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We propose a trajectory-based quasi-classical method for approximating dynamics in condensed phase systems. Building upon the previously developed optimized mean trajectory approximation that has been used to compute linear and nonlinear spectra, we borrow some ideas from filtering trajectory methods to obtain a novel semiclassical method for the dynamical propagation of density matrices. This new approximation is tested rigorously against standard multistate electronic models, spin-boson models, and models of the Fenna-Matthews-Olson complex. For dissipative systems, the current method is significantly better or as good as many other semiclassical methods available, especially at low temperatures and for off-diagonal density matrix elements, whereas for scattering models, the current method bears similar limitations as mean-field propagation schemes. All results are tested against the numerically exact hierarchical equations of motion method. The new method shows excellent agreement across various parameter regimes with numerically exact results, highlighting the robustness and accuracy of our approach.

Chemical vapor deposition (CVD) is a crucial technique to prepare high-quality graphene because of its controllability. In the research, we perform a systematic computational fluid dynamics numerical investigation on the effect of gas-phase reaction dynamics on the graphene growth in a horizontal tube CVD reactor. The research results indicate that the gas-phase chemical reactions in the CVD reactor are in a nonequilibrium state, as evidenced by the comparison of species mole fraction distributions during the CVD process and under chemical equilibrium conditions. The effect of gas-phase reaction dynamics on the deposition rate of graphene under different conditions is studied, and our research shows that the main causes of change in graphene growth rates under different conditions are gas-phase reaction dynamics and active species transport. The results of numerical simulation agree well with the experimental phenomena. The research results also indicate that, for methane, the main limiting factor of graphene growth is the surface kinetic reaction rate. Conversely, for active species, the main limiting factor of graphene growth is species transport. Our research suggests that the growth rate of graphene can be regulated from the perspective of the gas reaction mechanism. This method has theoretical guiding significance and can be extended to the preparation of large-area graphene. [ABSTRACT FROM AUTHOR]

DNA supercoils are generated in genome regulation processes such as transcription and replication and provide mechanical feedback to such processes. Under tension, a DNA supercoil can present a coexistence state of plectonemic and stretched phases. Experiments have revealed the dynamic behaviors of plectonemes, e.g., diffusion, nucleation, and hopping. To represent these dynamics with conformational changes, we demonstrated first the fast dynamics on the DNA to reach torque equilibrium within the plectonemic and stretched phases, and then identified the two-phase boundaries as collective slow variables to describe the essential dynamics. According to the timescale separation demonstrated here, we developed a two-phase model on the dynamics of DNA supercoiling, which can capture physiologically relevant events across timescales of several orders of magnitudes. In this model, we systematically characterized the slow dynamics between the two phases and compared the numerical results with those from the DNA polymer physics-based worm-like chain model. The supercoiling dynamics, including the nucleation, diffusion, and hopping of plectonemes, have been well represented and reproduced, using the two-phase dynamic model, at trivial computational costs. Our current developments, therefore, can be implemented to explore multiscale physical mechanisms of the DNA supercoiling-dependent physiological processes.

Background and Objectives: the coupling of EEG signals during an epileptic seizure in patients during coma is being studied. Materials and Methods: the analysis of the applicability of the method of detecting the interaction between oscillatory systems based on the phase dynamics modeling to EEG signals during an epilepsy seizure in comatose patients is carried out. Results: a method of preliminary filtering of EEG signals has been proposed and the values of the method parameters have been selected, which allow obtaining reliable estimates of directional coupling at a significance level of 0.05. As an example, the analysis of the couplings between EEG signals of two patients with the mentioned pathologies was carried out using the method of the coupling estimation developed in this work.

In floating catalyst chemical vapor deposition (FCCVD), high-aspect-ratio carbon nanotubes (CNTs) are produced in the gas phase at high number concentrations and undergo collision and agglomeration, eventually giving rise to a macroscale aerogel, enabling functional material forms such as fibers or mats to be obtained directly from the synthesis process. The self-assembly behavior between high-aspect-ratio CNTs dictates the resulting morphology at the nanoscale and subsequently the bulk properties of the CNT product. Reorientation between CNTs after collision is a critical step that results in bundle formation and precedes aerogel formation. However, it has been challenging to study the phenomenon with existing methods as it spans multiple time and length scales. In this study, a physics-based semi-analytical model was developed to study the gas-phase reorientation dynamics of high-aspect-ratio CNTs and their bundles, with ±10% accuracy compared with mesoscale molecular dynamics simulations, but at <0.1% the computational cost. It was revealed that the reorientation time scale is dictated by the interplay among the van der Waals potential, drag, and the geometric configuration of CNTs upon collision. This then allows the time scale of reorientation (i.e., bundle formation) to be compared with other gas-phase dynamics in a typical FCCVD reactor and offers new insights into the self-assembly behavior of 1D nanoparticles in the gas phase.

Real-time detection of intermediate species and final products at the surface and near-surface in interfacial solid-gas reactions is critical for an accurate understanding of heterogeneous reaction mechanisms. In this article, an experimental method that can simultaneously monitor the ultrafast dynamics at the surface and above the surface in photoinduced heterogeneous reactions is presented. This method relies on a combination of mass spectrometry and femtosecond pump-probe spectroscopy. As a model system, the photoinduced reaction of methyl iodide on and above a cerium oxide surface is investigated. The species that are simultaneously detected from the surface and gas-phase present distinct features in the mass spectra, such as a sharp peak followed by an adjacent broad shoulder. The sharp peak is attributed to the species detected from the surface, while the broad shoulder is due to the detection of gas-phase species above the surface, as confirmed by multiple experiments. By monitoring the evolution of the sharp peak and broad shoulder as a function of the pump-probe time delay, transient signals are obtained that describe the ultrafast photoinduced reaction dynamics of methyl iodide on the surface and in the gas-phase. Finally, SimION simulations are performed to confirm the origin of the ions produced on the surface and in the gas-phase., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Characterizing the brain’s dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain’s response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time–frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

We review an approach for reconstructing oscillatory networks’ undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network’s coupling matrix in the first approximation in the coupling strength.

The phase reduction approach has manifested its efficacy in investigating synchronization behaviors in limit-cycle oscillators. However, spatial distributions of the phase value on the limit cycle may lead to illusions of synchronizations for oscillators close to bifurcations. In this paper, we compared the phase sensitivity function in the spatial domain and time domain for oscillators close to saddle-node homoclinic (SNH) bifurcation, also known as saddle-node bifurcation on an invariant circle. It was found that the phase sensitivity function in the spatial domain can show the phase accumulation feature on the limit cycle, which can be ignored in the phase sensitivity function in the time domain. As an example, the synchronization distributions of uncoupled SNH oscillators driven by common and independent noises were investigated, where the space-dependent coupling function was considered on common noise. These results shed some light on the phase dynamics of oscillators close to bifurcations.

Light is the primary signal that calibrates circadian neural circuits and thus coordinates daily physiological and behavioral rhythms with solar entrainment cues. Drosophila and mammalian circadian circuits consist of diverse populations of cellular oscillators that exhibit a wide range of dynamic light responses, periods, phases, and degrees of synchrony. How heterogeneous circadian circuits can generate robust physiological rhythms while remaining flexible enough to respond to synchronizing stimuli has long remained enigmatic. Cryptochrome is a short-wavelength photoreceptor that is endogenously expressed in approximately half of Drosophila circadian neurons. In a previous study, physiological light response was measured using real-time bioluminescence recordings in Drosophila whole-brain explants, which remain intrinsically light-sensitive. Here we apply analysis of real-time bioluminescence experimental data to show detailed dynamic ensemble representations of whole circadian circuit light entrainment at single neuron resolution. Organotypic whole-brain explants were either maintained in constant darkness (DD) for 6 days or exposed to a phase-advancing light pulse on the second day. We find that stronger circadian oscillators support robust overall circuit rhythmicity in DD, whereas weaker oscillators can be pushed toward transient desynchrony and damped amplitude to facilitate a new state of phase-shifted network synchrony. Additionally, we use mathematical modeling to examine how a network composed of distinct oscillator types can give rise to complex dynamic signatures in DD conditions and in response to simulated light pulses. Simulations suggest that complementary coupling mechanisms and a combination of strong and weak oscillators may enable a robust yet flexible circadian network that promotes both synchrony and entrainment. A more complete understanding of how the properties of oscillators and their signaling mechanisms facilitate their distinct roles in light entrainment may allow us to direct and augment the circadian system to speed recovery from jet lag, shift work, and seasonal affective disorder.

Both straight and curved carbon fibers are widely used in various commercial gas diffusion layer (GDL) fabrications. The effect of the different carbon fiber curvatures on two-phase flow dynamics within the cathode GDLs of proton exchange membrane fuel cells remains unclear. In this study, we investigate liquid transport in three types of GDLs with varying fiber curvatures using the two-phase volume of fluid simulations in OpenFOAM. For the first time, a rod periodic surface model is combined with a layer-by-layer fiber stacking strategy, to stochastically reconstruct GDL structures while incorporating crucial parameters from physical (experimental) GDLs. A grid independence study and model validation are conducted. Following pore network analysis of pore size distribution and connectivity, we study the time-varying GDL total and local water saturation and capillary pressure. Despite maintaining similar layer and bulk porosity, increased fiber curvature enhances pore connectivity but raises water saturation and capillary pressure, increasing the risk of flooding. Additionally, droplets in gas channels with straight-fiber GDLs are larger and have slower movement than those in curved-fiber GDLs. Fiber curvature inversely affects drainage capacity in GDLs and connected channels. With comparable water saturation and capillary pressure, curved-fiber GDLs exhibit lower discrepancies, suggesting improved uniformity in water distribution. [Display omitted] • Curved and straight fiber GDLs are reconstructed with similar bulk and layer porosity. • Increasing fiber curvature enhances pore connectivity and pore quantity. • GDL water saturation and capillary pressure increase with an increased fiber curvature. • Increasing the amount of small pores results in stronger water spreading within GDLs. • Detached droplets in GCs have smaller sizes and longer liquid bridge when using curved-fiber GDLs. [ABSTRACT FROM AUTHOR]

By measuring the current–voltage characteristics and the switching current distributions as a function of temperature, we have investigated the phase dynamics of Al tunnel ferromagnetic Josephson junctions (JJs), designed to fall in the typical range of parameters of state-of-the-art transmons, providing evidence of phase diffusion processes. The comparison with the experimental outcomes on non-magnetic JJs with nominally the same electrodynamical parameters demonstrates that the introduction of ferromagnetic barriers does not cause any sizeable detrimental effect and supports the notion of including tunnel ferromagnetic JJs in qubit architectures. [ABSTRACT FROM AUTHOR]

We propose a method to reconstruct the phase dynamics in rhythmical interacting systems from macroscopic responses to weak inputs by developing linear and nonlinear response theories, which predict the responses in a given system. By solving an inverse problem, the method infers an unknown system: the natural frequency distribution, the coupling function, and the time delay which is inevitable in real systems. In contrast to previous methods, our method requires neither strong invasiveness nor microscopic observations. We demonstrate that the method reconstructs two phase systems from observed responses accurately. The qualitative methodological advantages demonstrated by our quantitative numerical examinations suggest its broad applicability in various fields, including brain systems, which are often observed through macroscopic signals such as electroencephalograms and functional magnetic response imaging.

The third-order response lies at the heart of simulating and interpreting nonlinear spectroscopies ranging from two-dimensional infrared (2D-IR) to 2D electronic (2D-ES), and 2D sum frequency generation (2D-SFG). The extra time and frequency dimensions in these spectroscopic techniques provide access to rich information on the electronic and vibrational states present, the coupling between them, and the resulting rates at which they exchange energy that are obscured in linear spectroscopy, particularly for condensed phase systems that usually contain many overlapping features. While the exact quantum expression for the third-order response is well established, it is incompatible with the methods that are practical for calculating the atomistic dynamics of large condensed phase systems. These methods, which include both classical mechanics and quantum dynamics methods that retain quantum statistical properties while obeying the symmetries of classical dynamics, such as LSC-IVR, centroid molecular dynamics, and Ring Polymer Molecular Dynamics (RPMD), naturally provide short-time approximations to the multi-time symmetrized Kubo transformed correlation function. Here, we show how the third-order response can be formulated in terms of equilibrium symmetrized Kubo transformed correlation functions. We demonstrate the utility and accuracy of our approach by showing how it can be used to obtain the third-order response of a series of model systems using both classical dynamics and RPMD. In particular, we show that this approach captures features such as anharmonically induced vertical splittings and peak shifts while providing a physically transparent framework for understanding multidimensional spectroscopies. [ABSTRACT FROM AUTHOR]

The roles of wave phase structure and evolution in zonal flow dynamics are investigated. Previous work (Guo, Diamond; 2016) has noted that wave phase curvature is sufficient to produce a gradient in the Reynolds force and thus to drive zonal flow formation — even for turbulence of homogeneous fluctuation intensity. In this study, we investigate the energetics and nonlinear evolution of the process. For energetics, refraction (i.e. changes in phase gradient due to Z.F. shear) allows wave energy — and thus the ZF — to evolve at fixed wave amplitude. Using the phase evolution equation derived for the Hasegawa-Mima model, we prove energy conservation between waves and ZF, for fixed wave potential intensity. By separating the mesoscale phase into mean and fluctuation, we show that phase evolution induces ZF formation due to phase gradient shocks. Then, we investigate how linear and nonlinear feedback of the gradient affect the synchronization of the shock structure. Finally, we consider dynamic amplitude to explore the full predator-prey relationship between ZF (predator) and drift waves (prey).