Institut für Physik und Astronomie
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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 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.
The Milky Way is a spiral galaxy consisting of a disc of gas, dust and stars embedded in a halo of dark matter. Within this dark matter halo there is also a diffuse population of stars called the stellar halo, that has been accreting stars for billions of years from smaller galaxies that get pulled in and disrupted by the large gravitational potential of the Milky Way. As they are disrupted, these galaxies leave behind long streams of stars that can take billions of years to mix with the rest of the stars in the halo. Furthermore, the amount of heavy elements (metallicity) of the stars in these galaxies reflects the rate of chemical enrichment that occurred in them, since the Universe has been slowly enriched in heavy elements (e.g. iron) through successive generations of stars which produce them in their cores and supernovae explosions. Therefore, stars that contain small amounts of heavy elements (metal-poor stars) either formed at early times before the Universe was significantly enriched, or in isolated environments. The aim of this thesis is to develop a better understanding of the substructure content and chemistry of the Galactic stellar halo, in order to gain further insight into the formation and evolution of the Milky Way.
The Pristine survey uses a narrow-band filter which specifically targets the Ca II H & K spectral absorption lines to provide photometric metallicities for a large number of stars down to the extremely metal-poor (EMP) regime, making it a very powerful data set for Galactic archaeology studies. In Chapter 2, we quantify the efficiency of the survey using a preliminary spectroscopic follow-up sample of ~ 200 stars. We also use this sample to establish a set of selection criteria to improve the success rate of selecting EMP candidates for follow-up spectroscopy. In Chapter 3, we extend this work and present the full catalogue of ~ 1000 stars from a three year long medium resolution spectroscopic follow-up effort conducted as part of the Pristine survey. From this sample, we compute success rates of 56% and 23% for recovering stars with [Fe/H] < -2.5 and [Fe/H] < -3.0, respectively. This demonstrates a high efficiency for finding EMP stars as compared to previous searches with success rates of 3-4%.
In Chapter 4, we select a sample of ~ 80000 halo stars using colour and magnitude cuts to select a main sequence turnoff population in the distance range 6 < dʘ < 20 kpc. We then use the spectroscopic follow-up sample presented in Chapter 3 to statistically rescale the Pristine photometric metallicities of this sample, and present the resulting corrected metallicity distribution function (MDF) of the halo. The slope at the metal-poor end is significantly shallower than previous spectroscopic efforts have shown, suggesting that there may be more metal-poor stars with [Fe/H] < -2.5 in the halo than previously thought. This sample also shows evidence that the MDF of the halo may not be bimodal as was proposed by previous works, and that the lack of globular clusters in the Milky Way may be the result of a physical truncation of the MDF rather than just statistical under-sampling.
Chapter 5 showcases the unexpected capability of the Pristine filter for separating blue horizontal branch (BHB) stars from Blue Straggler (BS) stars. We demonstrate a purity of 93% and completeness of 91% for identifying BHB stars, a substantial improvement over previous works. We then use this highly pure and complete sample of BHB stars to trace the halo density profile out to d > 100 kpc, and the Sagittarius stream substructure out to ~ 130 kpc.
In Chapter 6 we use the photometric metallicities from the Pristine survey to perform a clustering analysis of the halo as a function of metallicity. Separating the Pristine sample into four metallicity bins of [Fe/H] < -2, -2 < [Fe/H] < -1.5, -1.5 < [Fe/H] < -1 and -0.9 < [Fe/H] < -0.8, we compute the two-point correlation function to measure the amount of clustering on scales of < 5 deg. For a smooth comparison sample we make a mock Pristine data set generated using the Galaxia code based on the Besançon model of the Galaxy. We find enhanced clustering on small scales (< 0.5 deg) for some regions of the Galaxy for the most metal-poor bin ([Fe/H] < -2), while in others we see large scale signals that correspond to known substructures in those directions. This confirms that the substructure content of the halo is highly anisotropic and diverse in different Galactic environments. We discuss the difficulties of removing systematic clustering signals from the data and the limitations of disentangling weak clustering signals from real substructures and residual systematic structure in the data.
Taken together, the work presented in this thesis approaches the problem of better understanding the halo of our Galaxy from multiple angles. Firstly, presenting a sizeable sample of EMP stars and improving the selection efficiency of EMP stars for the Pristine survey, paving the way for the further discovery of metal-poor stars to be used as probes to early chemical evolution. Secondly, improving the selection of BHB distance tracers to map out the halo to large distances, and finally, using the large samples of metal-poor stars to derive the MDF of the inner halo and analyse the substructure content at different metallicities. The results of this thesis therefore expand our understanding of the physical and chemical properties of the Milky Way stellar halo, and provide insight into the processes involved in its formation and evolution.
Perovskite solar cells have become one of the most studied systems in the quest for new, cheap and efficient solar cell materials. Within a decade device efficiencies have risen to >25% in single-junction and >29% in tandem devices on top of silicon. This rapid improvement was in many ways fortunate, as e. g. the energy levels of commonly used halide perovskites are compatible with already existing materials from other photovoltaic technologies such as dye-sensitized or organic solar cells. Despite this rapid success, fundamental working principles must be understood to allow concerted further improvements. This thesis focuses on a comprehensive understanding of recombination processes in functioning devices.
First the impact the energy level alignment between the perovskite and the electron transport layer based on fullerenes is investigated. This controversial topic is comprehensively addressed and recombination is mitigated through reducing the energy difference between the perovskite conduction band minimum and the LUMO of the fullerene. Additionally, an insulating blocking layer is introduced, which is even more effective in reducing this recombination, without compromising carrier collection and thus efficiency. With the rapid efficiency development (certified efficiencies have broken through the 20% ceiling) and thousands of researchers working on perovskite-based optoelectronic devices, reliable protocols on how to reach these efficiencies are lacking. Having established robust methods for >20% devices, while keeping track of possible pitfalls, a detailed description of the fabrication of perovskite solar cells at the highest efficiency level (>20%) is provided. The fabrication of low-temperature p-i-n structured devices is described, commenting on important factors such as practical experience, processing atmosphere & temperature, material purity and solution age. Analogous to reliable fabrication methods, a method to identify recombination losses is needed to further improve efficiencies. Thus, absolute photoluminescence is identified as a direct way to quantify the Quasi-Fermi level splitting of the perovskite absorber (1.21eV) and interfacial recombination losses the transport layers impose, reducing the latter to ~1.1eV. Implementing very thin interlayers at both the p- and n-interface (PFN-P2 and LiF, respectively), these losses are suppressed, enabling a VOC of up to 1.17eV. Optimizing the device dimensions and the bandgap, 20% devices with 1cm2 active area are demonstrated. Another important consideration is the solar cells’ stability if subjected to field-relevant stressors during operation. In particular these are heat, light, bias or a combination thereof. Perovskite layers – especially those incorporating organic cations – have been shown to degrade if subjected to these stressors. Keeping in mind that several interlayers have been successfully used to mitigate recombination losses, a family of perfluorinated self-assembled monolayers (X-PFCn, where X denotes I/Br and n = 7-12) are introduced as interlayers at the n-interface. Indeed, they reduce interfacial recombination losses enabling device efficiencies up to 21.3%. Even more importantly they improve the stability of the devices. The solar cells with IPFC10 are stable over 3000h stored in the ambient and withstand a harsh 250h of MPP at 85◦C without appreciable efficiency losses. To advance further and improve device efficiencies, a sound understanding of the photophysics of a device is imperative. Many experimental observations in recent years have however drawn an inconclusive picture, often suffering from technical of physical impediments, disguising e. g. capacitive discharge as recombination dynamics. To circumvent these obstacles, fully operational, highly efficient perovskites solar cells are investigated by a combination of multiple optical and optoelectronic probes, allowing to draw a conclusive picture of the recombination dynamics in operation. Supported by drift-diffusion simulations, the device recombination dynamics can be fully described by a combination of first-, second- and third-order recombination and JV curves as well as luminescence efficiencies over multiple illumination intensities are well described within the model. On this basis steady state carrier densities, effective recombination constants, densities-of-states and effective masses are calculated, putting the devices at the brink of the radiative regime. Moreover, a comprehensive review of recombination in state-of-the-art devices is given, highlighting the importance of interfaces in nonradiative recombination. Different strategies to assess these are discussed, before emphasizing successful strategies to reduce interfacial recombination and pointing towards the necessary steps to further improve device efficiency and stability. Overall, the main findings represent an advancement in understanding loss mechanisms in highly efficient solar cells. Different reliable optoelectronic techniques are used and interfacial losses are found to be of grave importance for both efficiency and stability. Addressing the interfaces, several interlayers are introduced, which mitigate recombination losses and degradation.
After the United Kingdom has left the European Union it remains unclear whether the two parties can successfully negotiate and sign a trade agreement within the transition period. Ongoing negotiations, practical obstacles and resulting uncertainties make it highly unlikely that economic actors would be fully prepared to a “no-trade-deal” situation. Here we provide an economic shock simulation of the immediate aftermath of such a post-Brexit no-trade-deal scenario by computing the time evolution of more than 1.8 million interactions between more than 6,600 economic actors in the global trade network. We find an abrupt decline in the number of goods produced in the UK and the EU. This sudden output reduction is caused by drops in demand as customers on the respective other side of the Channel incorporate the new trade restriction into their decision-making. As a response, producers reduce prices in order to stimulate demand elsewhere. In the short term consumers benefit from lower prices but production value decreases with potentially severe socio-economic consequences in the longer term.
The passive and active motion of micron-sized tracer particles in crowded liquids and inside living biological cells is ubiquitously characterised by 'viscoelastic' anomalous diffusion, in which the increments of the motion feature long-ranged negative and positive correlations. While viscoelastic anomalous diffusion is typically modelled by a Gaussian process with correlated increments, so-called fractional Gaussian noise, an increasing number of systems are reported, in which viscoelastic anomalous diffusion is paired with non-Gaussian displacement distributions. Following recent advances in Brownian yet non-Gaussian diffusion we here introduce and discuss several possible versions of random-diffusivity models with long-ranged correlations. While all these models show a crossover from non-Gaussian to Gaussian distributions beyond some correlation time, their mean squared displacements exhibit strikingly different behaviours: depending on the model crossovers from anomalous to normal diffusion are observed, as well as a priori unexpected dependencies of the effective diffusion coefficient on the correlation exponent. Our observations of the non-universality of random-diffusivity viscoelastic anomalous diffusion are important for the analysis of experiments and a better understanding of the physical origins of 'viscoelastic yet non-Gaussian' diffusion.
Inorganic perovskites with cesium (Cs+) as the cation have great potential as photovoltaic materials if their phase purity and stability can be addressed. Herein, a series of inorganic perovskites is studied, and it is found that the power conversion efficiency of solar cells with compositions CsPbI1.8Br1.2, CsPbI2.0Br1.0, and CsPbI2.2Br0.8 exhibits a high dependence on the initial annealing step that is found to significantly affect the crystallization and texture behavior of the final perovskite film. At its optimized annealing temperature, CsPbI1.8Br1.2 exhibits a pure orthorhombic phase and only one crystal orientation of the (110) plane. Consequently, this allows for the best efficiency of up to 14.6% and the longest operational lifetime, T-S80, of approximate to 300 h, averaged of over six solar cells, during the maximum power point tracking measurement under continuous light illumination and nitrogen atmosphere. This work provides essential progress on the enhancement of photovoltaic performance and stability of CsPbI3 - xBrx perovskite solar cells.
As one of the most-produced commodity polymers, polypropylene draws considerable scientific and commercial interest as an electret material. In the present thesis, the influence of the surface chemical modification and crystalline reconstruction on the electret properties of the polypropylene thin films will be discussed. The chemical treatment with orthophosphoric acid can significantly improve the surface charge stability of the polypropylene electrets by introducing phosphorus- and oxygen-containing structures onto the modified surface. The thermally stimulated discharge measurement and charge profiling by means of piezoelectrically generated pressure steps are used to investigate the electret behaviour. It is concluded that deep traps of limited number density are created during the treatment with inorganic chemicals. Hence, the improvement dramatically decreases when the surface-charge density is substantially higher than ±1.2×10^(-3) C·m^(-2). The newly formed traps also show a higher trapping energy for negative charges. The energetic distributions of the traps in the non-treated and chemically treated samples offer an insight regarding the surface and foreign-chemical dominance on the charge storage and transport in the polypropylene electrets.
Additionally, different electret properties are observed on the polypropylene films with the spherulitic and transcrystalline structures. It indicates the dependence of the charge storage and transport on the crystallite and molecular orientations in the crystalline phase. In general, a more diverse crystalline growth in the spherulitic samples can result in a more complex energetic trap distribution, in comparison to that in a transcrystalline polypropylene. The double-layer transcrystalline polypropylene film with a crystalline interface in the middle can be obtained by crystallising the film in contact with rough moulding surfaces on both sides. A layer of heterocharges appears on each side of the interface in the double-layer transcrystalline polypropylene electrets after the thermal poling. However, there is no charge captured within the transcrystalline layers. The phenomenon reveals the importance of the crystalline interface in terms of creating traps with the higher activation energy in polypropylene. The present studies highlight the fact that even slight variations in the polypropylene film may lead to dramatic differences in its electret properties.
We show that, although the equilibrium band dispersion of the Shockley-type surface state of two-dimensional Au(111) quantum films grown on W(110) does not deviate from the expected free-electron-like behavior, its nonequilibrium energy-momentum dispersion probed by time- and angle-resolved photoemission exhibits a remarkable kink above the Fermi level due to a significant enhancement of the effective mass. The kink is pronounced for certain thicknesses of the Au quantum well and vanishes in the very thin limit. We identify the kink as induced by the coupling between the Au(111) surface state and emergent quantum-well states which probe directly the buried gold-tungsten interface. The signatures of the coupling are further revealed by our time-resolved measurements which show that surface state and quantum-well states thermalize together behaving as dynamically locked electron populations. In particular, relaxation of hot carriers following laser excitation is similar for both surface state and quantum-well states and much slower than expected for a bulk metallic system. The influence of quantum confinement on the interplay between elementary scattering processes of the electrons at the surface and ultrafast carrier transport in the direction perpendicular to the surface is shown to be the reason for the slow electron dynamics.
Perovskite photovoltaic (PV) cells have demonstrated power conversion efficiencies (PCE) that are close to those of monocrystalline silicon cells; however, in contrast to silicon PV, perovskites are not limited by Auger recombination under 1-sun illumination. Nevertheless, compared to GaAs and monocrystalline silicon PV, perovskite cells have significantly lower fill factors due to a combination of resistive and non-radiative recombination losses. This necessitates a deeper understanding of the underlying loss mechanisms and in particular the ideality factor of the cell. By measuring the intensity dependence of the external open-circuit voltage and the internal quasi-Fermi level splitting (QFLS), the transport resistance-free efficiency of the complete cell as well as the efficiency potential of any neat perovskite film with or without attached transport layers are quantified. Moreover, intensity-dependent QFLS measurements on different perovskite compositions allows for disentangling of the impact of the interfaces and the perovskite surface on the non-radiative fill factor and open-circuit voltage loss. It is found that potassium-passivated triple cation perovskite films stand out by their exceptionally high implied PCEs > 28%, which could be achieved with ideal transport layers. Finally, strategies are presented to reduce both the ideality factor and transport losses to push the efficiency to the thermodynamic limit.