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2D Ruddlesden-Popper perovskite (RPP) solar cells have excellent environmental stability. However, the power conversion efficiency (PCE) of RPP cells remains inferior to 3D perovskite-based cells. Herein, 2D (CH3(CH2)(3)NH3)(2)(CH3NH3)(n-1)PbnI3n+1 perovskite cells with different numbers of [PbI6](4-) sheets (n = 2-4) are analyzed. Photoluminescence quantum yield (PLQY) measurements show that nonradiative open-circuit voltage (V-OC) losses outweigh radiative losses in materials with n > 2. The n = 3 and n = 4 films exhibit a higher PLQY than the standard 3D methylammonium lead iodide perovskite although this is accompanied by increased interfacial recombination at the top perovskite/C-60 interface. This tradeoff results in a similar PLQY in all devices, including the n = 2 system where the perovskite bulk dominates the recombination properties of the cell. In most cases the quasi-Fermi level splitting matches the device V-OC within 20 meV, which indicates minimal recombination losses at the metal contacts. The results show that poor charge transport rather than exciton dissociation is the primary reason for the reduction in fill factor of the RPP devices. Optimized n = 4 RPP solar cells had PCEs of 13% with significant potential for further improvements.
Engineering the interface between the perovskite absorber and the charge-transporting layers has become an important method for improving the charge extraction and open-circuit voltage (V-OC) of hybrid perovskite solar cells. Conjugated polymers are particularly suited to form the hole-transporting layer, but their hydrophobicity renders it difficult to solution-process the perovskite absorber on top. Herein, oxygen plasma treatment is introduced as a simple means to change the surface energy and work function of hydrophobic polymer interlayers for use as p-contacts in perovskite solar cells. We find that upon oxygen plasma treatment, the hydrophobic surfaces of different prototypical p-type polymers became sufficiently hydrophilic to enable subsequent perovskite junction processing. In addition, the oxygen plasma treatment also increased the ionization potential of the polymer such that it became closer to the valance band energy of the perovskite. It was also found that the oxygen plasma treatment could increase the electrical conductivity of the p-type polymers, facilitating more efficient charge extraction. On the basis of this concept, inverted MAPbI(3) perovskite devices with different oxygen plasma-treated polymers such as P3HT, P3OT, polyTPD, or PTAA were fabricated with power conversion efficiencies of up to 19%.
Low cost, large area, lightweight, stretchable piezoelectric films, based on space-charge electret with a foam structure (i.e., ferroelectrets or piezoelectrets), have been fabricated by using commercially available irradiation cross-linked poly(propylene) (IXPP) foam sheets. Piezoelectric d(33) coefficients are as high as 100pCN(-1). The piezoelectric performance in such IXPP films is well preserved for repeated strains of less than 10%. Piezoelectric d(33) coefficients are frequency independent in the range from 2 to 100Hz. Such new class materials may be applied in sensory skins, smart clothing, bio-inspired systems, microenergy harvesters, and so on.
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.
Fast actuation speed, large-shape deformation and robust responsiveness are critical to synthetic soft actuators. A simultaneous optimization of all these aspects without trade-offs remains unresolved. Here we describe porous polymer actuators that bend in response to acetone vapour (24 kPa, 20 degrees C) at a speed of an order of magnitude faster than the state-of-the-art, coupled with a large-scale locomotion. They are meanwhile multi-responsive towards a variety of organic vapours in both the dry and wet states, thus distinctive from the traditional gel actuation systems that become inactive when dried. The actuator is easy-to-make and survives even after hydrothermal processing (200 degrees C, 24 h) and pressing-pressure (100 MPa) treatments. In addition, the beneficial responsiveness is transferable, being able to turn 'inert' objects into actuators through surface coating. This advanced actuator arises from the unique combination of porous morphology, gradient structure and the interaction between solvent molecules and actuator materials.
We present observations of three-dimensional magnetic power spectra in wavevector space to investigate the anisotropy and scalings of sub-Alfvenic solar wind turbulence at magnetohydrodynamic (MHD) scale using the Magnetospheric Multiscale spacecraft. The magnetic power distributions are organized in a new coordinate determined by wavevectors ((kappa) over cap) and background magnetic field ((b) over cap (0)) in Fourier space. This study utilizes two approaches to determine wavevectors: the singular value decomposition method and multispacecraft timing analysis. The combination of the two methods allows an examination of the properties of magnetic field fluctuations in terms of mode compositions without any spatiotemporal hypothesis. Observations show that fluctuations (delta B-perpendicular to 1) in the direction perpendicular to (kappa) over cap and (b) over cap (0) prominently cascade perpendicular to (b) over cap (0), and such anisotropy increases with wavenumbers. The reduced power spectra of 6.8 11 follow Goldreich-Sridhar scalings: (P) over cap (k(perpendicular to)) proportional to k(perpendicular to)(-5/3) and (P) over cap (k(parallel to)) proportional to k(parallel to)(-2). In contrast, fluctuations within the (k) over cap(b) over cap (0) plane show isotropic behaviors: perpendicular power distributions are approximately the same as parallel distributions. The reduced power spectra of fluctuations within the (k) over cap(b) over cap (0) plane follow the scalings (P) over cap (k(perpendicular to)) proportional to k(perpendicular to)(-3/2) and (P) over cap (k(parallel to)) proportional to k(parallel to)(-3/2). Comparing frequency-wavevector spectra with theoretical dispersion relations of MHD modes, we find that delta B-perpendicular to 1 are probably associated with Alfven modes. On the other hand, magnetic field fluctuations within the (k) over cap(b) over cap (0) plane more likely originate from fast modes based on their isotropic behaviors. The observations of anisotropy and scalings of different magnetic field components are consistent with the predictions of current compressible MHD theory. Moreover, for the Alfvenic component, the ratio of cascading time to the wave period is found to be a factor of a few, consistent with critical balance in the strong turbulence regime. These results are valuable for further studies of energy compositions of plasma turbulence and their effects on energetic particle transport.
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.
The plasmasphere is a dynamic region of cold, dense plasma surrounding the Earth. Its shape and size are highly susceptible to variations in solar and geomagnetic conditions. Having an accurate model of plasma density in the plasmasphere is important for GNSS navigation and for predicting hazardous effects of radiation in space on spacecraft. The distribution of cold plasma and its dynamic dependence on solar wind and geomagnetic conditions remain, however, poorly quantified. Existing empirical models of plasma density tend to be oversimplified as they are based on statistical averages over static parameters. Understanding the global dynamics of the plasmasphere using observations from space remains a challenge, as existing density measurements are sparse and limited to locations where satellites can provide in-situ observations. In this dissertation, we demonstrate how such sparse electron density measurements can be used to reconstruct the global electron density distribution in the plasmasphere and capture its dynamic dependence on solar wind and geomagnetic conditions.
First, we develop an automated algorithm to determine the electron density from in-situ measurements of the electric field on the Van Allen Probes spacecraft. In particular, we design a neural network to infer the upper hybrid resonance frequency from the dynamic spectrograms obtained with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite, which is then used to calculate the electron number density. The developed Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm is applied to more than four years of EMFISIS measurements to produce the publicly available electron density data set.
We utilize the obtained electron density data set to develop a new global model of plasma density by employing a neural network-based modeling approach. In addition to the location, the model takes the time history of geomagnetic indices and location as inputs, and produces electron density in the equatorial plane as an output. It is extensively validated using in-situ density measurements from the Van Allen Probes mission, and also by comparing the predicted global evolution of the plasmasphere with the global IMAGE EUV images of He+ distribution. The model successfully reproduces erosion of the plasmasphere on the night side as well as plume formation and evolution, and agrees well with data.
The performance of neural networks strongly depends on the availability of training data, which is limited during intervals of high geomagnetic activity. In order to provide reliable density predictions during such intervals, we can employ physics-based modeling. We develop a new approach for optimally combining the neural network- and physics-based models of the plasmasphere by means of data assimilation. The developed approach utilizes advantages of both neural network- and physics-based modeling and produces reliable global plasma density reconstructions for quiet, disturbed, and extreme geomagnetic conditions.
Finally, we extend the developed machine learning-based tools and apply them to another important problem in the field of space weather, the prediction of the geomagnetic index Kp. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere, the radiation belts and the plasmasphere. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast and make short-term predictions of Kp, basing their inferences on the recent history of Kp and solar wind measurements at L1. We analyze how the performance of neural networks compares to other machine learning algorithms for nowcasting and forecasting Kp for up to 12 hours ahead. Additionally, we investigate several machine learning and information theory methods for selecting the optimal inputs to a predictive model of Kp. The developed tools for feature selection can also be applied to other problems in space physics in order to reduce the input dimensionality and identify the most important drivers.
Research outlined in this dissertation clearly demonstrates that machine learning tools can be used to develop empirical models from sparse data and also can be used to understand the underlying physical processes. Combining machine learning, physics-based modeling and data assimilation allows us to develop novel methods benefiting from these different approaches.
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.
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.
Noise is ubiquitous in nature and usually results in rich dynamics in stochastic systems such as oscillatory systems, which exist in such various fields as physics, biology and complex networks. The correlation and synchronization of two or many oscillators are widely studied topics in recent years.
In this thesis, we mainly investigate two problems, i.e., the stochastic bursting phenomenon in noisy excitable systems and synchronization in a three-dimensional Kuramoto model with noise. Stochastic bursting here refers to a sequence of coherent spike train, where each spike has random number of followers due to the combined effects of both time delay and noise. Synchronization, as a universal phenomenon in nonlinear dynamical systems, is well illustrated in the Kuramoto model, a prominent model in the description of collective motion.
In the first part of this thesis, 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 extend it to the delay-coupled case and derive analytically the statistics of the spikes in each neuron, the pairwise correlations between any two neurons, and the spectrum of the total output from the network.
In the second part, we investigate the three-dimensional noisy Kuramoto model, which can be used to describe the synchronization in a swarming model with helical trajectory. In the case without natural frequency, the Kuramoto model can be connected with the Vicsek model, which is widely studied in collective motion and swarming of active matter. We analyze the linear stability of the incoherent state and derive the critical coupling strength above which the incoherent state loses stability. In the limit of no natural frequency, an exact self-consistent equation of the mean field is derived and extended straightforward to any high-dimensional case.
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 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 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.
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.
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.
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.
Noise-sustained and controlled synchronization of stirred excitable media by external forcing
(2005)
Most of the previous studies on constructive effects of noise in spatially extended systems have focused on static media, e.g., of the reaction diffusion type. Because many active chemical or biological processes occur in a fluid environment with mixing, we investigate here the interplay among noise, excitability, mixing and external forcing in excitable media advected by a chaotic flow, in a two-dimensional FitzHugh-Nagumo model described by a set of reaction- advection-diffusion equations. In the absence of external forcing, noise may generate sustained coherent oscillations of the media in a range of noise intensities and stirring rates. We find that these noise-sustained oscillations can be synchronized by external periodic signals much smaller than the threshold. Analysis of the locking regions in the parameter space of the signal period, stirring rate and noise intensity reveals that the mechanism underlying the synchronization behaviour is a matching between the time scales of the forcing signal and the noise-sustained oscillations. The results demonstrate that, in the presence of a suitable level of noise, the stirred excitable media act as self-sustained oscillatory systems and become much easier to be entrained by weak external forcing. Our results may be verified in experiments and are useful to understand the synchronization of population dynamics of oceanic ecological systems by annual cycles