Politecnio die Bari - Catalogo di prodotti della Ricerca
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Waste valorization for sustainable advanced materials and catalysts
Get PDFThe process of waste valorization, which involves converting trash materials into valuable resources, is particularly relevant in today's world, given the pressing environmental and economic challenges. Current research focuses on the valorization of diverse waste streams: steel slags from metallurgical production, cigarette butts, and invasive tunicates from aquaculture for producing cellulose.
Steel slags were utilized as support for copper and iron oxide catalysts, thanks to their alkaline features. The Cu/Steel Slags catalysts showed high activity in the reduction of nitroarenes in water in the presence of sodium borohydride as the reducing agent at room temperature. The FeOx/Steel Slags catalysts were employed in the catalytic transfer hydrogenation of nitrobenzene in the presence of isopropanol as the hydrogen source, without adding any external base, such as potassium or so-dium hydroxide.
Concerning the valorization of cigarette butts, cellulose acetate was success-fully recovered from them using inexpensive and sustainable solvents, such as water, NaCl solution and ethanol. An optimized purification protocol was developed, yielding high-purity cellulose acetate suitable for reuse in polymer applications.
Furthermore, in the framework of invasive tunicate valorization, an innovative and sustainable protocol for efficient extraction of cellulose from them was developed. The species under study, Clavelina oblonga, was subjected to acidic deep eu-tectic solvent (DES) treatment by using choline chloride and oxalic acid in 1:1 molar ratio under microwave irradiation obtaining purified cellulose having proprieties comparable to those observed by using conventional extractive procedures based on toxic and expensive reagents
Control and optimization strategies for noise transmission reduction in sonic crystal acoustic barriers
No full textAcoustic metamaterials, such as Sonic Crystals (SC), are periodic artificial structures designed to manipulate sound waves in ways that natural materials cannot. These materials consist of arranged lattices of rigid scatterers embedded in air and exhibit unique properties, such as negative refraction and Band Gaps (BG), which can be leveraged to create highly effective noise control solutions. These Band Gaps prevent transmission of certain frequency ranges depending on the angle of incidence the sound waves relative to the crystal, making them ideal for applications in noise barriers and other acoustic devices.
This research investigates the potential of these advanced materials to enhance the performance of noise reduction devices through innovative design and optimization strategies aimed at minimizing unwanted sound, thereby improving the acoustic environment in various settings. Specifically, among noise reduction devices such as barriers, absorbers, and diffusers, this thesis focuses on Sonic Crystal Noise Barriers (SCNB).
The design and optimization of these devices require a deep understanding of acoustic principles and the ability to predict how sound interacts with different materials and structures. This is achieved through the use of simulations that forecast the behavior and performance of these devices, thus saving time and resources by avoiding the need to manufacture and experimentally test multiple prototypes before arriving at the optimal solution for each specific case.
In line with this, the thesis explores the potential of Active Noise Control (ANC) systems enhanced with Reinforcement Learning (RL) techniques as a complement to improve SCNBs. By integrating reinforcement learning, the study aims to develop ANC systems that continuously learn and adapt to changing environmental conditions, providing a dynamic and adaptive approach to noise control, particularly at low frequencies, where active control is most effective, and where SCNBs tend to be less efficient.
In addition to ANC, Helmholtz Resonators (HR) are incorporated to improve these barriers. By combining numerical methods and optimization algorithms, not only is the performance of SCNBs found in the literature improved, but a deeper understanding of the physical principles governing them and their interaction with HR is also achieved. 3D printing technology, used in architectural engineering, combined with parametric modeling, greatly increases the versatility in the creation of prototypes resulting from optimizations with HRs.
This thesis also addresses the insulation capabilities of SCNBs made from cylindrical scatterers with multiple Helmholtz resonators, proposing a design that incorporates two Helmholtz resonators per scatterer. This design shows a significant increase in Insertion Loss (IL) compared to conventional barriers. Additionally,examining the interaction between the BGs of HRs and Bragg-BGs, this thesis proposes new transmission applications for multiresonant SCs beyond noise barriers. The ability to control transmission through an active metamaterial by rotating the scatterers provides an advantage over conventional passive metamaterials.
Furthermore, the thesis explores, both numerically and experimentally, wave incidence measurements to further refine noise control strategies. Prototypes designed to mitigate tonal noise, such as that produced by train braking, are experimentally tested under both normal incidence and diffuse incidence of sound waves on the SCNB.
In summary, this doctoral thesis provides a comprehensive exploration of SCNBs, from design and optimization to practical applications. It delves into the phenomenology of the interactions between these periodic materials, ANC, HRs, and 3D printing. By integrating advanced optimization techniques and reinforcement learning, the study aims to improve current noise control solutions
Search for resonant pair production of Higgs bosons in the final state using large-area jets in proton-proton collisions at = 13 TeV
No full textPredictability Verification of Fault Patterns in Labeled Petri Nets
No full textThis paper focuses on the predictability verification problem of fault patterns for both bounded and unbounded discrete event systems modeled with labeled Petri nets. A system is said to be predictable with respect to a fault pattern if any complete fault behavior in a fault pattern can be correctly predicted before its occurrence, where the fault patterns are characterized by a particular composition of a labeled Petri net with a fault pattern net. In this paper, we construct a fault pattern predictor net and a basis fault pattern predictor graph that is based on the notion of basis markings. By exploiting the fault pattern predictor net and basis fault pattern predictor graph, we derive a necessary and sufficient condition to check fault pattern predictability, which does not need the full reachability/coverability graph of a net, thus gaining practical computation benefits
Comprehensive Systematic Literature Review on Cognitive Workload: Trends on Methods, Technologies, and Case Studies
No full textCognitive workload (CWL) assessment has gained traction in Industry 4.0 and 5.0, where human-machine interactions are becoming more intricate. However, there is a lack of comprehensively addressed CWL assessment by considering methodologies, technologies, and case studies. The present work reviews 70 articles related to the CWL assessment. The review identifies five main methodologies for the CWL assessment: physiological measures (e.g. EEG, HRV, and eye-tracking), subjective evaluation (e.g. NASA-TLX), performance evaluation, cognitive load models, and multimodal approaches. The analysis shows an increasing trend towards multimodal approaches that combine subjective assessment methods with physiological measures obtained from electroencephalography, eye-tracking, and heart rate monitoring devices. Additionally, emerging technologies such as augmented reality and collaborative robots are increasingly considered in case studies that address the CWL assessment in current work environments. Results reveal significant advancements in physiological and multimodal assessment methods, particularly emphasising real-time monitoring capabilities and context-specific applications. Case studies underscore the key role of CWL management in assembly, maintenance, and construction tasks, demonstrating its impact on performance, safety, and adaptability in dynamic environments. This review establishes a framework for advancing CWL research by addressing methodological limitations and proposing future research directions, including the development of personalised, adaptive systems for real-time workload management
On homotopy properties of solutions of some differential inclusions in the W 1, P -topology
No full textWe consider a differential inclusion on a manifold, defined by a field of open half-spaces whose boundary in each tangent space is the kernel of a one-form ω. We make the assumption that the corank one distribution associated to the kernel of ω is completely nonholonomic of step 2. We identify a subset of solutions of the differential inclusion, satisfying two endpoints and periodic boundary conditions, which are homotopy equivalent in the W1,p-topology, for any p ∈ [1,+∞), to the based loop space and the free loop space respectively
Synergistic dye/photocatalyst interconnections for activating efficient light-induced degradation pathways
No full textEffective Rabi frequency in quantum cascade lasers and its role in the origin of harmonic frequency combs
No full textMeasurement of the double-differential inclusive jet cross section in proton-proton collisions at = 5.02 TeV
No full textCausal Loop Diagrams for bridging the gap between Water-Energy-Food-Ecosystem Nexus thinking and Nexus doing: Evidence from two case studies
Get PDFThe concept of Nexus management is gaining increasing attention in the scientific community as it emphasizes the mutual interdependencies among different sectors (typically Water, Energy, Food and Ecosystems − WEFE), overcoming the ‘silo’ approach that usually characterizes the management of natural resources along with a rather water-centered perspective. Supporting a comprehensive understanding of the cross-sectoral interdependencies and influences among sectors is a cutting-edge research issue, specifically as far as the production of ‘actionable’ knowledge for policy makers is concerned. Despite its success, the actual implementation of the Nexus holistic approach is still hampered by several barriers. Starting from the analysis of those barriers, this work describes a methodological approach based on Qualitative System Dynamic Model (and specifically Causal Loop Diagram – CLD), capable of enabling the transition from Nexus thinking to Nexus doing. The methodological approach maps and describes the dynamic evolution of complex WEFE Nexus systems, and proposes an innovative ‘leverage analysis’ – based on graph theory measures – for identifying policy interventions capable of impacting system state and potential evolution. The proposed approach is highly participatory as stakeholders engagement is facilitated throughout the modelling process. Besides a description of the methodology, the present work provides also full details on the results of its implementation in two different case studies in Europe