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Organic photovoltaics based on non-fullerene acceptors (NFAs) show record efficiency of 16 to 17% and increased photovoltage owing to the low driving force for interfacial charge-transfer. However, the low driving force potentially slows down charge generation, leading to a tradeoff between voltage and current. Here, we disentangle the intrinsic charge-transfer rates from morphology-dependent exciton diffusion for a series of polymer:NFA systems. Moreover, we establish the influence of the interfacial energetics on the electron and hole transfer rates separately. We demonstrate that charge-transfer timescales remain at a few hundred femtoseconds even at near-zero driving force, which is consistent with the rates predicted by Marcus theory in the normal region, at moderate electronic coupling and at low re-organization energy. Thus, in the design of highly efficient devices, the energy offset at the donor:acceptor interface can be minimized without jeopardizing the charge-transfer rate and without concerns about a current-voltage tradeoff.
The thermal behavior of poly(methoxydiethylenglycol acrylate) (PMDEGA) is studied in thin hydrogel films on solid supports and is compared with the behavior in aqueous solution. The PMDEGA hydrogel film thickness is varied from 2 to 422 nm. Initially, these films are homogenous, as measured with optical microscopy, atomic force microscopy, X-ray reflectivity, and grazing-incidence small-angle X-ray scattering (GISAXS). However, they tend to de-wet when stored under ambient conditions. Along the surface normal, no long-ranged correlations between substrate and film surface are detected with GISAXS, due to the high mobility of the polymer at room temperature. The swelling of the hydrogel films as a function of the water vapor pressure and the temperature are probed for saturated water vapor pressures between 2,380 and 3,170 Pa. While the swelling capability is found to increase with water vapor pressure, swelling in dependence on the temperature revealed a collapse phase transition of a lower critical solution temperature type. The transition temperature decreases from 40.6 A degrees C to 36.6 A degrees C with increasing film thickness, but is independent of the thickness for very thin films below a thickness of 40 nm. The observed transition temperature range compares well with the cloud points observed in dilute (0.1 wt.%) and semi-dilute (5 wt.%) solution which decrease from 45 A degrees C to 39 A degrees C with increasing concentration.
We study the properties of classical and quantum strongly nonlinear chains by means of extensive numerical simulations. Due to strong nonlinearity, the classical dynamics of such chains remains chaotic at arbitrarily low energies. We show that the collective excitations of classical chains are described by sound waves whose decay rate scales algebraically with the wave number with a generic exponent value. The properties of the quantum chains are studied by the quantum Monte Carlo method and it is found that the low-energy excitations are well described by effective phonon modes with the sound velocity dependent on an effective Planck constant. Our results show that at low energies the quantum effects lead to a suppression of chaos and drive the system to a quasi-integrable regime of effective phonon modes.
We investigate the transition from incoherence to global collective motion in a three-dimensional swarming model of agents with helical trajectories, subject to noise and global coupling. Without noise this model was recently proposed as a generalization of the Kuramoto model and it was found that alignment of the velocities occurs discontinuously for arbitrarily small attractive coupling. Adding noise to the system resolves this singular limit and leads to a continuous transition, either to a directed collective motion or to center-of-mass rotations.
We show that "stochastic bursting" is observed in a ring of unidirectional delay-coupled noisy excitable systems, thanks to the combinational action of time-delayed coupling and noise. Under the approximation of timescale separation, i.e., when the time delays in each connection are much larger than the characteristic duration of the spikes, the observed rather coherent spike pattern can be described by an idealized coupled point processwith a leader-follower relationship. We derive analytically the statistics of the spikes in each unit, the pairwise correlations between any two units, and the spectrum of the total output from the network. Theory is in good agreement with the simulations with a network of theta-neurons. Published under license by AIP Publishing.
We show that a combined action of noise and delayed feedback on an excitable theta-neuron leads to rather coherent stochastic bursting. An idealized point process, valid if the characteristic timescales in the problem are well separated, is used to describe statistical properties such as the power spectral density and the interspike interval distribution. We show how the main parameters of the point process, the spontaneous excitation rate, and the probability to induce a spike during the delay action can be calculated from the solutions of a stationary and a forced Fokker-Planck equation.
We present the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm for automatic inference of the electron number density from plasma wave measurements made on board NASA's Van Allen Probes mission. A feedforward neural network is developed to determine the upper hybrid resonance frequency, fuhr, from electric field measurements, which is then used to calculate the electron number density. In previous missions, the plasma resonance bands were manually identified, and there have been few attempts to do robust, routine automated detections. We describe the design and implementation of the algorithm and perform an initial analysis of the resulting electron number density distribution obtained by applying NURD to 2.5 years of data collected with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite of the Van Allen Probes mission. Densities obtained by NURD are compared to those obtained by another recently developed automated technique and also to an existing empirical plasmasphere and trough density model.
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.
Abstract
In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network‐based models capture the large‐scale dynamics of the plasmasphere, such as plume formation and erosion of the plasmasphere on the nightside. However, their performance depends strongly on the availability of training data. When the data coverage is limited or non‐existent, as occurs during geomagnetic storms, the performance of NNs significantly decreases, as networks inherently cannot learn from the limited number of examples. This limitation can be overcome by employing physics‐based modeling during strong geomagnetic storms. Physics‐based models show a stable performance during periods of disturbed geomagnetic activity if they are correctly initialized and configured. In this study, we illustrate how to combine the neural network‐ and physics‐based models of the plasmasphere in an optimal way by using data assimilation. The proposed approach utilizes advantages of both neural network‐ and physics‐based modeling and produces global plasma density reconstructions for both quiet and disturbed geomagnetic activity, including extreme geomagnetic storms. We validate the models quantitatively by comparing their output to the in‐situ density measurements from RBSP‐A for an 18‐month out‐of‐sample period from June 30, 2016 to January 01, 2018 and computing performance metrics. To validate the global density reconstructions qualitatively, we compare them to the IMAGE EUV images of the He+ particle distribution in the Earth's plasmasphere for a number of events in the past, including the Halloween storm in 2003.
Bimetallic nanostructures comprising plasmonic and catalytic components have recently emerged as a promising approach to generate a new type of photo-enhanced nanoreactors. Most designs however concentrate on plasmon-induced charge separation, leaving photo-generated heat as a side product.
This work presents a photoreactor based on Au-Pd nanorods with an optimized photothermal conversion, which aims to effectively utilize the photo-generated heat to increase the rate of Pd-catalyzed reactions. Dumbbell-shaped Au nanorods were fabricated via a seed-mediated growth method using binary surfactants. Pd clusters were selectively grown at the tips of the Au nanorods, using the zeta potential as a new synthetic parameter to indicate the surfactant remaining on the nanorod surface.
The photothermal conversion of the Au-Pd nanorods was improved with a thin layer of polydopamine (PDA) or TiO2.
As a result, a 60% higher temperature increment of the dispersion compared to that for bare Au rods at the same light intensity and particle density could be achieved.
The catalytic performance of the coated particles was then tested using the reduction of 4-nitrophenol as the model reaction. Under light, the PDA-coated Au-Pd nanorods exhibited an improved catalytic activity, increasing the reaction rate by a factor 3.
An analysis of the activation energy confirmed the photoheating effect to be the dominant mechanism accelerating the reaction. Thus, the increased photothermal heating is responsible for the reaction acceleration.
Interestingly, the same analysis shows a roughly 10% higher reaction rate for particles under illumination compared to under dark heating, possibly implying a crucial role of localized heat gradients at the particle surface.
Finally, the coating thickness was identified as an essential parameter determining the photothermal conversion efficiency and the reaction acceleration.