charged from 1–5 C under an applied field of 1.8 kG, with a maximum capacity gain of 22% at 5C. Post-mortem FE-SEM and EDS mapping shows that samples charged with magnetic field have a reduced lithium deposition at 3C and a complete suppression of interfacial fracture at 5C. At 5C, a 24% reduction in the lithium content is observed by performing XPS on the anodic interfacial film. Finally, fast charging performance under variable magnetic field strengths indicate a saturation behavior in capacity at high fields (>2 kG), thereby limiting the field and consequent energy requirements to obtain maximum capacity gain under extreme conditions.
Assembly of biomolecules at solid-water interfaces requires molecules to traverse complex orientation-dependent energy landscapes through processes that are poorly understood, largely due to the dearth of in-situ single molecule measurements and statistical analyses of the rotational dynamics that define directional selection. Emerging capabilities in high-speed atomic force microscopy and machine learning have allowed us to directly determine the orientational energy landscape and observe and quantify the rotational dynamics for protein nanorods on the surface of muscovite mica under a variety of conditions. Comparisons with kinetic Monte Carlo simulations show that the transition rates between adjacent orientation-specific energetic minima can largely be understood through traditional models of in-plane Brownian rotation across a biased energy landscape, with resulting transition rates that are exponential in the energy-barriers between states. However, transitions between more distant angular states are decoupled from barrier height, with jump-size distributions showing a power-law decay that is characteristic of a non-classical Levy-flight random walk, indicating that large jumps are enabled by alternative modes of motion via activated states. The findings provide new insights into the dynamics of biomolecules at solid-liquid interfaces that lead to self-assembly, epitaxial matching and other orientationally anisotropic outcomes and define a general procedure for exploring such dynamics with implications for hybrid biomolecular-inorganic materials design.
Abstract The secular evolution of disk galaxies is largely driven by resonances between the orbits of “particles” (stars or dark matter) and the rotation of non-axisymmetric features (spiral arms or a bar). Such resonances may also explain kinematic and photometric features observed in the Milky Way and external galaxies. In simplified cases, these resonant interactions are well understood: for instance, the dynamics of a test particle trapped near a resonance of a steadily rotating bar is easily analyzed using the angle-action tools pioneered by Binney, Monari, and others. However, such treatments do not address the stochasticity and messiness inherent to real galaxies—effects that have, with few exceptions, been previously explored only with complex N -body simulations. In this paper, we propose a simple kinetic equation describing the distribution function of particles near an orbital resonance with a rigidly rotating bar, allowing for diffusion of the particles’ slow actions. We solve this equation for various values of the dimensionless diffusion strength Δ, and then apply our theory to the calculation of bar–halo dynamical friction. For Δ = 0, we recover the classic result of Tremaine and Weinberg that friction ultimately vanishes, owing to the phase mixing of resonant orbits. However, for Δ > 0, we find that diffusion suppresses phase mixing, leading to a finite torque. Our results suggest that stochasticity—be it physical or numerical—tends to increase bar–halo friction, and that bars in cosmological simulations might experience significant artificial slowdown, even if the numerical two-body relaxation time is much longer than a Hubble time.
Since the 1980s, the paddlewheel effect has been suggested as a mechanism to boost lithium-ion diffusion in inorganic materials via the rotation of rotor-like anion groups. However, it remains unclear whether the paddlewheel effect, defined as large-angle anion group rotations assisting Li hopping, indeed exists; furthermore, the physical mechanism by which the anion-group dynamics affect lithium-ion diffusion has not yet been established. In this work, we differentiate various types of rotational motions of anion groups and develop quaternion-based algorithms to detect, quantify, and relate them to lithium-ion motion in ab initio molecular dynamics simulations. Our analysis demonstrates that, in fact, the paddlewheel effect, where an anion group makes a large angle rotation to assist a lithium-ion hop, does not exist and thus is not responsible for the fast lithium-ion diffusion in superionic conductors, as historically claimed. Instead, we find that materials with topologically isolated anion groups can enhance lithium-ion diffusivity via a more classic nondynamic soft-cradle mechanism, where the anion groups tilt to provide optimal coordination to a lithium ion throughout the hopping process to lower the migration barrier. This anion-group disorder is static in nature, rather than dynamic and can explain most of the experimental observations. Our work substantiates the nonexistence of the long-debated paddlewheel effect and clarifies any correlation that may exist between anion-group rotations and fast ionic diffusion in inorganic materials.
The interplay between crystal and solvent structure, interparticle forces and ensemble particle response dynamics governs the process of crystallization by oriented attachment (OA), yet a quantitative understanding is lacking. Using as a model system ZnO, we combine in situ TEM observations of single particle and ensemble assembly dynamics with simulations of interparticle forces and responses to relate experimentally derived interparticle potentials to the underlying interactions. We show that OA is driven by forces and torques due to a combination of electrostatic ion-solvent correlations and dipolar interactions that act at separations well beyond 5 nm. Importantly, coalignment is achieved before particles reach separations at which strong attractions drive the final jump to contact. The observed barrier to attachment is negligible, while dissipative factors in the quasi-2D confinement of the TEM fluid cell lead to abnormal diffusivities with timescales for rotation much less than for translation, thus enabling OA to dominate.
Solid-state electrolytes (SSEs) for all-solid-state lithium-ion batteries are generating intense interest because these batteries can improve the safety and performance compared with devices fabricated with conventional, flammable liquid electrolytes. In these SSEs, it has been suggested that Li + ion diffusion in the grain boundaries is hindered and is a critical determinant of the overall ionic conductivity (σ). However, Li + ion diffusivities in the grain (D G ) and the grain boundary (D GB ) are difficult to determine experimentally, with few techniques capable of distinguishing the individual contributions. Here, we distinguished the D G and D GB for the protonated lithium antiperovskites (pLiAPs) SSEs: Li 2 OHCl, Li 2 OHBr, Li 2 OHF 0.1 Cl 0.9 , Li 2 OHF 0.1 Br 0.9 , and Li 2 OHCl 0.3 7Br 0.63 . The measurements were obtained directly from 7 Li pulsed-field gradient nuclear magnetic resonance (PFG-NMR) at 353 K. The 7 Li PFG-NMR echo profiles were composed of two primary components with additional secondary oscillatory components – the so-called NMR diffraction phenomenon. The length scale separating the two main components corresponds to a diffusion length of ~1.7 µm, which is thought to be the average grain size (by diameter). The short-range (≤ 1.7 µm) diffusion component associated with D G (≈10 -11 m 2 /s) varied minimally with halide substitution, while the long-range (≥ 1.7 µm) component D GB (≈10 -12 to 10 -15 m 2 /s) was highly sensitive to the substitution of halides and closely correlated with s. In addition, from the comparison of the ratio D GB /D G to D t (the Li + ion diffusion coefficient estimated from the rotational correlation time, t c ), it was determined that the contribution of D G to σ is negligible; 0.01 ~ 0.04 in the pLiAPs studied here. Finally, these insights provide fundamental understanding of the halide substitution effects on Li + ion grain versus grain boundary diffusion, and suggest that careful engineering of the grain boundaries at the microscopic level is necessary to achieve high-performance pLiAP SSEs.
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.