Silica-Supported Gold Nanoparticles: Synthesis, Characterization and Reactivity

The main aim of this work was the synthesis and applications of functionalized-silica-supported gold nanoparticles. The silica-anchored functionalities employed, e.g. amine, alkynyl carbamate and sulfide moieties, possess a notable affinity with gold, so that they could be able to capture the gold precursor, to spontaneously reduce it (possibly at room temperature), and to stabilize the resulting gold nanoparticles. These new materials, potentially suitable for heterogeneous catalysis applications, could represent a breakthrough among the “green” synthesis of supported gold nanoparticles, since they would circumvent the addition of extra reducing agent and stabilizers, also allowing concomitant absorption of the active catalyst particles on the support immediately after spontaneous formation of gold nanoparticles. In chapter 4 of this thesis is also presented the work developed during a seven-months Marco Polo fellowship stay at the University of Lille (France), regarding nanoparticles nucleation and growth inside a microfluidic system and the study of the corresponding mechanism by in situ XANES spectroscopy. Finally, studies regarding the reparation and reactivity of gold decorated nanodiamonds are also described. Various methods of characterization have been used, such as ultraviolet-visible spectroscopy (UV-Vis), Transmission Electron Microscopy (TEM), Dynamic Light Scattering (DLS), X-ray Fluorescence (XRF), Field Emission Gun Scanning Electron Microscopy (SEM-FEG), X-ray Photoionization (XPS), X ray Absorption Spectroscopy (XAS).

Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.

Serverless middlewares to integrate heterogeneous and distributed services in cloud continuum environments

The application of modern ICT technologies is radically changing many fields pushing toward more open and dynamic value chains fostering the cooperation and integration of many connected partners, sensors, and devices. As a valuable example, the emerging Smart Tourism field derived from the application of ICT to Tourism so to create richer and more integrated experiences, making them more accessible and sustainable. From a technological viewpoint, a recurring challenge in these decentralized environments is the integration of heterogeneous services and data spanning multiple administrative domains, each possibly applying different security/privacy policies, device and process control mechanisms, service access, and provisioning schemes, etc. The distribution and heterogeneity of those sources exacerbate the complexity in the development of integrating solutions with consequent high effort and costs for partners seeking them. Taking a step towards addressing these issues, we propose APERTO, a decentralized and distributed architecture that aims at facilitating the blending of data and services. At its core, APERTO relies on APERTO FaaS, a Serverless platform allowing fast prototyping of the business logic, lowering the barrier of entry and development costs to newcomers, (zero) fine-grained scaling of resources servicing end-users, and reduced management overhead. APERTO FaaS infrastructure is based on asynchronous and transparent communications between the components of the architecture, allowing the development of optimized solutions that exploit the peculiarities of distributed and heterogeneous environments. In particular, APERTO addresses the provisioning of scalable and cost-efficient mechanisms targeting: i) function composition allowing the definition of complex workloads from simple, ready-to-use functions, enabling smarter management of complex tasks and improved multiplexing capabilities; ii) the creation of end-to-end differentiated QoS slices minimizing interfaces among application/service running on a shared infrastructure; i) an abstraction providing uniform and optimized access to heterogeneous data sources, iv) a decentralized approach for the verification of access rights to resources.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

Investigation on the role of polymeric ligands in heterogeneous nanocatalysis towards the development of hybrid metal-polymer catalysts

The relationship between catalytic properties and the nature of the active phase is well-established, with increased presence typically leading to enhanced catalysis. However, the costs associated with acquiring and processing these metals can become economically and environmentally unsustainable for global industries. Thus, there is potential for a paradigm shift towards utilizing polymeric ligands or other polymeric systems to modulate and enhance catalytic performance. This alternative approach has the potential to reduce the requisite amount of active phase while preserving effective catalytic activity. Such a strategy could yield substantial benefits from both economic and environmental perspectives. The primary objective of this research is to examine the influence of polymeric hydro-soluble ligands on the final properties, such as size and dispersion of the active phase, as well as the catalytic activity, encompassing conversion, selectivity towards desired products, and stability, of colloidal gold nanoparticles supported on active carbon. The goal is to elucidate the impact of polymers systematically, offering a toolbox for fine-tuning catalytic performances from the initial stages of catalyst design. Moreover, investigating the potential to augment conversion and selectivity in specific reactions through tailored polymeric ligands holds promise for reshaping catalyst preparation methodologies, thereby fostering the development of more economically sustainable materials.