Atomic Dynamics Research Articles

Carbon nanotube triaxial woven films with high mechanical properties for impacting protection.

Fiber-based fabrics have great potential in impacting protection as composite materials. Herein, we proposed a novel nanostructure in which the (5,5) single-walled CNTs (SWCNTs) with a diameter d of ∼6.67 Å were employed to weave triaxial 2D films with an angle of adjacent CNTs as 60°. The bending angle of a single CNT in the structure unit length was set at 90°, while the bent length was 196.87 Å. The in-plane mechanical properties and impacting properties of triaxial woven films (TWFs) were investigated through a fully atomic molecular dynamics (MD) simulation. Experiments verified that the tensile mechanical properties of TWFs were related to the loading directions. The impacting test of single-layer films showed that the TWF possessed impacting protection and reached its force peak at the center of the sphere passing through the film. In addition, for bilayer composite films of TWFs and plain woven films, this combination developed a high strain with high stress. The impacting properties of the composite film in both directions (plain/triaxial and triaxial/plain) were investigated, and the percentages of absorbed energy were calculated to be 87.8% and 86.9% for the Plain/Triaxial and Triaxial/Plain films, respectively, indicating an outstanding impacting protection for the composite films. This work provides an in-depth understanding of the mechanical properties of TWFs and broadens the applications of CNT-based nanomaterials.

Unraveling Electron Transfer in the Oxidation of Yttrium Metal Atoms: Dual Pathways from Reactive and Nonreactive Imaging.

The intricate mechanisms underlying electron transfer and structural evolution are essential to understanding the oxidation dynamics of transition metal atoms; however, accurately measuring the mechanisms remains challenging. In this study, utilizing laser ablation-crossed beam and time-sliced ion velocity imaging techniques, we identified two distinct electron transfer mechanisms in the reactions of Y and O2 based on reactive and nonreactive scattering measurements across varying collision energies. (1) Low-barrier end-on pathway: Electron transfer occurs through a collinear Y-O-O geometry with a low activation barrier, evidenced by rebound scattering of YO products at low collision energy and backward scattering of Y reactants at higher energies. (2) High-barrier side-on pathway: Electron transfer proceeds through a side-on geometry, presenting a higher activation barrier that facilitates the formation of long-lived O-Y-O intermediates, which is characterized by the backward-forward peaking angular distribution of YO products and broad energy distributions of O2 reactants at high collision energy.

Hydrogen Controls the Heavy Atom Roaming in Transient Negative Ion.

Bromine and hydrogen play unusual roles as mobile atom and dissociation dynamics moderator, respectively, during roaming in the 3-bromo-4H-1,2,4-triazole anion. The present study of the reactivity of 3-bromo-1H-1,2,4-triazole and 3-bromo-4H-1,2,4-triazole with low-energy electrons reveals the effect of the hydrogen position on the reaction dynamics. We report energy-dependent ion yields for both molecules showing significant differences. Quantum chemical calculations reveal that heavy Br atom migration is energetically more favored than H atom migration in the case of the H atom adjacent to Br. This is enabled by the energetically favorable formation of a noncovalent complex of Br- around the triazole ring. Recently, such complexes have been reported for several other biologically relevant molecules. In the present work, we demonstrate that the position of hydrogen on the ring influences the character of the lowest resonant state and, consequently, the Br- roaming and dissociation dynamics, particularly the neutral release of hydrogen bromide.

Quantum beating and cyclic structures in the phase-space dynamics of the Kramers-Henneberger atom

Quantum beating and cyclic structures in the phase-space dynamics of the Kramers-Henneberger atom

Secondary atomization of thixotropic kerosene gel: Detailed numerical investigation of dynamics and characteristics

Gel propellants, as innovative energy materials, have shown great potential in aerospace applications due to their excellent safety and storage stability. However, their complex rheological properties pose challenges to atomization and combustion efficiency, particularly in understanding the dynamics of secondary atomization, which remains insufficiently explored. This study investigates the secondary atomization of thixotropic kerosene gel droplets (5% organic gel) through numerical simulations and theoretical modeling. A two-phase flow framework incorporating the volume of fluid and adaptive mesh refinement is developed to analyze deformation and breakup dynamics under bag, multimode, and shear breakup modes. The results show that at low Weber numbers (We), bag and multimode breakup exhibit similar initial stages, characterized by windward flattening, leeward stability, and inertia-driven bag formation. The increase in We enhances stamen structure development in multimode breakup, occasionally causing dual-bag formation. Shear breakup initiates with rapid edge ligament detachment, followed by core transition. Droplet size distributions demonstrate complex patterns influenced by breakup mechanisms, with thixotropic gels showing non-monotonic dependence on We and Ohnesorge numbers. Drag coefficients, affected by frontal area dynamics, indicate momentum exchange with surrounding gas. These findings advance understanding of non-Newtonian fluid atomization, providing insights for gel propellant applications in aerospace engineering.

Sr-doping effects on piezoelectric and dielectric properties of lead-free barium titanate via molecular dynamics approach

The study investigated the effects of Sr-doping on BaTiO₃ regarding the mean square displacement, diffusion coefficient, polarization-strain response, dielectric constant, and dielectric loss. Initially, increasing strontium doping up to 6% enhanced the mean square displacement (from 0.211 to 0.218 Å2) and the diffusion coefficient (from 0.361 to 0.380 nm2/ns) due to improved atomic mobility and lattice dynamics. However, further doping to 8% caused a decrease in both the mean square displacement and the diffusion coefficient, due to structural disorder and defects that impeded atomic movement. Similarly, the piezoelectric coefficient increased from 273.07 pC/N to 321.15 pC/N with 6% doping but declined to 308.65 pC/N at 8%. This indicated enhanced polarization at moderate doping levels but reduced performance due to excess impurities. The dielectric constant arose from 76 to 85 with 6% strontium but decreased to 80 at 8%, reflecting increased ferroelectric properties followed by structural degradation. Lastly, dielectric loss increased from 0.06 to 0.08 with 6% doping and then decreased to 0.073 at 8%. This suggested that while ionic mobility initially improved energy loss, excessive doping led to inefficiencies. Overall, optimal strontium doping enhanced the material’s ferroelectric and piezoelectric properties, while excessive doping introduced detrimental effects.

Simulation of atom trajectories in the original Stern–Gerlach experiment

Abstract Following a comprehensive analysis of the historical literature, we model the geometry of the Stern–Gerlach experiment to numerically calculate the magnetic field using the finite-element method. Using this calculated field and Monte Carlo methods, the semiclassical atomic translational dynamics are simulated to produce the well-known quantized end-pattern with matching dimensions. The finite-element method used provides the most accurate description of the Stern–Gerlach magnetic field and end-pattern in the literature, matching the historically reported values and figures.

Apolipoprotein E3 and E4 isoforms exhibit differing effects in countering endotoxins.

Apolipoprotein E (APOE) is distributed across various human tissues and plays a crucial role in lipid metabolism. Recent investigations have uncovered an additional facet of APOE's functionality, revealing its role in host defense against bacterial infections. To assess the antibacterial attributes of APOE3 and APOE4, we conducted antibacterial assays using Pseudomonas aeruginosa and Escherichia coli. Exploring the interaction between APOE isoforms and lipopolysaccharides (LPSs) from E.coli, we conducted several experiments, including gel shift assays, CD, and fluorescence spectroscopy. Furthermore, the interaction between APOE isoforms and LPS was further substantiated through atomic resolution molecular dynamics simulations. The presence of LPS induced the aggregation of APOE isoforms, a phenomenon confirmed through specific amyloid staining, as well as fluorescence and electron microscopy. The scavenging effects of APOE3/4 isoforms were studied through both invitro and invivo experiments. In summary, our study established that APOE isoforms exhibit binding to LPS, with a more pronounced affinity and complex formation observed for APOE4 compared with APOE3. Furthermore, our data suggest that APOE isoforms neutralize LPS through aggregation, leading to a reduction of local inflammation in experimental animal models. In addition, both isoforms demonstrated inhibitory effects on the growth of P.aeruginosa and E.coli. These findings provide new insights into the multifunctionality of APOE in the human body, particularly its role in innate immunity during bacterial infections.

Controlling many-body synchronous revival dynamics and entanglement cycle of ultracold atoms with synthetic gauge fields

The exploration of tunneling dynamics in many-body quantum systems plays a pivotal role in realizing groundbreaking atomtronic devices and achieving precise control over entanglement. Here, we delve into the intricate many-body tunneling dynamics of ultracold atoms confined within a quadruple-well potential, with a particular emphasis on the manipulative role played by interatomic interactions and synthetic gauge fields. The quantum dynamics displayed by the system closely mimic the operations of qudit hybrid gates, which are comprised of two relatively independent sets of qudits. In the Rabi regime, which is characterized by strong coupling, we observe an intriguing phenomenon: the cyclic collapse and revival of discrete tunneling dynamics within two distinct qudits. Notably, this intricate dynamical behavior exhibits remarkable synchronous and anti-synchronous revival features, offering a unique opportunity to engineer many-body synchronization pulse signals. In the resonant tunneling regime, distinguished by strong interatomic interactions, we made a remarkable observation: the hybrid qudits undergo a dimensionality reduction, effectively transforming into a qudit. This dimensionality reduction is accompanied by an intriguing phenomenon: the periodicity of its dynamics is intimately intertwined with the occurrence of the entanglement cycle. Remarkably, synthetic gauge fields emerge as potent regulators of this entanglement cycle. Our results uncover the profound implications of synthetic gauge fields on quantum tunneling processes within many-body systems, which hold promising potential applications for engineering atomtronic devices and controlling quantum entanglement.

Correlated Atomic Dynamics in a CuZrAl Liquid Seen in Real Space and Time Using Time-of-Flight Inelastic Neutron Scattering Studies

When examined at the nanometer length scale, metallic liquids exhibit extensive ordering. Bonding enthalpies are balanced against entropic tendencies resulting in a rich complicated behavior that leads to clustering that depends on temperature but evolves on picosecond time scales. The structural organization of metallic liquids affects their thermophysical properties, such as viscosity and density, thus influencing the ability of a metallic liquid to form useful technological phases, such as metallic glasses. The time-dependent pair correlation function (the Van Hove function) was determined for metallic-glass forming Cu49Zr45Al6 at 1060 °C from time-of-flight inelastic neutron scattering measurements made using the Neutron Electrostatic Levitation facility at the Spallation Neutron Source. The time for changes in local atomic connectivity, which is the timescale of atomic ordering, was determined by examining the decay of the nearest neighbor peak. The results of rigorous statistical analyses were used to distinguish between competing models of ordering, suggesting that a stretched exponential model of coordination number change is valid for this system.

Multi-colour cascaded wave-mixing for studying atomic and molecular dynamics

Multi-colour cascaded wave-mixing for studying atomic and molecular dynamics

Atom's Dynamics and Crystal Structure: An Ordinal Pattern Method.

The ubiquitous nature of thermal fluctuations poses a limitation on the identification of crystal structures. However, the trajectory of an atom carries a fingerprint of its surroundings. This rationalizes the search for a method that can determine the local atomic configuration via the analysis of the movement of an individual atom. Here, we report, while using molecular modeling, how a statistical analysis of a single-atom speed trajectory, represented by ordinal patterns, distinguishes between actual crystal structures. Using the Shannon entropy of ordinal patterns enabled discernment of the studied high-pressure silicon phases. Identification of the atoms occupying the 2(c) and 6(f) Wyckoff positions of the r8 crystal revealed an increase in the developed method's accuracy with trajectory length. The proposed concept of studying the structure of crystals offers new opportunities in solid-solid phase transformation studies.

Elucidating the Oxidation Process and Enhanced Stability of Black Phosphorus through NTCDA Passivation: A Molecular Dynamics Study

AbstractUnderstanding the oxidation mechanisms of black phosphorus (BP) at the atomic scale is essential for developing effective passivation strategies to enhance its stability in ambient conditions. To explore this, the effects of O2 and H2O molecules on BP layers are elucidated using reactive force field (ReaxFF) molecular dynamics simulations at constant concentrations of molecules and room temperature. As a potential solution, the passivation efficacy of 1,4,5,8‐naphthalenetetracarboxylic dianhydride (NTCDA) is evaluated. The initial oxidation processes are analyzed through atomic structural changes, charge dynamics, and radial distribution functions. Moreover, the thickness of the oxidized BP layers is quantitatively determined. Results show that elevated O2 concentrations significantly accelerate oxidation and increase the thickness of the oxidized layers, while H2O has a weaker influence. The interaction between O⁻ and H⁺ ions in H2O reduces its interaction with BP, but O2 molecules cause H2O to become negatively charged, allowing it to interact with P⁺ ions. Importantly, passivating BP with NTCDA effectively mitigates oxidation, creating a protective layer that repels O2 molecules. Ultimately, this study reveals the initial oxidation and passivation processes of BP layers, offering crucial theoretical insights to guide experimental methods and practical applications in semiconductor devices.

Effects of Temperature Fluctuations on Surface Mobility of Atomic Steps and Oxidation Dynamics in High-Temperature Alloys.

In contrast to the traditional perspective that thermal fluctuations are insignificant in surface dynamics, here we report their influence on surface reaction dynamics. Using real-time low-energy electron microscopy imaging of NiAl(100) under both vacuum and O2 atmospheres, we demonstrate that transient temperature variations substantially alter the direction of atom diffusion between the surface and bulk, leading to markedly different oxidation outcomes. During heating, substantial outward diffusion of atoms from the bulk to the surface results in step growth. Conversely, cooling induces considerable inward diffusion of adatoms, producing a distinct oxide morphology. In both scenarios, initially formed oxide islands impede local atomic step mobility, thereby increasing step length due to mass transfer between the surface and bulk, with atomic steps acting as adatom sinks during heating and sources during cooling. Furthermore, we show that this pinning effect on atomic step mobility can be mitigated by applying persistent temperature fluctuations. Understanding these nuances is vital for accurately predicting and dynamically manipulating the performance of active materials in various chemical processes under transient thermal conditions.

Trajectory analysis of anomalous dynamics in optical lattice.

We apply the trajectory formulation to analyze the anomalous dynamics of cold atoms in an optical lattice. The phase space probability density function of cold atoms, their dynamics, and the mechanism of dynamic evolution from an initial Gaussian distribution to a power-law distribution are analyzed. The results of the trajectory formulation are in good agreement with the previously reported experimental results for the exponent of position variance for a long time and the position-momentum correlation. The self-similar natures of trajectories in phase space are found for Lévy distributions. Our results unify the raw moments that can be expressed as the summation of a number of independent, identically distributed variables and the anomalous dynamics, which holds promise for an intuitive interpretation anomalous behavior and their kinetic mechanisms from initial Gaussian to anomalous distributions for a long time.

Ultrafast X-ray induced damage and nonthermal melting in cadmium sulfide.

Cadmium sulfide is a valuable material for solar cells, photovoltaic, and radiation detectors. It is thus important to evaluate the material damage mechanisms and damage threshold in response to irradiation. Here, we simulate the ultrafast XUV/X-ray irradiation of CdS with the combined model, XTANT-3. It accounts for nonequilibrium electronic and atomic dynamics, nonadiabatic coupling between the two systems, nonthermal melting and bond breaking due to electronic excitation. We find that the two phases of CdS, zinc blende and wurtzite, demonstrate very close damage threshold dose of ∼0.4-0.5 eV per atom. The damage is mainly thermal, whereas with increase of the dose, nonthermal effects begin to dominate leading to nonthermal melting. The transient disordered state is a high-density liquid, which may be semiconducting or metallic depending on the dose. Later recrystallization may recover the material back to the crystalline phase, or at high doses create an amorphous phase with variable bandgap. The revealed effects may potentially allow for controllable tuning of the band gap via laser irradiation of CdS.

Changing the amyloid nucleation process using small molecules and substrates: a way to build two-dimensional materials.

The assembly of two-dimensional (2D) materials on substrates presents a wide range of potential applications in nanomaterials. However, there is limited information available in the literature regarding the tunable nucleation process in molecular assembly. In this paper, a neurodegenerative disease-related short peptide and a small molecule named Fast Green (FG) were selected for their binding affinity with mica/highly oriented pyrolytic graphite (HOPG) substrates. Based on atomic force microscopy (AFM) and molecular dynamics (MD) simulation, we investigated the control of 2D assemblies. By tuning FG small molecules and substrates, the assemblies grew epitaxially from nanosheets to nanofilms on mica and highly ordered nanofilaments on HOPG substrates. Notably, the nuclei formed an orderly array without a critical size or lag phase in the presence of FG molecules on the HOPG substrate, facilitating a quicker co-assembly of ordered filaments compared to bulk conditions. Our MD simulations further demonstrated that the interaction between Aβ16-22 molecules and the HOPG substrate was primarily due to π-π interactions between aromatic rings, which led to the formation of single-layer filaments by lying on the surface of HOPG. Additionally, parallel π-π stacking acted as the primary force to inhibit the aggregation of peptides into fibrils. Overall, our results provide a strategy for modulating the interaction of amyloid peptides with small molecules and substrates in the assembly of 2D nanomaterials.

Quantifying sulfhydryl oxidation rates using Ellman's procedure

Ellman's procedure has been used to study the oxidation rates of cysteine (CSH) and glutathione (GSH) in aqueous solutions, and it was reported that, for CSH, the number of sulfhydryl molecules not oxidized became zero at a specific time, called tc, where it was reported to be finite. We point out that under very general considerations, we should observe tc to be unbounded, and it becomes infinite. The reason is that as the process of forming a disulfide bond proceeds, the probability of two CSH molecules finding each other eventually becomes vanishingly small so that the number of unoxidized molecules approach zero only as tc becomes infinite. We used a Smoluchowski equation to model the process of disulfide bond formation in order to understand how a finite tc can be observed. In addition, atomic scale molecular dynamics simulations were carried out in order to study the spatial distributions of CSH and GSH in aqueous solutions. It was found that electrostatic interactions bring about aggregation of these molecules, and we conclude that this aggregation “hides” unoxidized sufhydryl moieties from interacting with (5,5′-dithiobis-(2-nitrobenzoic acid) DTNB of Ellman's reagent, thereby remaining undetected. It will thus appear as if the number of unoxidized moieties has become zero. In order that all sulfhydryl moieties be detected, it is necessary to disrupt the aggregate, as has been carried out for proteins, so as to expose those moieties to be oxidized and be detected.

Tracking Li atoms in real-time with ultra-fast NMR simulations.

We present for the first time a multiscale machine learning approach to jointly simulate atomic structure and dynamics with the corresponding solid state Nuclear Magnetic Resonance (ssNMR) observables. We study the use-case of spin-alignment echo (SAE) NMR for exploring Li-ion diffusion within the solid state electrolyte material Li3PS4 (LPS) by calculating quadrupolar frequencies of 7Li. SAE NMR probes long-range dynamics down to microsecond-timescale hopping processes. Therefore only a few machine learning force field schemes are able to capture the time- and length scales required for accurate comparison with experimental results. By using a new class of machine learning interatomic potentials, known as ultra-fast potentials (UFPs), we are able to efficiently access timescales beyond the microsecond regime. In tandem, we have developed a machine learning model for predicting the full 7Li electric field gradient (EFG) tensors in LPS. By combining the long timescale trajectories from the UFP with our model for 7Li EFG tensors, we are able to extract the autocorrelation function (ACF) for 7Li quadrupolar frequencies during Li diffusion. We extract the decay constants from the ACF for both crystalline β-LPS and amorphous LPS, and find that the predicted Li hopping rates are on the same order of magnitude as those predicted from the Li dynamics. This demonstrates the potential for machine learning to finally make predictions on experimentally relevant timescales and temperatures, and opens a new avenue of NMR crystallography: using machine learning dynamical NMR simulations for accessing polycrystalline and glass ceramic materials.

Predicting dynamics from structure in a sodium silicate glass

Understanding the dynamics of atoms in glasses is crucial for unraveling the origin of relaxation and the glass transition as well as predicting transport properties. However, identifying the structural features controlling atom dynamics in glasses remains challenging. Recently, machine learning models based on graph neural networks (GNNs) have successfully been used to predict future dynamics, but these prior studies focused primarily on model systems such as Kob–Andersen-type Lennard–Jones mixtures. This study investigates the use of local descriptors, GNN models, and molecular dynamics simulations to clarify the atomics dynamics in a realistic glass system (sodium silicate) across varying time scales. By harnessing the capabilities of different structural representations, we develop effective models for predicting the dynamics of sodium ions within the glassy silicate network, based solely on the initial atom positions. We further demonstrate the viability of our approach through comparison to previously proposed methods. Our findings pave the way for designing new glass formulations with tailored dynamical properties (e.g., as glassy electrolytes for batteries).Impact statementGlass science has long grappled with understanding the fundamental nature and origin of glassy dynamics. The governing principles of atomic dynamics in glasses remain elusive as it is not obvious what to look for in the glass structure. While previous studies have focused on simplified model systems, we demonstrate for the first time that machine learning models can be used to accurately predict multi-time scale atomic dynamics in a complex oxide glass (sodium silicate) from the static atomic structure. By comparing different machine learning architectures, we establish that graph neural networks outperform conventional structural descriptors for dynamics prediction, with graph representations being able to effectively capture the complex multibody correlations that govern dynamics. Our findings show that the future dynamics in oxide glasses on time scales up to nanoseconds are at least partially encoded in the initial glassy configuration itself, showing that glassy dynamics is not a completely stochastic process. The capability to predict dynamics from structure has major implications as it could provide new tools for rational design of glassy materials with tailored dynamical properties and functionalities, possibly accelerating development of advanced glasses for applications in areas such as solid-state batteries and nuclear waste immobilization.Graphical abstractMachine learning models based on local descriptors and graph neural networks can be used to predict the future dynamics of Na ions in sodium silicate glass structures