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Ce mémoire a pour but d'explorer l'utilisation de données de contexte, notamment spatial, pour prédire de la durée que va prendre un garage pour effectuer les réparations à la suite d'un accident automobile. Le contexte réfère à l'environnement dans lequel évolue le garage. Il s'agit donc de développer une approche permettant de prédire une caractéristique précise en utilisant notamment de l'information historique. L'information historique comprend des composantes spatiales, comme des adresses, qui vont être exploitées afin de générer de nouvelles informations relatives à la localisation des garages automobiles. L'utilisation des données accumulées sur les réclamations automobiles va permettre d'établir un niveau initial de prédiction qu'il est possible d'atteindre avec de l'apprentissage supervisé. En ajoutant ensuite petit à petit de l'information de contexte spatial dans lequel évolue le garage responsable des réparations, de nouveaux niveaux de prédiction seront atteints. Il sera alors possible d'évaluer la pertinence de considérer le contexte spatial dans un problème de prédiction comme celui des temps de réparations des véhicules accidentés en comparant ces niveaux de prédiction précédemment cités. L'utilisation de données historiques pour prédire une nouvelle donnée se fait depuis plusieurs années à l'aide d'une branche de l'intelligence artificielle, à savoir : l'apprentissage machine. Couplées à cette méthode d'analyse et de production de données, des analyses spatiales vont être présentées et introduites pour essayer de modéliser le contexte spatial. Pour quantifier l'apport d'analyses spatiales et de données localisées dans un problème d'apprentissage machine, il sera question de comparer l'approche n'utilisant pas d'analyse spatiale pour produire de nouvelles données, avec une approche similaire considérant cette fois-ci les données de contexte spatial dans lequel évolue le garage. L'objectif est de voir l'impact que peut avoir une contextualisation spatiale sur la prédiction d'une variable quantitative. / The purpose of this paper is to explore the use of context data, particularly spatial context, to predict how long it will take a garage to complete repairs following an automobile accident. The context refers to the environment in which the garage evolves. It is therefore a question of developing an approach that makes it possible to predict a precise characteristic by using historical information in particular. The historical information includes spatial components, such as addresses, which will be exploited to generate new information about the location of car garages. The use of the accumulated data on car claims will allow to establish an initial level of prediction that can be reached with supervised learning. By then gradually adding information about the spatial context in which the garage responsible for the repairs evolves, new levels of prediction will be reached. It will then be possible to evaluate the relevance of considering the spatial context in a prediction problem such as that of the repair times of accidented vehicles by comparing these prediction levels previously mentioned. The use of historical data to predict new data has been done for several years with the help of a branch of artificial intelligence, namely: machine learning. Coupled with this method of data analysis and production, spatial analyses will be presented and introduced to try to model the spatial context. To quantify the contribution of spatial analysis and localized data in a machine learning problem, we will compare the approach that does not use spatial analysis to produce new data with a similar approach that considers the spatial context data in which the garage evolves. The objective is to see the impact that spatial contextualization can have on the prediction of a quantitative variable.

Ce mémoire a pour but d'explorer l'utilisation de données de contexte, notamment spatial, pour prédire de la durée que va prendre un garage pour effectuer les réparations à la suite d'un accident automobile. Le contexte réfère à l'environnement dans lequel évolue le garage. Il s'agit donc de développer une approche permettant de prédire une caractéristique précise en utilisant notamment de l'information historique. L'information historique comprend des composantes spatiales, comme des adresses, qui vont être exploitées afin de générer de nouvelles informations relatives à la localisation des garages automobiles. L'utilisation des données accumulées sur les réclamations automobiles va permettre d'établir un niveau initial de prédiction qu'il est possible d'atteindre avec de l'apprentissage supervisé. En ajoutant ensuite petit à petit de l'information de contexte spatial dans lequel évolue le garage responsable des réparations, de nouveaux niveaux de prédiction seront atteints. Il sera alors possible d'évaluer la pertinence de considérer le contexte spatial dans un problème de prédiction comme celui des temps de réparations des véhicules accidentés en comparant ces niveaux de prédiction précédemment cités. L'utilisation de données historiques pour prédire une nouvelle donnée se fait depuis plusieurs années à l'aide d'une branche de l'intelligence artificielle, à savoir : l'apprentissage machine. Couplées à cette méthode d'analyse et de production de données, des analyses spatiales vont être présentées et introduites pour essayer de modéliser le contexte spatial. Pour quantifier l'apport d'analyses spatiales et de données localisées dans un problème d'apprentissage machine, il sera question de comparer l'approche n'utilisant pas d'analyse spatiale pour produire de nouvelles données, avec une approche similaire considérant cette fois-ci les données de contexte spatial dans lequel évolue le garage. L'objectif est de voir l'impact que peut avoir une contextualisation spatiale sur la prédiction d'une variable quantitative. / The purpose of this paper is to explore the use of context data, particularly spatial context, to predict how long it will take a garage to complete repairs following an automobile accident. The context refers to the environment in which the garage evolves. It is therefore a question of developing an approach that makes it possible to predict a precise characteristic by using historical information in particular. The historical information includes spatial components, such as addresses, which will be exploited to generate new information about the location of car garages. The use of the accumulated data on car claims will allow to establish an initial level of prediction that can be reached with supervised learning. By then gradually adding information about the spatial context in which the garage responsible for the repairs evolves, new levels of prediction will be reached. It will then be possible to evaluate the relevance of considering the spatial context in a prediction problem such as that of the repair times of accidented vehicles by comparing these prediction levels previously mentioned. The use of historical data to predict new data has been done for several years with the help of a branch of artificial intelligence, namely: machine learning. Coupled with this method of data analysis and production, spatial analyses will be presented and introduced to try to model the spatial context. To quantify the contribution of spatial analysis and localized data in a machine learning problem, we will compare the approach that does not use spatial analysis to produce new data with a similar approach that considers the spatial context data in which the garage evolves. The objective is to see the impact that spatial contextualization can have on the prediction of a quantitative variable.

Driver heterogeneity in travel behavior has repeatedly been cited in the literature as a limitation that needs to be addressed. In this work, driver heterogeneity is addressed from four different perspectives. First, driver heterogeneity is addressed by models of driver perceptions of travel conditions: travel distance, time, and speed. Second, it is addressed from the perspective of driver learning trends and models of driver-types. Driver type is not commonly used in the vernacular of transportation engineering. It is a term that was developed in this work to reflect driver aggressiveness in route switching behavior. It may be interpreted as analogous to the commonly known personality-types, but applied to driver behavior. Third, driver heterogeneity is addressed via latent class choice models. Last, personality traits were found significant in all estimated models. The first three adopted perspectives were modeled as functions of variables of driver demographics, personality traits, and choice situation characteristics. The work is based on three datasets: a driving simulator experiment, an in situ driving experiment in real-world conditions, and a naturalistic real-life driving experiment. In total, the results are based on three experiments, 109 drivers, 74 route choice situations, and 8,644 route choices. It is assuring that results from all three experiments were found to be highly consistent. Discrepancies between predictions of network-oriented traffic assignment models and observed route choice percentages were identified and incorporating variables of driver heterogeneity were found to improve route choice model performance. Variables from all three groups: driver demographics, personality traits, and choice situation characteristics, were found significant in all considered models for driver heterogeneity. However, it is extremely interesting that all five variables of driver personality traits were found to be, in general, as significant as, and frequently more significant than, variables of trip characteristics — such as travel time. Neuroticism, extraversion and conscientiousness were found to increase route switching behavior, and openness to experience and agreeable were found to decrease route switching behavior. In addition, as expected, travel time was found to be highly significant in the models that were developed. However, unexpectedly, travel speed was also found to be highly significant, and travel distance was not as significant as expected. Results of this work are highly promising for the future of understanding and modeling of heterogeneity of human travel behavior, as well as for identifying target markets and the future of intelligent transportation systems. / Ph. D.

Recently, many new types of wireless networks have emerged, such as mobile ad hoc networks (MANETs), cognitive radio networks (CRNs) and large scale wireless sensor networks. To get better performance in these wireless networks, various schemes, e.g., metrics, policies, algorithms, protocols, etc., have been proposed. Among them, optimal schemes that can achieve optimal performance are of great importance. On the theoretical side, they provide important design guidelines and performance benchmarks. On the practical side, they guarantee best communication performance with limited network resources. In this dissertation, we focus on the modeling and optimization of routing in wireless networks, including both broadcast routing, unicast routing, and convergecast routing. We study two aspects of routing: algorithm analysis and Qos analysis. In the algorithmic work, we focus on how to build optimal broadcast trees. We investigate the optimality compatibility between three tree-based broadcast routing algorithms and routing metrics. The Qos work includes three parts. First, we focus on how to optimally repair broken paths to minimize impact of path break in MANETs. We propose a provably optimal cached-based route repair policy for real-time traffic in MANETs. Second, we focus on the impact of secondary user (SU) node placement on SU traffic delay in CRNs. We design SU node placement schemes that can minimize the multi-hop delay in CRNs. Third, we analyze the convergecast delay of a large scale sensor network which coexists with WiFi nodes. We derive a closed form delay formula, which can be used to estimate sensor packet convergecast delay given the distance between a sensor node and the sink node together with other networking setting parameters. The main contributions of this dissertation are summarized as follows: Optimality compatibility study between tree-based broadcast routing algorithms and routing metrics: Broadcast routing is a critical component in the routing design. While there are plenty of routing metrics and broadcast routing schemes in current literature, arbitrary combination of broadcast routing metrics with broadcast tree construction (BTC) algorithms may not result in optimal broadcast trees. In this work, we study the requirement on the combination of routing metrics and BTC algorithms to ensure optimal broadcast tree construction. When a BTC algorithm fails to find the optimal broadcast tree, we define that the BTC algorithm and the metric are not optimality compatible. We show that different BTC algorithms have different requirements on the properties of broadcast routing metrics. The metric properties for BTC algorithms in both undirected network topologies and directed network topologies are developed and proved. They are successfully used to verify the optimality compatibility between broadcast routing metrics and BTC algorithms. Optimal cache-based route repair policy for real-time traffic in mobile ad hoc networks: Real-time applications in ad hoc networks require fast route repair mechanisms to minimize the interruptions to their communications. Cache-based route repair schemes are popular choices since they can quickly resume communications using cached backup paths after a route break. In this work, through thorough theoretical modeling of the cache-based route repair process, we derive a provably optimal cache-based route repair policy. This optimal policy considers both the overhead of the route repair schemes and the promptness of the repair action. The correctness and advantages of our optimal policy are validated by extensive simulations. Optimal secondary user node placement study in cognitive radio networks: Information propagation speed (IPS) in a multi-hop CRN is an important factor that affects the network's delay performance and needs to be considered in network planning. The impact of primary user (PU) activities on IPS makes the problem of analyzing IPS in multi-hop CRNs very challenging and hence unsolved in existing literature. In this work, we fill this technical void. We establish models of IPS in multi-hop CRNs and compute how to maximize IPS in two cases. The first case, named the maximum network IPS, maximizes IPS across a network topology over an infinite plane. The second case, named the maximum flow IPS, maximizes the IPS between a given pair of source and destination nodes separated by a fixed distance. We reveal that both maximum IPSs are determined by the PU activity level and the placement of SU relay nodes. We design optimal relay placement strategies in CRNs to maximize these two IPS under different PU activity levels. The correctness of our analytical results is validated by simulations and numerical experiments. Convergecast delay analysis of large scale sensor networks coexisting with WiFi networks: Due to the increasing popularity of wireless devices, such as WiFi (IEEE 802.11) and ZigBee (IEEE 802.15.4), the ISM bands have become more and more crowded. Since ZigBee is the de facto radio technology of sensor networks, coexistence of WiFi networks and sensor (ZigBee) networks is challenging because of the great heterogeneity between WiFi and ZigBee technologies. In the presence of interference from WiFi and other sensor nodes, the performance of sensor networks is not clearly understood. In this work, we study delay performance of a large scale sensor network which coexists with WiFi networks. Given the distance from the sensor node to the sink node, we are interested in the expected delay of sensor packets to reach the sink node in the presence of both WiFi and sensor interference. We formulate the delay analysis problem as a two priority M/G/1 preemptive repeat identical queueing system, and analyze the delay using queueing theory and probability theory. First, we use a path probabilistic approach to derive the expected delay. Second, we develop a simplified linear approximation model for delay analysis. The correctness of both models is validated by NS2 simulations.Recently, many new types of wireless networks have emerged, such as mobile ad hoc networks (MANETs), cognitive radio networks (CRNs) and large scale wireless sensor networks. To get better performance in these wireless networks, various schemes, e.g., metrics, policies, algorithms, protocols, etc., have been proposed. Among them, optimal schemes that can achieve optimal performance are of great importance. On the theoretical side, they provide important design guidelines and performance benchmarks. On the practical side, they guarantee best communication performance with limited network resources. In this dissertation, we focus on the modeling and optimization of routing in wireless networks, including both broadcast routing, unicast routing, and convergecast routing. We study two aspects of routing: algorithm analysis and Qos analysis. In the algorithmic work, we focus on how to build optimal broadcast trees. We investigate the optimality compatibility between three tree-based broadcast routing algorithms and routing metrics. The Qos work includes three parts. First, we focus on how to optimally repair broken paths to minimize impact of path break in MANETs. We propose a provably optimal cached-based route repair policy for real-time traffic in MANETs. Second, we focus on the impact of secondary user (SU) node placement on SU traffic delay in CRNs. We design SU node placement schemes that can minimize the multi-hop delay in CRNs. Third, we analyze the convergecast delay of a large scale sensor network which coexists with WiFi nodes. We derive a closed form delay formula, which can be used to estimate sensor packet convergecast delay given the distance between a sensor node and the sink node together with other networking setting parameters. The main contributions of this dissertation are summarized as follows: Optimality compatibility study between tree-based broadcast routing algorithms and routing metrics: Broadcast routing is a critical component in the routing design. While there are plenty of routing metrics and broadcast routing schemes in current literature, arbitrary combination of broadcast routing metrics with broadcast tree construction (BTC) algorithms may not result in optimal broadcast trees. In this work, we study the requirement on the combination of routing metrics and BTC algorithms to ensure optimal broadcast tree construction. When a BTC algorithm fails to find the optimal broadcast tree, we define that the BTC algorithm and the metric are not optimality compatible. We show that different BTC algorithms have different requirements on the properties of broadcast routing metrics. The metric properties for BTC algorithms in both undirected network topologies and directed network topologies are developed and proved. They are successfully used to verify the optimality compatibility between broadcast routing metrics and BTC algorithms. Optimal cache-based route repair policy for real-time traffic in mobile ad hoc networks: Real-time applications in ad hoc networks require fast route repair mechanisms to minimize the interruptions to their communications. Cache-based route repair schemes are popular choices since they can quickly resume communications using cached backup paths after a route break. In this work, through thorough theoretical modeling of the cache-based route repair process, we derive a provably optimal cache-based route repair policy. This optimal policy considers both the overhead of the route repair schemes and the promptness of the repair action. The correctness and advantages of our optimal policy are validated by extensive simulations. Optimal secondary user node placement study in cognitive radio networks: Information propagation speed (IPS) in a multi-hop CRN is an important factor that affects the network's delay performance and needs to be considered in network planning. The impact of primary user (PU) activities on IPS makes the problem of analyzing IPS in multi-hop CRNs very challenging and hence unsolved in existing literature. In this work, we fill this technical void. We establish models of IPS in multi-hop CRNs and compute how to maximize IPS in two cases. The first case, named the maximum network IPS, maximizes IPS across a network topology over an infinite plane. The second case, named the maximum flow IPS, maximizes the IPS between a given pair of source and destination nodes separated by a fixed distance. We reveal that both maximum IPSs are determined by the PU activity level and the placement of SU relay nodes. We design optimal relay placement strategies in CRNs to maximize these two IPS under different PU activity levels. The correctness of our analytical results is validated by simulations and numerical experiments. Convergecast delay analysis of large scale sensor networks coexisting with WiFi networks: Due to the increasing popularity of wireless devices, such as WiFi (IEEE 802.11) and ZigBee (IEEE 802.15.4), the ISM bands have become more and more crowded. Since ZigBee is the de facto radio technology of sensor networks, coexistence of WiFi networks and sensor (ZigBee) networks is challenging because of the great heterogeneity between WiFi and ZigBee technologies. In the presence of interference from WiFi and other sensor nodes, the performance of sensor networks is not clearly understood. In this work, we study delay performance of a large scale sensor network which coexists with WiFi networks. Given the distance from the sensor node to the sink node, we are interested in the expected delay of sensor packets to reach the sink node in the presence of both WiFi and sensor interference. We formulate the delay analysis problem as a two priority M/G/1 preemptive repeat identical queueing system, and analyze the delay using queueing theory and probability theory. First, we use a path probabilistic approach to derive the expected delay. Second, we develop a simplified linear approximation model for delay analysis. The correctness of both models is validated by NS2 simulations. / Ph. D.

Thesis (MPhil)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Climate change creates both risks and opportunities worldwide. By understanding, planning for and adapting to a changing climate, individuals and societies can take advantage of these opportunities and reduce risks where possible. The consequences of climate variability and climate change are potentially more significant for activities that depend on local weather and climatic conditions. The Garden Route in the Western Cape (southern region), is an agricultural region that is vulnerable to the impacts of climate change and climate variables; if these climatic conditions should change, productivity levels and livelihoods would be directly affected. This study examined how farmers’ perceptions of weather conditions have corresponded with the climatic data recorded at various meteorological stations in the Garden Route, South Africa, and whether these perceptions could be linked to an understanding of the ethical implications of climate change or not. Through the use of indepth interviews, the study analysed farmers’ adaptive responses, their perceptions and understanding of climate change, and their perceptions and understanding of the ethical challenges posed by climate change. The Heckman Probit Adaptation Model was used to examine perception and adaptation to climate change and climate variability. Main constraints cited by farmers in changing their ways of farming and adapting to climate change were obtaining rights to increasing their water storage capacities (increasing dam walls or building dams), flood water management, cash flow and financial support, obtaining permits to burn, and general support from official structures. Furthermore this study implemented a scenario-planning exercise to determine adaptation trends in the observed and projected climate for the Garden Route, with the aim of providing possible solutions for wiser agricultural practices. The following scenarios were compared: (1) If agricultural practices continue as per status quo – with no change in climatic conditions; (2) If agricultural patterns are significantly modified, to reduce agricultural impact on local biodiversity – with no change in climatic conditions; (3) If agricultural practices continue as per status quo – with significant change in climatic conditions; and (4) If agricultural practices are significantly modified, to reduce agricultural impact on local biodiversity, and taking account of likely changes in climatic conditions. The analyses – according to these four scenarios – indicated the likelihood of possible trends in future, using specific climate variables, together with possible adaptation strategies. With no change in climatic conditions, but a change in farming practices towards environmental protection, the farming sector may achieve sustainability. However, if climatic conditions should change, changes in farming practices may not be enough to guarantee its sustainability. Farmers in the Garden Route indicated that agricultural production on any scale is completely dependent on water, leaving this sector exposed and vulnerable posing substantial obstacles to farmers to continue farming in the same way. Farmers are now faced with the decision to “adapt or die”. The convergence of these factors has the potential to create a “perfect moral storm”. One consequence of this storm is that, even if the other difficult ethical questions surrounding climate change could be answered, farmers still may find it difficult to articulate what this moral storm could entail, and how to act upon it. / AFRIKAANSE OPSOMMING: Klimaatsverandering skep wêreldwyd beide risiko’s en geleenthede. Deur klimaatsverandering te verstaan, daarvoor te beplan en daarby aan te pas, kan individue en gemeenskappe hierdie geleenthede aangryp en, waar moontlik, die risiko’s verlaag. Die gevolge van klimaatskommelings en klimaatverandering is potensieel meer betekenisvol vir daardie aktiwiteite wat afhanklik is van plaaslike weer- en klimaatstoestande. Die landboustreek in die Tuinroete in die Wes-Kaap (suidelike streek) is gevoelig vir die impak van klimaatsverandering en klimaatskommelings; indien klimaatstoestande verander, sal produktiwiteitsvlakke en lewenskwaliteit direk beïnvloed word. Hierdie studie het ondersoek ingestel na die ooreenkoms tussen boere se persepsie van klimaatsverandering, en die klimaatsdata by verskeie meteorologiese stasies in die Tuinroete, Suid-Afrika en of hierdie persepsies verbind kan word aan 'n begrip van die etiese implikasies van klimaatsverandering of nie. By wyse van indiepte onderhoude het die studie boere se aanpassingmeganismes, hul persepsies en begrip van klimaatsverandering, asook hul persepsies en begrip van die etiese uitdagings van klimaatsverandering ontleed. Die Heckman Probit Aanpassings-Model is gebruik om die persepsie en aanpassing by klimaatsverandering en klimaatskommelings te bepaal. Boere het die volgende as die vernaamste struikelblokke in die verandering in landboupraktyke en aanpassing by klimaatsverandering beskou: a) verkryging van toestemming om wateropgaarkapasiteit te verhoog (die bou of verhoging van damme); b) vloedbestuur; c) kontantvloei en finansiële ondersteuning; d) verkryging van brandpermitte; en e) algemene ondersteuning vanaf amptelike instansies. Voorts het hierdie studie scenario-beplanning gebruik om tendense in die aanpassing by die waargenome en voorspelde klimaatsverandering in die Tuinroete te bepaal. Die doel hiervan is om moontlike oplossings vir beter landboupraktyke te verskaf. Die volgende scenario’s is met mekaar vergelyk: (1) Indien landboupraktyke voortgaan soos gewoonlik (status quo) – geen verandering in klimaatstoestande; (2) Indien landbou betekenisvol verander om die impak van landbou op plaaslike biodiversiteit te verlaag – geen verandering in klimaatstoestande; (3) Indien landboupraktyke voortgaan soos gewoonlik (status quo) – betekenisvolle verandering in klimaatstoestande; en (4) Indien landbou betekenisvol verander om die impak van landbou op plaaslike biodiversiteit te verlaag – met inagneming van moontlike veranderings in klimaatstoestande. By wyse van die vier scenario’s dui die analise moontlike toekomstige tendense aan deur gebruik te maak van spesifieke klimaatskommelings, tesame met moontlike aanpassingstrategieë. Met geen verandering in die klimaatstoestand kan die landbousektor volhoubaar wees indien landboupraktyke verander en omgewingsbeskerming in ag neem. Indien klimaatstoestande egter verander, mag gewysigde landboupraktyke nie genoeg wees om die volhoubaarheid daarvan te verseker nie. Boere in die Tuinroete het aangedui dat enige skaal van landbouproduksie geheel en al van water afhanklik is, wat hierdie sektor blootgestel en kwesbaar maak, en ‘n groot struikelblok is indien boere op dieselfde wyse bly boer. Boere is nou onderworpe aan die besluit om aan te pas of onder te gaan. Die sameloop van al hierdie faktore het die potensiaal om die “perfekte morele storm” te ontketen. Een gevolg van hierdie storm is dat, alhoewel ander moeilike etiese kwessies rondom klimaatsverandering beantwoord sou kon word, boere dit nog steeds moeilik mag vind om dié morele storm te omskryf en hoe om hierop te reageer.

Intelligent Transportation System (ITS) provides a great platform for the planners to reduce environmental externalities from auto. We now have access to real time data. We have been using shortest path to provide route choice to the user. But we have the potential to add more variables in choosing routes. Real time data can be used to measure carbon di-oxide emission during a trip. Also, fuel efficiency can be measured using the real time data. Planners should use this potential of ITS to reduce the environmental impact. This paper thus try to evaluate if considering three variables shortest path, environmental impact and fuel efficiency together instead of only shortest path will change the route choice or not. It provides case studies on different types of routes and between different sets of origin /destination to evaluate the combined influence of these three variables on route choice.

Ein viel diskutiertes Thema unserer Zeit ist die Zukunft der Energiegewinnung und Speicherung. Dabei nimmt die Nanowissenschaft eine bedeutende Rolle ein; sie führt zu einer Effizienzsteigerung bei der Speicherung und Gewinnung durch bereits bekannte Materialien und durch neue Materialien. In diesem Zusammenhang ist die Chemie Wegbereiter für Nanomaterialien. Allerdings führen bisher die meisten bekannten Synthesen von Nanopartikeln zu undefinierten Partikeln. Eine einfache, kostengünstige und sichere Synthese würde die Möglichkeit einer breiten Anwendung und Skalierbarkeit bieten. In dieser Arbeit soll daher die Darstellung der einfachen Synthese von Mangannitrid-, Aluminiumnitrid-, Lithiummangansilicat-, Zirkonium-oxinitrid- und Mangancarbonatnanopartikel betrachtet werden. Dabei werden die sogenannte Harnstoff-Glas-Route als eine Festphasensynthese und die Solvothermalsynthese als typische Flüssigphasensynthese eingesetzt. Beide Synthesewege führen zu definierten Partikelgrößen und interessanten Morphologien und ermöglichen eine Einflussnahme auf die Produkte. Im Falle der Synthese der Mangannitridnanopartikel mithilfe der Harnstoff-Glas-Route führt diese zu Nanopartikeln mit Kern-Hülle-Struktur, deren Einsatz als Konversionsmaterial erstmalig vorgestellt wird. Mit dem Ziel einer leichteren Anwendung von Nanopartikeln wird eine einfache Beschichtung von Oberflächen mit Nanopartikeln mithilfe der Rotationsbeschichtung beschrieben. Es entstand ein Gemisch aus MnN0,43/MnO-Nanopartikeln, eingebettet in einem Kohlenstofffilm, dessen Untersuchung als Konversionsmaterial hohe spezifische Kapazitäten (811 mAh/g) zeigt, die die von dem konventionellen Anodenmaterial Graphit (372 mAh/g) übersteigt. Neben der Synthese des Anodenmaterials wurde ebenfalls die des Kathodenmaterials Li2MnSiO4-Nanopartikeln mithilfe der Harnstoff-Glas-Route vorgestellt. Mithilfe der Synthese von Zirkoniumoxinitridnanopartikeln Zr2ON2 kann eine einfache Einflussnahme auf das gewünschte Produkt durch die Variation derReaktionsbedingungen, wie Harnstoffmenge oder Reaktionstemperatur, bei der Harnstoff-Glas-Route demonstriert werden. Der Zusatz von kleinsten Mengen an Ammoniumchlorid vermeidet, dass sich Kohlenstoff im Endprodukt bildet und führt so zu gelben Zr2ON2-Nanopartikeln mit einer Größe d = 8 nm, die Halbleitereigen-schaften besitzen. Die Synthese von Aluminiumnitridnanopartikeln führt zu kristallinen Nanopartikeln, die in eine amorphe Matrix eingebettet sind. Die Solvothermalsynthese von Mangancarbonatnanopartikel lässt neue Morphologien in Form von Nanostäbchen entstehen, die zu schuppenartigen sphärischen Überstrukturen agglomeriert sind. / The development of new methods toward alternative clean energy production and efficient energy storage is a hot topic nowadays. In this context nanoscience has an important role to find suitable ways of increasing the efficiency of storage and production of energy of already known materials and new materials. However, until now the most well-known syntheses of MnN0,43 and Zr2ON2 nanoparticles lead to undefined particles. A simple, cheap and safe synthesis would offer the possibility of broader applications and scalability. We herein present the so-called urea-glass route which is used as a sol-gel process. This synthetic route leads to well-defined particle sizes, novel particle morphologies and allows the tailoring of the desired products. In the case of the synthesis of manganese nitride nanoparticles (MnN0,43), nanoparticles with a core-shell structure are obtained, their use as conversion materials in batteries is first introduced. On the other hand, the formation of zirconium oxynitride nanoparticles (Zr2ON2) can be easily influenced by varying the reaction conditions such as the amount of urea or the reaction temperature. The addition of small amounts of salt prevents the formation of carbon in the final product, leading to yellow Zr2ON2 nanoparticles with a size of d = 8 nm which show semiconductor behavior.