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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]

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.

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.

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]

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. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]

Noise can induce coherent oscillations in excitable systems without periodic orbits. Here, we establish a method to derive a hybrid system approximating the noise-induced coherent oscillations in excitable systems and further perform phase reduction of the hybrid system to derive an effective, dimensionality-reduced phase equation. We apply the reduced phase model to a periodically forced excitable system and two-coupled excitable systems, both undergoing noise-induced oscillations. The reduced phase model can quantitatively predict the entrainment of a single system to the periodic force and the mutual synchronization of two coupled systems, including the phase slipping behavior due to noise, as verified by Monte Carlo simulations. The derived phase model gives a simple and efficient description of noise-induced oscillations and can be applied to the analysis of more general cases.

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. [ABSTRACT FROM AUTHOR]