Organic Semiconductors for Next Generation Organic Photovoltaics

Zonne-energie is een noodzakelijk onderdeel van de beweging richting duurzame energie. Het grootste gedeelte van elektriciteit opgewekt door zonne-energie wordt geleverd door fotovoltaïsche (PV) faciliteiten. Echter, het totale aandeel van PV in het globale elektriciteitsopwekking is nog steeds erg klein. Een vermindering van de module kosten en de totale kosten van installatie zou PV kunnen helpen om zijn aandeel onder de duurzame energiebronnen te verhogen. Voor dit doel is de dunne-laag technologie zeer waarschijnlijk een geschikte kandidaat. Organische fotovoltaïca (OPV) is één van de opkomende dunne-laag technologieën die materialen gebruikt die niet schaars zijn, en is veelbelovend voor het terugdringen van de kosten van PV vanwege hun flexibiliteit en het lichte gewicht van de materialen. Echter, om een plek in de PV markt veilig te stellen moeten organische zonnecellen eerst hun huidige beperkingen zien te overwinnen wat betreft zowel de efficiëntie (PCE) waarmee het licht omzet naar elektriciteit, als de stabiliteit en produceerbaarheid. Door een strategie te onderzoeken om de permittiviteit van organische halfgeleiders te verhogen, door de betrouwbaarheid van de meetmethodes van de permittiviteit te bestuderen, en door de optimalisatieroutes voor de morfologie van efficiënte zonnecellen te vergelijken, geeft dit proefschrift een complete routebeschrijving van het ontwerpen van materialen tot aan de uiteindelijke implementatie

Impact of Electrodes on Recombination in Bulk Heterojunction Organic Solar Cells

In recent years, the efficiency of organic solar cells (OSCs) has increased to more than 13%, although different barriers are on the way for reaching higher efficiencies. One crucial barrier is the recombination of charge carriers, which can either occur as the bulk recombination of photogenerated charges or the recombination of photogenerated charges and electrodic induced charges (EICs). This work studies the impact of EICs on the recombination lifetime in OSCs. To this end, the net recombination lifetime of photogenerated charge carriers in the presence of EICs is measured by means of conventional and newly developed transient photovoltage techniques. Moreover, a new approach has been introduced to exclusively measure the bulk recombination lifetime, i.e., in the absence of EICs; this approach was conducted by depositing transparent insulating layers on both sides of the OSC active layer. An examination of these approaches on OSCs with different active layer materials, thicknesses, and varying light intensities determined that the EICs can only reduce the recombination lifetime of the photogenerated charges in OSCs with very weak recombination strength. This work supports that for OSCs with highly reduced recombination strength, eliminating the recombination of photogenerated charges and EICs is critical for achieving better performance. Therefore, the use of a proper blocking layer suppresses EIC recombination in systems with very weak recombination.</p

Rough Electrode Creates Excess Capacitance in Thin-Film Capacitors

The parallel-plate capacitor equation is widely used in contemporary material research for nanoscale applications and nanoelectronics. To apply this equation, flat and smooth electrodes are assumed for a capacitor. This essential assumption is often violated for thin-film capacitors because the formation of nanoscale roughness at the electrode interface is very probable for thin films grown via common deposition methods. In this work, we experimentally and theoretically show that the electrical capacitance of thin-film capacitors with realistic interface roughness is significantly larger than the value predicted by the parallel-plate capacitor equation. The degree of the deviation depends on the strength of the roughness, which is described by three roughness parameters for a self-affine fractal surface. By applying an extended parallel-plate capacitor equation that includes the roughness parameters of the electrode, we are able to calculate the excess capacitance of the electrode with weak roughness. Moreover, we introduce the roughness parameter limits for which the simple parallel-plate capacitor equation is sufficiently accurate for capacitors with one rough electrode. Our results imply that the interface roughness beyond the proposed limits cannot be dismissed unless the independence of the capacitance from the interface roughness is experimentally demonstrated. The practical protocols suggested in our work for the reliable use of the parallel-plate capacitor equation can be applied as general guidelines in various fields of interest

Fundamental Limits of Communication in Distributed Computation Frameworks

This dissertation develops a method for integrating information theoretic principles in distributed computing frameworks, distributed learning, and database design. In particular, it incorporates compression coding in such a way as to accelerate the computation of statistical functions of the data in distributed computing frameworks. Moreover, it studies the limits of decentralized compressors for query processing which speeds up computations by requiring less network traffic and efficient memory usage, and proposes a rate-distortion principle to gain some insight in distributed learning algorithms. The dissertation's insights into tradeoffs in practical distributed computing are garnered by an information theoretic model for decentralized function computation. In decentralized function computation, a series of two or more networked nodes, each storing or measuring local observation sequences, separately encode their observations, and communicate interactively with a sink, hence-forth the central estimation officer (CEO), with the goal of enabling it to compute some function or statistic of their combined data across each element in the sequence. Of great interest in this problem is the rate-distortion region, describing all possible vectors of rates, determining the sizes of each of the messages sent for which there exist encoders at the observation nodes and a decoder not exceeding a specified distortion. We assemble this problem formulation to model the cache formation design in distributed shared memory systems for query processing. Unlike traditional designs that focus on a particular query (e.g., search, retrieval, similarity), we aim to couple the representation extraction with compression and generalize the query to any types of function. We study the single letter description of the achievable region of trade-off between the storage size versus accuracy (general distortion metric) and storage size versus the cost of cache miss (log-loss distortion) for finite-alphabet sources. Another main source of difficulty after obtaining the rate-distortion expression is finding practical schemes and computing the region. The second part of the dissertation is focused on designing optimal encoding functions. With the aim of enabling the master node to compute the extrema function, the fundamental lower bound on the information exchange rate required over all quantization schemes, both scalar and vector, is computed for this interactive problem with a known iterative convex geometric method. Next, an optimal dynamic program achieving the minimum expected rate and expected rate delay tradeoff over all scalar quantization schemes is presented, and the benefits of enabling nodes to overhear each others' messages is assessed. Furthermore, a series of substantially reduced complexity dynamic programs are shown, both theoretically and empirically, to obtain performance close to the fundamental limits, and to scale favorably as the number of nodes grow. The final chapter of the dissertation proposes a novel method to design practical codes for the dissertation's accelerated distributed computing model by interactively training multi-task neural networks. This deep learning model is able to design the memory content to maximize cache hits even if the query is not known in advance. This use of deep neural networks to design codes for interactive distributed cache formation is inspired by a popular recent insight, known as the information bottleneck, that activations of the nodes at each layer of a neural network can be reinterpreted as a sufficient statistic compressing the information provided by the previous layer about the ultimate classification problem. Multiple experiments show that our neural cache formation machine can learn to compute a set of possible query functions from the cache contents by caching a learned meaningful representation of the data universal across these query tasks.Ph.D., Electrical Engineering -- Drexel University, 201

Fundamental Limits of Communication in Distributed Computation Frameworks

This dissertation develops a method for integrating information theoretic principles in distributed computing frameworks, distributed learning, and database design. In particular, it incorporates compression coding in such a way as to accelerate the computation of statistical functions of the data in distributed computing frameworks. Moreover, it studies the limits of decentralized compressors for query processing which speeds up computations by requiring less network traffic and efficient memory usage, and proposes a rate-distortion principle to gain some insight in distributed learning algorithms. The dissertation's insights into tradeoffs in practical distributed computing are garnered by an information theoretic model for decentralized function computation. In decentralized function computation, a series of two or more networked nodes, each storing or measuring local observation sequences, separately encode their observations, and communicate interactively with a sink, hence-forth the central estimation officer (CEO), with the goal of enabling it to compute some function or statistic of their combined data across each element in the sequence. Of great interest in this problem is the rate-distortion region, describing all possible vectors of rates, determining the sizes of each of the messages sent for which there exist encoders at the observation nodes and a decoder not exceeding a specified distortion. We assemble this problem formulation to model the cache formation design in distributed shared memory systems for query processing. Unlike traditional designs that focus on a particular query (e.g., search, retrieval, similarity), we aim to couple the representation extraction with compression and generalize the query to any types of function. We study the single letter description of the achievable region of trade-off between the storage size versus accuracy (general distortion metric) and storage size versus the cost of cache miss (log-loss distortion) for finite-alphabet sources. Another main source of difficulty after obtaining the rate-distortion expression is finding practical schemes and computing the region. The second part of the dissertation is focused on designing optimal encoding functions. With the aim of enabling the master node to compute the extrema function, the fundamental lower bound on the information exchange rate required over all quantization schemes, both scalar and vector, is computed for this interactive problem with a known iterative convex geometric method. Next, an optimal dynamic program achieving the minimum expected rate and expected rate delay tradeoff over all scalar quantization schemes is presented, and the benefits of enabling nodes to overhear each others' messages is assessed. Furthermore, a series of substantially reduced complexity dynamic programs are shown, both theoretically and empirically, to obtain performance close to the fundamental limits, and to scale favorably as the number of nodes grow. The final chapter of the dissertation proposes a novel method to design practical codes for the dissertation's accelerated distributed computing model by interactively training multi-task neural networks. This deep learning model is able to design the memory content to maximize cache hits even if the query is not known in advance. This use of deep neural networks to design codes for interactive distributed cache formation is inspired by a popular recent insight, known as the information bottleneck, that activations of the nodes at each layer of a neural network can be reinterpreted as a sufficient statistic compressing the information provided by the previous layer about the ultimate classification problem. Multiple experiments show that our neural cache formation machine can learn to compute a set of possible query functions from the cache contents by caching a learned meaningful representation of the data universal across these query tasks.Ph.D., Electrical Engineering -- Drexel University, 201

Fullerene derivatives with increased dielectric constants

The invention of new organic materials with high dielectric constants is of extreme importance for the development of organic-based devices such as organic solar cells. We report on a synthetic way to increase the dielectric constant of fullerene derivatives. It is demonstrated that introducing triethylene glycol monoethyl ether (teg) side chains into fulleropyrrolidines increases the dielectric constant by ~46 percent without devaluation of optical properties, electron mobility and the energy level of the compound

Deposition of LiF onto Films of Fullerene Derivatives Leads to Bulk Doping

One of the most commonly used cathode interlayers for increasing the efficiency of electron injection/extraction in organic electronic devices is an ultrathin layer of LiF. Our capacitance measurements and electrical conductivity E analysis show that thin films of fullerene derivatives and their mixtures with polymers are unintentionally doped upon deposition of LiF. The level of doping depends on the chemical, structure of the fullerene derivatives. The doping effect on polymer/fullerene mixtures is significant only for blends in which the fullerene content is greater than the polymer content by weight. Our finding has profound implications for the development and characterization of organic photovoltaic devices, including a negative impact of doping on the stability of the device and erroneous estimations of properties such as charge carrier mobility and the dielectric constant