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Solvent-free extrusion emulsification (SFEE) is a novel emulsification technology that operates without solvent to produce sub-micron sized particles (100–200 nm) using a twin-screw extruder (TSE) with high viscosity polymers (up to 600 Pa.s has been tested to date) and only water as the liquid medium. Surfactants have always been known to play a key role in the success of the SFEE process, however very little work has been done to investigate the mechanisms by which they operate, along with isolating the region of the process to which they play the most vital role. The first part of this thesis focused on an investigation into how different surface-active properties impacted the mechanism of SFEE. Three ionic (SDBS, Unicid 350, Calfax DB-45) and three non-ionic surfactants (Igepal CO-890, Brij 58, Synperonic F-108), each with differing surface-active properties were tested in solvent emulsification (SE) prior to their evaluation in SFEE. Synperonic F-108 was the only surfactant found unsuccessful in the SE process, and was therefore disregarded prior to SFEE testing. Of the three ionic surfactants, SDBS and Calfax were the only ones found to successfully create a stable emulsion in SFEE; the latter species doing so with 50% reduced molar loading. Igepal and Brij were found to produce very low amounts of emulsified material (5-25% of the total solids mass), requiring molar loadings that greatly exceed those of SDBS and Calfax to do so. Particles generated by both SE and SFEE were tested at extreme operating conditions to compare their relative stabilities, and were found to experience similar stability behaviours. This result reinforces previous findings that the dispersion stage controls the SFEE technique. The second part of this thesis continued the investigation on the use of non-ionics in SFEE, with a focus on the impact of their molecular structure on the overall process. Non-ionic surfactants with varying hydrophilic end group chain lengths were tested in SFEE, and it was determined that the optimal hydrophilic chain length was between 10–12 ethoxy units, where shorter chains resulted in coarse particle generation. The structure of the hydrophobic end group was tested as well, and through experimentation it was determined that a branched end group structure was slightly more beneficial than a linear end group to emulsion stabilization. As seen in the first part of this thesis, none of the new selection of non-ionic surfactants were capable of inducing sufficient phase inversion to result in a high percentage of emulsion leaving the extruder. The most promising ionic surfactant, Calfax DB-45, was combined with various promising non-ionic surfactants to create binary surfactant mixtures, and were tested in SFEE. Initial results yielded the most promising blend as Calfax/Igepal CA-630. After manipulation of both molar ratio and total surfactant loading, it was determined that a minimum Calfax loading of 0.06 mmol/g resin was required in the blend to achieve a stable 100 – 200 nm emulsion in both SE and SFEE processes, regardless of non-ionic concentration. The benefits of adding a non-ionic surfactant in the blend were seen with the substantial reduction of Calfax entrapped in the final latex particles, apparent by the distinct decrease in overall particle charge. A mini-study examining the impacts of increasing the viscosity of the water phase by hydrocolloid addition for the dilution stage has shown that positive changes to emulsion properties can be seen by this approach, but further experimentation is required before concrete conclusions can be made. / Thesis / Master of Applied Science (MASc) / The creation of nanoparticles has been a growing area of research in recent years, with numerous different means of generation being developed. Extruders have seldom been used for the generation of nanoparticles due to issues related to controlling generated particle characteristics. Previous work has shown that twin-screw extruders are capable of generating 100–200 nm particles, but the process has shown minimal robustness to variations in operating conditions. The aim of this study has been to continue the work of nanoparticle generation within a twin-screw extruder, with a specific focus on the impacts that special soap-like particles (surfactants) have on the process. Surfactants are special particles consisting of both a hydrophilic (“water-loving”) and hydrophobic (“water-hating”) end group that allows multiple substances to combine on a chemical level. Variations in the molecular structure and electronic charge of these surfactants, along with blends of different types of surfactants have been tested to gain a better understanding of their role in the process, and hopefully increase the overall robustness of the process. Overall, it was determined that surfactants with a negative charge were more successful in creating polyester latex particles than ones with a neutral molecular structure. The blending of a charged and neutral surfactant has been shown in this study to not only be successful in generating particles of desired size, but have also shown the ability to reduce the overall charge of the final latex particles.

No / Mechanochemistry provides a method to reduce or eliminate the use of solvents by carrying out reactions through the grinding of neat reagents. Until recently a significant drawback of this form of synthesis has been the limited ability to scale up. However, it has been shown that twin screw extrusion (TSE) may overcome this problem as demonstrated in the continuous synthesis of co-crystals, Metal Organic Frameworks (MOFs) and Deep Eutectic Solvents (DES), in multi kg h−1 quantities. TSE has provided a means to carry out mechanochemical synthesis in a continuous, large scale and efficient fashion, which is adaptable to a manufacturing process. Herein, we highlight the potential of this technique for organic synthesis by reporting four condensation reactions, the Knoevenagel condensation, imine formation, aldol reaction and the Michael addition, to produce analytically pure products, most of which did not require any post synthetic purification or isolation. Each reaction was carried out in the absence of solvents and the water byproduct was conveniently removed as water vapour during the extrusion process due to the elevated temperatures used. Furthermore, the Knoevenagel condensation has been studied in detail to gain insight into the mechanism by which these mechanochemical reactions proceed. The results point to effective wetting of one reactant by another as being critical for these reactions to occur under these reaction conditions. / EPSRC EP/L019655/1

Artificial intelligence (AI) has progressed significantly during the last several decades, along with the rapid advancements in computational power. This advanced technology is currently being employed in various engineering fields, not just in computer science. In aerospace engineering, AI and machine learning (ML), a major branch of AI, are now playing an important role in various applications, such as automated systems, unmanned aerial vehicles, aerospace optimum design structure, etc. This dissertation mainly focuses on structural engineering to employ AI to develop lighter and safer aircraft structures as well as challenges involving structural optimization and analysis. Therefore, various ML applications are studied in this research to provide novel frameworks for structural optimization, analysis, and design. First, the application of a deep-learning-based (DL) convolutional neural network (CNN) was studied to develop a surrogate model for providing optimum structural topology. Typically, conventional structural topology optimization requires a large number of computations due to the iterative finite element analyses (FEAs) needed to obtain optimal structural layouts under given load and boundary conditions. A proposed surrogate model in this study predicts the material density layout inputting the static analysis results using the initial geometry but without performing iterative FEAs. The developed surrogate models were validated with various example cases. Using the proposed method, the total calculation time was reduced by 98 % as compared to conventional topology optimization once the CNN had been trained. The predicted results have equal structural performance levels compared to the optimum structures derived by conventional topology optimization considered ``ground truths". Secondly, reinforcement learning (RL) is studied to create a stand-alone AI system that can design the structure from trial-and-error experiences. RL application is one of the major ML branches that mimic human behavior, specifically how human beings solve problems based on their experience. The main RL algorithm assumes that the human problem-solving process can be improved by earning positive and negative rewards from good and bad experiences, respectively. Therefore, this algorithm can be applied to solve structural design problems whereby engineers can improve the structural design by finding the weaknesses and enhancing them using a trial and error approach. To prove this concept, an AI system with the RL algorithm was implemented to drive the optimum truss structure using continuous and discrete cross-section choices under a set of given constraints. This study also proposed a unique reward function system to examine the constraints in structural design problems. As a result, the independent AI system can be developed from the experience-based training process, and this system can design the structure by itself without significant human intervention. Finally, this dissertation proposes an ML-based classification tool to categorize the vibrational mode shapes of tires. In general, tire vibration significantly affects driving quality, such as stability, ride comfort, noise performance, etc. Therefore, a comprehensive study for identifying the vibrational features is necessary to design the high-performance tire by considering the geometry, material, and operation conditions. Typically, the vibrational characteristics can be obtained from the modal test or numerical analysis. These identified modal characteristics can be used to categorize the tire mode shapes to determine the specific mode cause poorer driving performances. This study suggests a method to develop an ML-based classification tool that can efficiently categorize the mode shape using advanced feature recognition and classification algorithms. The best-performed classification tool can accurately predict the tire category without manual effort. Therefore, the proposed classification tool can be used to categorize the tire mode shapes for subsequent tire performance and improve the design process by reducing the time and resources for expensive calculations or experiments. / Doctor of Philosophy / Artificial intelligence (AI) has significantly progressed during the last several decades with the rapid advancement of computational capabilities. This advanced technology is currently employed to problems in various engineering fields, not just problems in computer science. Machine learning (ML), a major branch of AI, is actively applied to mechanical/structural problems since an ML model can replace a physical system with a surrogate model, which can be used to predict, control, and optimize its behavior. This dissertation provides a new framework to design and analyze structures using ML-based techniques. In particular, the latest ML technologies, such as convolutional neural networks, widely used for image processing and feature recognition, are applied to replace numerical calculations in structural optimization and analysis with the ML-based system. Also, this dissertation suggests how to develop a smart system that can design the structure by itself using reinforcement learning, which is utilized for autonomous driving systems and robot walking algorithms. Finally, this dissertation suggests an ML-based classification approach to categorize complex vibration modes of a structure.

L’Intelligent Dance Music (IDM) est un courant de musique électronique semi-expérimental qui a émergé au Royaume-Uni au début des années 1990. Souvent considérée comme une musique cérébrale peu propice à la danse, l’IDM n’avait jamais fait l’objet de l’étude nécessaire à la justification d’un tel jugement. Les analyses formelles menées ici sur des œuvres représentatives d’Aphex Twin, pionnier et figure principale du courant, montrent une démarche sophistiquée de complexification rythmique qui a pour effet de solliciter activement l’attention de l’auditeur. D’une part, l’asymétrie organisée aux différents niveaux du rythme provoque des phénomènes d’ambigüité et de rupture sur le plan de la perception métrique. D’autre part, l’abondance d’ensembles microrythmiques, parfaitement synchronisés avec la structure métrique, produit un dense et précis enrichissement du rythme opéré sous forme d’agréments gestuels ou texturaux. Ces particularités musicales résultent d’un lot de choix prédominants définissant le style de l’artiste et, par la même occasion, fournissent les premiers éléments d’une définition de l’IDM basée sur des caractéristiques formelles. / Intelligent Dance Music (IDM) is a semi-experimental genre of electronic music that emerged in UK at the turn of the 1990s. Even though IDM has been described as a kind of cerebral music not appropriate for dancing, it has not been considered worth of musicological study until now. This thesis presents formal analyses of pieces by pioneering act and main IDM figure Aphex Twin: listeners’ attention is notably drawn by Aphex Twin's sophisticated approach and complex rhythmic structures. On the one hand, the different rhythmic layers are characterized by an organized asymmetry leading to ambiguity and rupture in the domain of metrical perception. On the other, the large amount of microrhythmic elements, all perfectly synchronized to the metric structure, contribute to greatly enrich rhythm through gestural and textural material. These musical characteristics, that are the result of a number of choices peculiar to the artist’s style, open the way to a formal definition of IDM.

Background: The aim of this study was to compare the cephalometric changes in patients Class II division 1treated with Twin Block and MARA appliances. Material and Methods: The sample comprised 66 patients with Class II malocclusion divided into 3 groups: The Twin Block group consisted of 21 patients, the MARA group of 21 patients and the control group of 24 untreated subjects. Intergroup comparisons were performed at pre- (T1) and posttreatment (T2) stages. The initial and posttreatment measures concerning changes in angular and linear variables were compared using the analysis of variance (ANOVA) and Kruskal-Wallis tests. Results: Both appliances showed improvement of the relation Class II. MARA resulted in a significant increase in FMA and the occlusal plane, 1º more than control. Twin Block revealed significantly greater increase in LAFH than the others. MARA produced a significantly greater amount of labial tipping and protrusion of the mandibular incisors than the others groups. TB showed significant extrusion of the mandibular incisors compared to MARA and extrusion of the mandibular molars in relation to the control. Both treated groups showed improvement in the overjet, overbite and molar relationship. The treatment time for MARA almost was 1 year less than TB. Conclusion: The appliances were effective in the treatment of Class II malocclusion; however, the correction was mainly due to dentoalveolar effects. / Proposição: O objetivo deste estudo foi comparar as alterações cefalométricas em pacientes Classe II divisão 1 tratados com os aparelhos Twin Block e MARA. Material e Métodos: A amostra foi composta por 66 pacientes com má oclusão de Classe II divididos em 3 grupos: o grupo Twin Block composto por 21 pacientes, o grupo MARA constituído por 21 e o grupo controle com 24 indivíduos não tratados. Comparações intergrupos foram realizadas nos estágios pré (T1) e pós- tratamento (T2). As medidas iniciais e pós-tratamento referentes as alterações nas variáveis angulares e lineares foram comparadas pelos seguintes testes: análise de variância (ANOVA) e Kruskal- Wallis. Resultados: Ambos os aparelhos apresentaram melhora na relação de Classe II. O MARA resultou em um aumento significante do FMA e do plano oclusal em relação ao controle. O Twin Block revelou um aumento significantemente maior na AFAI em relação aos outros grupos. O MARA produziu um aumento significantemente maior na inclinação vestibular e protrusão dos incisivos inferiores em relação aos outros grupos. O Twin Block promoveu significativa extrusão dos incisivos inferiores em relação ao MARA e significativa extrusão dos molares inferiores em relação ao controle. Ambos os grupos experimentais resultaram em melhora no trespasse horizontal, vertical e relação molar. O tempo de tratamento do MARA foi próximo de 1 ano a menos em relação ao Twin Block. Conclusão: Os aparelhos foram eficazes no tratamento da má oclusão de Classe II, porém a correção ocorreu em sua maior parte por efeitos dentoalveolares.

Com as evoluções tecnológicas nas áreas de hardware, microeletrônica, sistemas de informação e computação, o conceito de sistemas ciberfísicos (do inglês Cyber-Physical Systems) vem ganhando importância. Este sistemas se referem à junção entre sistemas computacionais distribuídos e processos físicos da natureza e, são base fundamental para a nova revolução industrial que esta sendo introduzida. Esta revolução industrial é marcada pela completa descentralização do controle dos processos produtivos e uma proliferação de dispositivos inteligentes interconectados, ao longo de toda a cadeia de produção e logística. Sistemas de automação, e particularmente os sistemas de automação industrial, nos quais elementos computacionais controlam e automatizam a execução de processos físicos em plantas industriais, são um exemplo de sistemas ciber-físicos. Com isso, percebe-se que é necessário relacionar objetos físicos a informações associadas a este objeto no mundo cibernético. Para isso, destaca-se o conceito e o uso do Digital Twin, que é uma representação virtual de objetos físicos. O Digital Twin possibilita a virtualização e centralização do controle no produto. Este estudo irá explorar uma metodologia de modelagem genérica e flexível para o Digital Twin usando a ferramenta AutomationML e propor uma arquitetura de comunicação para a troca de dados sob a ótica de Cyber Physical Systems. Com a implementação dessa metodologia, pretende-se validar o conceito proposto e oferecer um método de modelagem e configuração para obter dados, extrair conhecimento e proporcionar sistemas de visualização para os usuários. / With technological advances in the fields of hardware, microelectronics and computer systems, Cyber Physical Systems is a new concept that is gaining importance. This systems are integrations of computation, networking, and physical processes. Cyber Physical Systems are one of the pillars for the new industrial revolution, and it is marked by the complete decentralization of the control of production processes and, marked by a proliferation of interconnected intelligent devices throughout the production and logistics chain. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. A industrial automation system, is an example of cyber physical systems where computational elements control and automate the execution of physical processes in industrial plants. Thus, it is clear the need to relate physical objects to information associated with this object in the cyber world. For this, this work pretends to use the concept of Digital Twin, that is a virtual representation of physical objects. Digital Twin enables the virtualization of physical components and descentralization of control. This study will explore a generic and flexible modeling methodology for Digital Twin using the AutomationML tool. Also this work proposes a communication architecture for the exchange of data from the perspective of Cyber Physical Systems. With the implementation of this methodology, we intend to validate the proposed concept and offer a modeling and configuration method to obtain data, extract knowledge and provide visualization systems for users.