Photophysical investigation of light-harvesting systems for solar-to-fuel conversion

In recent years, an increasing attention has been given to the optimization of the performances of new supramolecular systems, as antennas for light collection. In such background, the aim of this thesis was the study of multichromophoric architectures capable of performing such basic action. A synthetic antenna should consist of a structure with large UV-Vis absorption cross-section, panchromatic absorption, fixed orientation of the components and suitable energy gradients between them, in order to funnel absorbed energy towards a specific site, through fast energy-transfer processes. Among the systems investigated in this thesis, three suitable classes of compounds can be identified: 1) transition metal-based multichromophoric arrays, as models for antenna construction, 2) free-base trans-A2B-phenylcorroles, as self-assembling systems to make effective mimics of the photosynthetic system, and 3) a natural harvester, the Photosystem I, immobilized on the photoanode of a solar-to-fuel conversion device. The discussion starts with the description of the photophysical properties of dinuclear quinonoid organometallic systems, able to fulfil some of the above mentioned absorption requirements, displaying in some cases panchromatic absorption. The investigation is extended to the efficient energy transfer processes occurring in supramolecular architectures, suitably organized around rigid organic scaffolds, such as spiro-bifluorene and triptycene. Furthermore, the photophysical characterization of three trans-A2B-phenylcorroles with different substituents on the meso-phenyl ring is introduced, revealing the tendency of such macrocycles to self-organize into dimers, by mimicking natural self-aggregates antenna systems. In the end, the photophysical analysis moved towards the natural super-complex PSI-LHCI, immobilized on the hematite surface of the photoanode of a bio-hybrid dye-sensitized solar cell. The importance of the entire work is related to the need for a deep understanding of the energy transfer mechanisms occurring in supramolecules, to gain insights and improve the strategies for governing the directionality of the energy flow in the construction of well-performing antenna systems.

Employment of Real-Time/FPGA Architectures for Test and Control of Automotive Engines

Nowadays the production of increasingly complex and electrified vehicles requires the implementation of new control and monitoring systems. This reason, together with the tendency of moving rapidly from the test bench to the vehicle, leads to a landscape that requires the development of embedded hardware and software to face the application effectively and efficiently. The development of application-based software on real-time/FPGA hardware could be a good answer for these challenges: FPGA grants parallel low-level and high-speed calculation/timing, while the Real-Time processor can handle high-level calculation layers, logging and communication functions with determinism. Thanks to the software flexibility and small dimensions, these architectures can find a perfect collocation as engine RCP (Rapid Control Prototyping) units and as smart data logger/analyser, both for test bench and on vehicle application. Efforts have been done for building a base architecture with common functionalities capable of easily hosting application-specific control code. Several case studies originating in this scenario will be shown; dedicated solutions for protype applications have been developed exploiting a real-time/FPGA architecture as ECU (Engine Control Unit) and custom RCP functionalities, such as water injection and testing hydraulic brake control.

Taxonomy, phylogeography and myrmecophily of the spider genus Mastigusa (Araneae, Cybaeidae)

The genus Mastigusa Menge, 1854 includes small entelegyne spiders represented by extant and fossil species presenting characteristic features in male and female genitalia. The genus has a palearctic distribution, being present in Europe, North Africa, and the Near East, and shows ecological plasticity, with free-living, cave- dwelling and myrmecophile populations. The taxonomic history of the genus has been problematic, both regarding its phylogenetic placement and the delimitation of the species it includes. Three extant species are currently recognized, but the characters used to discriminate them have been inconsistent, leading to confusion about their identification and distribution. In the present thesis we addressed the taxonomic issues regarding Mastigusa by combining molecular and morphological data in an integrative taxonomy approach. For the first time, we included the genus in a molecular phylogenetic matrix solving a long going debate regarding its familiar placement, obtaining a well-supported placement in the family Cybaeidae. We used multi-locus molecular phylogenetic and DNA barcoding techniques as a starting point for identifying divergent lineages within the genus and revise the taxonomic status of the three known Mastigusa species, identifying a new species from the Iberian Peninsula, Algeria and the United Kingdom: M. raimondi sp. n. This taxonomic revision allowed a phylogeographic and ecological study of Mastigusa across its distribution range, carried out using phylogenetics and ecological niche modelling techniques, aiming at a comparison of the lifestyles and ecological requirements of the different species on a geographic scale. The Italian Alps were finally used as a testing ground for investigating the ecology and host preference of myrmecophile Mastigusa arietina populations living in association with ant species belonging to the Formica rufa species group. Spiders were found in association with five different Formica species, demonstrating little specificity and the tendency of associating with the locally present host species.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.