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We consider synchronization properties of arrays of spin-torque nano-oscillators coupled via an RC load. We show that while the fully synchronized state of identical oscillators may be locally stable in some parameter range, this synchrony is not globally attracting. Instead, regimes of different levels of compositional complexity are observed. These include chimera states (a part of the array forms a cluster while other units are desynchronized), clustered chimeras (several clusters plus desynchronized oscillators), cluster state (all oscillators form several clusters), and partial synchronization (no clusters but a nonvanishing mean field). Dynamically, these states are also complex, demonstrating irregular and close to quasiperiodic modulation. Remarkably, when heterogeneity of spin-torque oscillators is taken into account, dynamical complexity even increases: close to the onset of a macroscopic mean field, the dynamics of this field is rather irregular.
We consider synchronization properties of arrays of spin-torque nano-oscillators coupled via an RC load. We show that while the fully synchronized state of identical oscillators may be locally stable in some parameter range, this synchrony is not globally attracting. Instead, regimes of different levels of compositional complexity are observed. These include chimera states (a part of the array forms a cluster while other units are desynchronized), clustered chimeras (several clusters plus desynchronized oscillators), cluster state (all oscillators form several clusters), and partial synchronization (no clusters but a nonvanishing mean field). Dynamically, these states are also complex, demonstrating irregular and close to quasiperiodic modulation. Remarkably, when heterogeneity of spin-torque oscillators is taken into account, dynamical complexity even increases: close to the onset of a macroscopic mean field, the dynamics of this field is rather irregular.
While the performance of laboratory-scale organic solar cells (OSCs) continues to grow, development of high efficiency large area OSCs remains a big challenge. Although a few attempts to produce large area organic solar cells (OSCs) have been reported, there are still challenges on the way to realizing efficient module devices, such as the low compatibility of the thickness-sensitive active layer with large area coating techniques, the frequent need for toxic solvents and tedious optimization processes used during device fabrication. In this work, highly efficient thickness-insensitive OSCs based on PTB7-Th:PC71BM that processed with single-component green solvent 2-methylanisole are presented, in which both junction thickness limitation and solvent toxicity issues are simultaneously addressed. Careful investigation reveals that this green solvent prevents the evolution of PC71BM into large area clusters resulting in reduced charge carrier recombination, and largely eliminates trapping centers, and thus improves the thickness tolerance of the films. These findings enable us to address the scalability and solvent toxicity issues and to fabricate a 16 cm(2) OSC with doctor-blade coating with a state-of-the-art power conversion efficiency of 7.5% using green solvent.
Many studies of synchronization properties of coupled oscillators, based on the classical Kuramoto approach, focus on ensembles coupled via a mean field. Here we introduce a setup of Kuramoto-type phase oscillators coupled via two mean fields. We derive stability properties of the incoherent state and find traveling wave solutions with different locking patterns; stability properties of these waves are found numerically. Mostly nontrivial states appear when the two fields compete, i.e. one tends to synchronize oscillators while the other one desynchronizes them. Here we identify normal branches which bifurcate from the incoherent state in a usual way, and anomalous branches, appearance of which cannot be described as a bifurcation. Furthermore, hybrid branches combining properties of both are described. In the situations where no stable traveling wave exists, modulated quasiperiodic in time dynamics is observed. Our results indicate that a competition between two coupling channels can lead to a complex system behavior, providing a potential generalized framework for understanding of complex phenomena in natural oscillatory systems.
We present the PINE (Plasma density in the Inner magnetosphere Neural network‐based Empirical) model ‐ a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural‐network‐based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model‐predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM‐H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.