Theoretical Studies on the Influence of Size and Support Interactions of Copper Catalysts for CO2 Hydrogenation to Methanol

Global warming and climate change, caused by greenhouse gases (GHG) released into the atmosphere by human activities, are becoming one of the world’s most crucial issues. Carbon dioxide (CO2) is the primary emitted greenhouse gas, produced mostly from the usage of fossil fuels. The daily increase in the global energy demand and the promising potential of converting and using the captured emitted CO2 to value-added products and chemicals (e.g., methanol) resulted in a vast amount of inventions and investigations on this topic. Methanol is the simplest alcohol and one of the valuable converted products of the CO2 conversion process, which can be used as renewable energy, fuels, etc. In industry, methanol is synthesized through heterogeneous catalytic reactions utilizing Cu-based catalysts, promoted or unpromoted nanoparticles (NPs) on support materials. Theoretical and computational methods of modelling heterogeneous catalytic reactions, done mostly by applying density functional theory (DFT) methods, are one example of benefiting from computer-aided material designing. However, investigating this procedure is challenging as the difference in the size scale of the study systems between the experiments and theoretical works are different. DFT is perfectly capable of handling systems consisting of a few hundred atoms, which contrasts with real systems with thousands of atoms. In this thesis, by applying cost-efficient and, at the same time, highly accurate computational models, some critical challenges regarding the procedure of converting CO2 to methanol, such as the catalyst’s particle size and shape effect, support effect, and applications of trends in catalytic reactions, were investigated. The utilization of DFT provided a fundamental understanding of the properties of the transition metal (TM) catalysts, with sizes ranging from 0.5 nm to 3.6 nm, using their fixed geometries and the adsorption energies of the intermediates related to methanol synthesis as descriptors. In addition, after confirming the reliability of the proposed models for different transition metals, the influence of the supports, inert (graphene) and oxide (magnesium oxide (MgO)), on the properties of NPs, through adsorption studies with different sizes and shapes was determined. Moreover, the development of the models mentioned paved the way toward finding the trends in catalytic reactions through linear scaling relationships between the adsorption energies of the intermediates involved in methanol synthesis. In addition to the linear scaling relationships in the adsorption energies, the important role of geometrical and electronic effects affecting these relations is also demonstrated. Finally, the origin of the copper particle size effect, which plays a significant role in CO2 hydrogenation to methanol and its competitor reverse water gas shift reaction (RWGS), is demonstrated as the reaction being clearly structure sensitive and providing a new guideline in designing of novel catalysts for methanol synthesis from CO2

Optimization and Energy Maximizing Control Systems for Wave Energy Converters

The book, “Optimization and Energy Maximizing Control Systems for Wave Energy Converters”, presents eleven contributions on the latest scientific advancements of 2020-2021 in wave energy technology optimization and control, including holistic techno-economic optimization, inclusion of nonlinear effects, and real-time implementations of estimation and control algorithms

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden