Extraction of antioxidants from corn by-products for applications in bio-based polymer matrices

The objective of this dissertation is the evaluation of the exploitability of corn cobs as natural additives for bio-based polymer matrices, in order to hone their properties while keeping the fundamental quality of being fully bio-derived. The first part of the project has the purpose of finding the best solvent and conditions to extract antioxidants and anti-degrading molecules from corn cobs, exploiting room and high-temperature processes, traditional and advanced extraction methods, as well as polar and nonpolar solvents. The extracts in their entirety are then analysed to evaluate their antioxidant content, in order to select the conditions able to maximise their anti-degrading properties. The second part of the project, instead, focuses on assessing chemical and physical properties of the best-behaving extract when inserted in a polymeric matrix. To achieve this, low-density polyethylene (LDPE) and poly (butylene succinate – co – adipate) (PBSA) are employed. These samples are obtained through extrusion and are subsequently characterised exploiting the DSC equipment and a sinusoidally oscillating rheometer. In addition, extruded polymeric matrices are subjected to thermal and photo ageing, in order to identify their behaviour after different forms of degradation and to assess their performances with respect to synthetically produced anti-degrading additives.

Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.

Organic/inorganic polyoxometalate-based hybrids for oxidation catalysis and energy conversion

This thesis work has been carried out during the Erasmus exchange period at the “Université Paris 6 – Pierre et Marie Curie”, in the “Edifices PolyMétalliques – EPOM” team, leaded by Prof. Anna Proust, belonging to the “Institut Parisien de Chimie Moléculaire”, under the supervision of Dr. Guillaume Izzet and Dr. Geoffroy Guillemot. The redox properties of functionalized Keggin and Dawson POMs have been exploited in photochemical, catalytic and reactivity tests. For the photochemical purposes, the selected POMs have been functionalized with different photoactive FGs, and the resulting products have been characterized by CV analyses, luminescence tests and UV-Vis analyses. In future, these materials will be tested for hydrogen photoproduction and polymerization of photoactive films. For the catalytic purposes, POMs have been firstly functionalized with silanol moieties, to obtain original coordination sites, and then post-functionalized with TMs such as V, Ti and Zr in their highest oxidation states. In this way, the catalytic properties of TMs were coupled to the redox properties of POM frameworks. The redox behavior of some of these hybrids has been studied by spectro-electrochemical and EPR methods. Catalytic epoxidation tests have been carried out on allylic alcohols and n-olefins, employing different catalysts and variable amounts of them. The performances of POM-V hybrids have been compared to those of VO(iPrO)3. Finally, reactivity of POM-VIII hybrids has been studied, using styrene oxide and ethyl-2-diazoacetate as substrates. All the obtained products have been analyzed via NMR techniques. Cyclovoltammetric analyses have been carried out in order to determine the redox behavior of selected hybrids.

Preparazione di una resina epossidica bio-based e studio delle relative proprietà

Epoxy resins are very diffused materials due to their high added value deriving from high mechanical proprieties and thermal resistance; for this reason they are widely used both as metallic coatings in aerospace and in food packaging. However, their preparation uses dangerous reagents like bisphenol A and epichlorohydrin respectively classified as suspected of causing damage to fertility and to be carcinogen. Therefore, to satisfy the ever-growing attention to environmental problems and human safeness, we are considering alternative “green” processes through the use of reagents obtained as by-products from other processes and mild experimental conditions, and also economically sustainable and attractive for industries. Following previous results, we carried out the reaction leading to the formation of diphenolic acid (DPA), its allylation and the following epoxidation of the double bonds, all in aqueous solvent. In a second step the obtained product were cross-linked at high temperature with and without the use of hardeners. Then, on the obtained resin, some tests were performed like release in aqueous solution, scratch test and DSC analysis.

Modelling, simulation and optimization of maintenance considerations on condition based maintenance

Globalization has increased the pressure on organizations and companies to operate in the most efficient and economic way. This tendency promotes that companies concentrate more and more on their core businesses, outsource less profitable departments and services to reduce costs. By contrast to earlier times, companies are highly specialized and have a low real net output ratio. For being able to provide the consumers with the right products, those companies have to collaborate with other suppliers and form large supply chains. An effect of large supply chains is the deficiency of high stocks and stockholding costs. This fact has lead to the rapid spread of Just-in-Time logistic concepts aimed minimizing stock by simultaneous high availability of products. Those concurring goals, minimizing stock by simultaneous high product availability, claim for high availability of the production systems in the way that an incoming order can immediately processed. Besides of design aspects and the quality of the production system, maintenance has a strong impact on production system availability. In the last decades, there has been many attempts to create maintenance models for availability optimization. Most of them concentrated on the availability aspect only without incorporating further aspects as logistics and profitability of the overall system. However, production system operator’s main intention is to optimize the profitability of the production system and not the availability of the production system. Thus, classic models, limited to represent and optimize maintenance strategies under the light of availability, fail. A novel approach, incorporating all financial impacting processes of and around a production system, is needed. The proposed model is subdivided into three parts, maintenance module, production module and connection module. This subdivision provides easy maintainability and simple extendability. Within those modules, all aspect of production process are modeled. Main part of the work lies in the extended maintenance and failure module that offers a representation of different maintenance strategies but also incorporates the effect of over-maintaining and failed maintenance (maintenance induced failures). Order release and seizing of the production system are modeled in the production part. Due to computational power limitation, it was not possible to run the simulation and the optimization with the fully developed production model. Thus, the production model was reduced to a black-box without higher degree of details.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

Synthesis, characterization and catalytic performances of ruthenium-based catalysts for the acceptorless dehydrogenative coupling of butanol

A growing interest towards new sources of energy has led in recent years to the development of a new generation of catalysts for alcohol dehydrogenative coupling (ADC). This green, atom-efficient reaction is capable of turning alcohol derivatives into higher value and chemically more attractive ester molecules, and it finds interesting applications in the transformation of the large variety of products deriving from biomass. In the present work, a new series of ruthenium-PNP pincer complexes are investigated for the transformation of 1-butanol, one of the most challenging substrates for this type of reactions, into butyl butyrate, a short-chain symmetrical ester widely used in flavor industries. Since the reaction kinetics depends on hydrogen diffusion, the study aimed at identifying proper reactor type and right catalyst concentration to avoid mass transfer interferences and to get dependable data. A comparison between catalytic activities and productivities has been made to establish the role of the different ligands bonded both to the PNP binder and to the ruthenium metal center, and hence to find the best catalyst for this type of reaction.

Theoretical and experimental investigations on homologation of ethanol to butanol using a ruthenium-based catalyst

The mechanism of homologation of bioethanol to butanol and higher alcohols via the Guerbet reaction was computationally and experimentally investigated. The catalytic pathway involves a ruthenium-based complex and a base co-catalyst which work simultaneously. Due to selectivity issues, secondary products were formed and high competition between main pathway and side reactions was recorded. Herein, the overall catalytic mechanism for all the processes involved in was investigated, also considering the principal side reactions, using density functional theory (DFT) methods and experiments to confirm theoretical outcomes. Due to the complexity of the reaction network, kinetic simulations were established from DFT results, confirming experimental products distribution and giving insights into the factors governing the reaction mechanism.

Study of the activity and enantioselectivity of alginate-based catalysts in Friedel-Crafts reactions

This thesis is part of a long-term project which aims to demonstrate for the first time that alginate gel beads can be used as chiral heterogeneous catalysts for enantioselective reactions. Alginate barium beads were prepared as previously optimized and applied to the Friedel-Crafts reaction between indoles and nitroalkenes. New substrates were tested, showing that the reaction can accommodate different nitroalkenes and indoles, affording the corresponding products with moderate yields and good enantioselectivities. However, aliphatic nitroalkenes cannot be used as they degrade under the catalytic reaction conditions. Preliminary study on the recyclability of the heterogeneous catalyst indicated a moderate stability of the catalyst, which can be used for few cycles with a slight erosion of enantioinducing power. Some directions for future improvements (storage and work-up solvent, use of ultrasonic bath) have been suggested.

Formulazione e caratterizzazione di idropitture con materie prime “Bio-Based”

The market for paint products with raw materials derived from renewable sources is growing rapidly in the building industry. When high performance in wet scrub resistance is required, “washable” paints are used. However, formulating products with Bio-Based raw materials generally results in a decrease in performances compared to similar products with raw materials from fossil sources. Therefore, a new formulation approach is needed to characterize polymeric binders from renewable sources and to consider the synergistic effects given by blends of polymeric binders of different origin and chemical structure. To date, the development of new formulations that imply less environmental impact is necessary if these products have to remain competitive in the marketplace. During the trainingship in IVAS S.p.A., washable paints with different PVC (Pigment Volume Concentration) were formulated and tested, evaluating whether the performance of paints with polymeric binders obtained from renewable sources was comparable to those with polymeric binders from fossil sources. The binders were chemically characterized by DSC, FT-IR and NMR analysis. Characterization tests of paints were focused on the evaluation of degree of whiteness, hiding power, dirt setting, and wet scrub resistance. Following the results obtained from the available binder combinations, it was possible to formulate two washable paints with comparable performances to those from fossil sources: paint A with 20 % of alkydic polymer and 80 % styrene/acrylic polymer and paint B with 40 % of alkydic polymer and 60 % styrene/acrylic polymer. Finally, the formulation was completed by adding the mainly Bio-Based derived additives generally used for this category of paints.