This is a project thesis that will examine how facts about electric vehicles are presented to customers.That means presenting data and prove it right or wrong. To help proving if the information is truthful, it’sincluded in the project to make a measuring device that can be deployed in any vehicle. The main focus ison consumption during day to day use. The project also includes planning and executing an electric vehicleGrand Prix. This Grand Prix is a way of spreading knowledge about electric vehicles and also collect necessarydata. This thesis proves that information about electric vehicles is not always truthful. The information isnot adjusted to normal day to day use or varying climate.

A cretaceous (Cenomanian) flora from the Dakota Sandstone Formation near Westwater, Grand County, Utah contains an admixture of ferns and angiosperms. The ferns of this flora are representative of an older Jurassic-Wealden vegetational type, whereas the angiosperms are typical of the modern vegetational type. Species of Gleichenia and Matonidium and Astralopteris coloradica represent the dominant forms in this flora. The Westwater flora contains fourteen genera including nineteen species and one variety. New species described from this flora include Asplenium dakotensis, Coniopteris westwaterensis and Ilex serrata.

Molecular dynamics (MD) simulations find their applications in fundamental understanding of molecular level mechanisms of physical processes. This assists in tuning the key features affecting the development of the novel hybrid materials. A certain application demanding the need for a desired function can be cherished through the hybrids with a blend of new properties by a combination of pure materials. However, to run MD simulations, an accurate representation of the interatomic potentials i.e. force-fields (FF) models remain a crucial aspect. This thesis intricately explores the fusion of MD simulations, uncertainty quantification, and data-driven methodologies to accelerate the computational design of innovative materials and models across the following interconnected chapters. Beginning with the development of force fields for atomic-level systems and coarse-grained models for FCC metals, the study progresses into exploring the intricate interfacial interactions between 2D materials like graphene, MoS2, and water. Current state-of-the-art model development faces the challenge of high dimensional input parameters' model and unknown robustness of developed model. The utilization of advanced optimization techniques such as particle swarm optimization (PSO) integrated with MD enhances the accuracy and precision of FF models. Moreover, the bayesian uncertainty quantification (BUQ) assists FF model development researchers in estimating the robustness of the model. Furthermore, the complex structure and dynamics of water confined between and around sheets was unraveled using 3D Convolutional Neural Networks (3D-CNN). Specifically, through classification and regression models, water molecule ordering/disordering and atomic density profiles were accurately predicted, thereby elucidating nuanced interplays between sheet compositions and confined water molecules. To further the computational design of hybrid materials, this thesis delves into designing and investigating polymer composites with functionalized MOFs shedding light on crucial factors governing their compatibility and performance. Therefore, this report includes the study of structure and dynamics of functionalized MOF in the polymer matrix. Additionally, it investigates the biomedical potential of porous MOFs as drug delivery vehicles (DDVs). Often overlooked is the pivotal role of solvents (used in MOF synthesis or found in relevant body fluids) in the drug adsorption and release process. This report underscores the solvent's impact on drug adsorption within MOFs by comparing results in its presence and absence. Building on these findings, the study delves into the effects of MOF functionalization on tuning the drug adsorption and release process. It further explores how different physical and chemical properties influence drug adsorption within MOFs. Furthermore, the research explores the potential of functionalized MOFs for improved carbon capture, considering their application in energy-related contexts. By harnessing machine learning and deep learning, the thesis introduces innovative pathways for material property prediction and design, emphasizing the pivotal fusion of computational methodologies with data-driven approaches to advance molecular-level understanding and propel future material design endeavors. / Doctor of Philosophy / Envision a world where scientific exploration reaches the microscopic scale, powered by advanced computational tools. In this frontier of materials science, researchers employ sophisticated computer simulations to delve into the intricate properties of materials, particularly focusing on Metal-Organic Frameworks (MOFs). These MOFs, equivalent to microscopic molecular sponges, exhibit remarkable abilities to capture gases or hold medicinal drug compounds. This thesis meticulously studies MOFs alongside materials like graphene, Boron Nitride and Molybdenum disulfide, investigating their interactions with water with unprecedented precision. Through these detailed explorations and the fusion of cutting-edge technologies, we aim to unlock a future featuring enhanced drug delivery systems, improved energy storage solutions, and innovative energy applications.

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