Effect of post-emergent herbicides application on carotenoid concentration in green corn.

Considering the influence of herbicides in the metabolism of the carotenoids in corn, the objective of the present study was to evaluate the effect of herbicides and genotype on carotenoids concentration. The green corn hybrids BRS 1030 and P30F53 were subjected to a post-emergent herbicides application at 20 and 30 days after emergence. Carotenoids were extracted from corn grains and analyzed to quantify ?- and ?-carotene, lutein, zeaxanthin, ?-cryptoxanthin, total carotenoids (TC), and total of vitamin A carotenoids precursors (proVA). The application of foramsulfuron + iodosulfuron-methyl-sodium (40 + 2.6 g ha-1), nicosulfuron (20 g ha-1), mesotrione (120 g ha-1) and tembotrione (80 g ha-1 and 100 g ha-1) promoted higher concentration of carotenoids in fresh green corn. Lutein, zeaxanthin, ?-cryptoxanthin, ?-carotene, ?-carotene, proVA carotenoids, and TC concentration increased after foramsulfuron + iodosulfuron-methyl-sodium in late application (V5 to V6), nicosulfuron in both applications, mesotrione applied post-initial (V3 to V4), tembotrione (100 g ha-1) in both applications and tembotrione (80 g ha-1) in late post-application, at least for one hybrid. The content of carotenoids in the green corn kernels differed between ?BRS 1030? and ?P30F53?. Our results suggest a possibility of significant increase of carotenoids in green corn kernels through the handling of corn production with post-emergent herbicides.

Modelling and Optimization of Energy Management Strategies for Hybrid Vehicles

In the last decades the automotive sector has seen a technological revolution, due mainly to the more restrictive regulation, the newly introduced technologies and, as last, to the poor resources of fossil fuels remaining on Earth. Promising solution in vehicles’ propulsion are represented by alternative architectures and energy sources, for example fuel-cells and pure electric vehicles. The automotive transition to new and green vehicles is passing through the development of hybrid vehicles, that usually combine positive aspects of each technology. To fully exploit the powerful of hybrid vehicles, however, it is important to manage the powertrain’s degrees of freedom in the smartest way possible, otherwise hybridization would be worthless. To this aim, this dissertation is focused on the development of energy management strategies and predictive control functions. Such algorithms have the goal of increasing the powertrain overall efficiency and contextually increasing the driver safety. Such control algorithms have been applied to an axle-split Plug-in Hybrid Electric Vehicle with a complex architecture that allows more than one driving modes, including the pure electric one. The different energy management strategies investigated are mainly three: the vehicle baseline heuristic controller, in the following mentioned as rule-based controller, a sub-optimal controller that can include also predictive functionalities, referred to as Equivalent Consumption Minimization Strategy, and a vehicle global optimum control technique, called Dynamic Programming, also including the high-voltage battery thermal management. During this project, different modelling approaches have been applied to the powertrain, including Hardware-in-the-loop, and diverse powertrain high-level controllers have been developed and implemented, increasing at each step their complexity. It has been proven the potential of using sophisticated powertrain control techniques, and that the gainable benefits in terms of fuel economy are largely influenced by the chose energy management strategy, even considering the powerful vehicle investigated.

Newly developed electrosynthesis of Layered Double Hydroxides: application to sensing, energy storage and conversion

Layered Double hydroxides (LDHs) have been widely studied for their plethora of fascinating features and applications. The potentiostatic electrodeposition of LDHs has been extensively applied in the literature as a fast and direct method to substitute classical chemical routes. However, it does not usually allow for a fine control of the M(II)/M(III) ratio in the synthesized material and it is not suitable for large anions intercalation. Therefore, in this work a novel protocol has been proposed with the aim to overcome all these constraints using a method based on potentiodynamic synthesis. LDHs of controlled composition were prepared using different molar ratios of the trivalent to bivalent cations in the electrolytic solution ranging from 1:1 to 1:4. Moreover, we were able to produce electrochemically LDHs intercalated with carbon nanomaterials for the first time. A one-step procedure which contemporaneously allows for the Ni/Al-LDH synthesis, the reduction of graphene oxide (GO) and its intercalation inside the structure has been developed. The synthesised materials have been applied in several fields of interest. First of all, LDHs with a ratio 3:1 were exploited, and displayed good performances as catalysts for 5-(hydroxymethyl)furfural electro-oxidation, thus suggesting to carry out further investigation for applications in the field of industrial catalysis. The same materials, but with different metals ratios, were tested as catalysts for Oxygen Evolution Reaction, obtaining results comparable to LDHs synthesised by the classical co-precipitation method and also a better activity with respect to LDHs obtained by the potentiostatic approach. The composite material based on LDH and reduced graphene oxide was employed to fabricate a cathode of a hybrid supercapacitor coupled with an activated carbon anode. We can thus conclude that, to date, the potentiodynamic method has the greatest potential for the rapid synthesis of reproducible films of Co and Ni-based LDHs with controlled composition.

Novel membranes for hemodialysis application

The present work is focused on the synthesis and characterization of novel materials for hemodialysis applications. Cellulose acetate was chosen as base polymer for the preparation of porous Mixed Matrix Membrane adsorbers (MMMAs) and for the synthesis of hybrid ultrafiltration membranes. Hemodialysis is a renal replacement therapy used to eliminate,the waste products and excess fluids accumulating in the blood of people affected by an end stage renal disease. The main environmental drawback associated to it is the large water consumption. The MMMAs were prepared with the porpoise of eliminating waste metabolites (uremic toxins) from the spent dialysate solution, with the prospective limiting the consumption of water related to the process. Batch tests of MMMAs showed that the removal of uric acid is almost complete while the one of urea and creatinine is limited to a 20/30 %. The thinking behind the concept of MMMAs was aimed to develop a small a lab scale chromatographic cartridge to continuously remove uremic toxins from an aqueous feed solution. The cartridge was packed with MMMAs and tested with a mixture of toxins. Experiments results shown a promising removal capability of the system even if the necessity of a higher surface area to achieve better efficiency is denoted. The other important issue related to hemodialysis is the assessment of an overall mass transfer rates in hemodialyzers. The mass transfer correlations proposed in literature do not take into account the effect of permeation and are developed for turbulent flow regime. Therefore, hybrid cellulose acetate/Silica ultrafiltration membranes were prepared to characterize a surrogate system of an artificial kidney (AK) in terms of fluid mechanics and mass transfer. The effect of surface roughness and suction on the velocity profiles was determined and a new dimensionless mass transfer correlation accounting for permeation was developed.

Advanced Functional Organic-Inorganic Hybrid (Nano)Materials: from Theranostics to Organic Electronics and Additive Manufacturing

This work is going to show the activities performed in the frame of my PhD studies at the University of Bologna, under the supervision of Prof. Mauro Comes Franchini, at the Department of Industrial Chemistry “Toso Montanari”. The main topic of this dissertation will be the study of organic-inorganic hybrid nanostructures and materials for advanced applications in different fields of materials technology and development such as theranostics, organic electronics and additive manufacturing, also known as 3D printing. This work is therefore divided into three chapters, that recall the fundamentals of each subject and to recap the state-of-the-art of scientific research around each topic. In each chapter, the published works and preliminary results obtained during my PhD career will be discussed in detail.

Modeling of Electric Hybrid Vehicles based on Innovative Energy Storage Technologies for Super Sports Cars Applications

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment has given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid vehicles can differ in the powertrain configuration and the choice of the energy storage system. Lamborghini has recently invested in the development of hybrid super sport cars, due to performance and comfort reasons, with the possibility to reduce fuel consumption. This research activity has been conducted as a joint collaboration between the University of Bologna and the sportscar manufacturer, to analyze the impact of innovative energy storage solutions on the hybrid vehicle performance. Capacitors have been studied and modeled to analyze the pros and cons of such solution with respect to batteries. To this aim, a full simulation environment has been developed and validated to provide a concept design tool capable of precise results and able to foresee the longitudinal performance on regulated emission cycles and real driving conditions, with a focus on fuel consumption. In addition, the target of the research activity is to deepen the study of hybrid electric super sports cars in the concept development phase, focusing on defining the control strategies and the energy storage system’s technology that best suits the needs of the vehicles. This dissertation covers the key steps that have been carried out in the research project.

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

Sintesi di oligotiofeni “push-pull” per applicazioni in celle solari di tipo bulkheterojunction (BHJ)

Sintesi e caratterizzazione di cinque eptameri "push-pull" a base tiofenica con sequenza D-A-D-A-D-A-D e D-D-D-A-D-D-D per applicazioni in celle solari di tipo bulkheterojunction (BHJ).

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Hybrid Artificial Intelligence to Extract Patterns and Rules from Argumentative and Legal Texts

This Thesis is composed of a collection of works written in the period 2019-2022, whose aim is to find methodologies of Artificial Intelligence (AI) and Machine Learning to detect and classify patterns and rules in argumentative and legal texts. We define our approach “hybrid”, since we aimed at designing hybrid combinations of symbolic and sub-symbolic AI, involving both “top-down” structured knowledge and “bottom-up” data-driven knowledge. A first group of works is dedicated to the classification of argumentative patterns. Following the Waltonian model of argument and the related theory of Argumentation Schemes, these works focused on the detection of argumentative support and opposition, showing that argumentative evidences can be classified at fine-grained levels without resorting to highly engineered features. To show this, our methods involved not only traditional approaches such as TFIDF, but also some novel methods based on Tree Kernel algorithms. After the encouraging results of this first phase, we explored the use of a some emerging methodologies promoted by actors like Google, which have deeply changed NLP since 2018-19 — i.e., Transfer Learning and language models. These new methodologies markedly improved our previous results, providing us with best-performing NLP tools. Using Transfer Learning, we also performed a Sequence Labelling task to recognize the exact span of argumentative components (i.e., claims and premises), thus connecting portions of natural language to portions of arguments (i.e., to the logical-inferential dimension). The last part of our work was finally dedicated to the employment of Transfer Learning methods for the detection of rules and deontic modalities. In this case, we explored a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures.

Theoretical and experimental study of novel hybrid composite and metal-composites interfaces

The growing demand for lightweight solutions in every field of engineering is driving the industry to seek new technological solutions to exploit the full potential of different materials. The combination of dissimilar materials with distinct property ranges embodies a transparent allocation of component functions while allowing an optimal mix of their characteristics. From both technological and design perspectives, the interaction between dissimilar materials can lead to severe defects that compromise a multi-material hybrid component's performance and its structural integrity. This thesis aims to develop methodologies for designing, manufacturing, and monitoring of hybrid metal-composite joints and hybrid composite components. In Chapter 1, a methodology for designing and manufacturing hybrid aluminum/composite co-cured tubes is assessed. In Chapter 2, a full-field methodology for fiber misalignment detection and stiffness prediction for hybrid, long fiber reinforced composite systems is shown and demonstrated. Chapter 3 reports the development of a novel technology for joining short fiber systems and metals in a one-step co-curing process using lattice structures. Chapter 4 is dedicated to a novel analytical framework for the design optimization of two lattice architectures.

A novel hybrid thermochemical-biological refinery integrated with power-to-X approach for obtaining biopolymers

In this study, a novel hybrid thermochemical-biological refinery integrated with power-to-x approach was developed for obtaining biopolymers (namely polyhydroxyalkanoates, PHA). Within this concept, a trilogy process schema comprising of, (i) thermochemical conversion via integrated pyrolysis-gasification technologies, (ii) anaerobic fermentation of the bioavailable products obtained through either thermochemistry or water-electrolysis for volatile fatty acids (VFA) production, (iii) and VFA-to-PHA bioconversion via an original microaerophilic-aerobic process was developed. During the first stage of proposed biorefinery where lignocellulosic (wooden) biomass was converted into, theoretically fermentable products (i.e. bioavailables) which were defined as syngas and water-soluble fraction of pyrolytic liquid (WS); biochar as a biocatalyst material; and a dense-oil as a liquid fuel. Within integrated pyrolysis - gasification process, biomass was efficiently converted into fermentable intermediates representing up to 66% of biomass chemical energy content in chemical oxygen demand (COD) basis. In the secondary stage, namely anaerobic fermentation for obtaining VFA rich streams, three different downstream process were investigated. First fermentation test was acidogenic bioconversion of WS materials obtained through pyrolysis of biomass within an original biochar-packed bioreactor, it was sustained up to 0.6 gCOD/L-day volumetric productivity (VP). Second, C1 rich syngas materials as the gaseous fraction of pyrolysis-gasification stage, was fermented within a novel char-based biofilm sparger reactor (CBSR), where up to 9.8 gCOD/L-day VP was detected. Third was homoacetogenic bioconversion within the innovative power-to-x pathway for obtaining commodities via renewable energy sources. More specifically, water-electrolysis derived H2 and CO2 as a primary greenhouse gas was successfully bio-utilized by anaerobic mixed cultures into VFA within CBSR system (VP: 18.2 gCOD/L-day). In the last stage of the developed biorefinery schema, VFA is converted into biopolymers within a new continuous microaerophilic-aerobic microplant, where up to 60% of PHA containing sludges was obtained.

High performance battery chargers for full electric and hybrid cars

This Doctoral Thesis aims to study and develop advanced and high-efficient battery chargers for full electric and plug-in electric cars. The document is strictly industry-oriented and relies on automotive standards and regulations. In the first part a general overview about wireless power transfer battery chargers (WPTBCs) and a deep investigation about international standards are carried out. Then, due to the highly increasing attention given to WPTBCs by the automotive industry and considering the need of minimizing weight, size and number of components this work focuses on those architectures that realize a single stage for on-board power conversion avoiding the implementation of the DC/DC converter upstream the battery. Based on the results of the state-of-the-art, the following sections focus on two stages of the architecture: the resonant tank and the primary DC/AC inverter. To reach the maximum transfer efficiency while minimizing weight and size of the vehicle assembly a coordinated system level design procedure for resonant tank along with an innovative control algorithm for the DC/AC primary inverter is proposed. The presented solutions are generalized and adapted for the best trade-off topologies of compensation networks: Series-Series and Series-Parallel. To assess the effectiveness of the above-mentioned objectives, validation and testing are performed through a simulation environment, while experimental test benches are carried out by the collaboration of Delft University of Technology (TU Delft).

Hybrid lead-halide perovskites as novel materials for thin film direct and flexible ionizing radiation detectors

Ionizing radiations are important tools employed every day in the modern society. For example, in medicine they are routinely used for diagnostic and therapy. The large variety of applications leads to the need of novel, more efficient, low-cost ionizing radiation detectors with new functionalities. Personal dosimetry would benefit from wearable detectors able to conform to the body surfaces. Traditional semiconductors used for ionizing radiation direct detectors offer high performance but they are intrinsically stiff, brittle and require high voltages to operate. Hybrid lead-halide perovskites emerged recently as a novel class of materials for ionizing radiation detection. They combine high absorption coefficient, solution processability and high charge transport capability, enabling efficient and low-cost detection. The deposition from solution allows the fabrication of thin-film flexible devices. In this thesis, I studied the detection properties of different types of hybrid perovskites, deposited from solution in thin-film form, and tested under X-rays, gamma-rays and protons beams. I developed the first ultraflexible X-ray detector with exceptional conformability. The effect of coupling organic layers with perovskites was studied at the nanoscale giving a direct demonstration of trap passivation effect at the grain boundaries. Different perovskite formulations were deposited and tested to improve the film stability. I report about the longest aging studies on perovskite X-ray detectors showing that the addition of starch in the precursors’ solution can improve the stability in time with only a 7% decrease in sensitivity after 630 days of storage in ambient conditions. 2D perovskites were also explored as direct detector for X-rays and gamma-rays. Detection of 511 keV photons by a thin-film device is here demonstrated and was validated for monitoring a radiotracer injection. At last, a new approach has been used: a 2D/3Dmixed perovskite thin-film demonstrated to reliably detect 5 MeV protons, envisioning wearable dose monitoring during proton/hadron therapy treatments.

Performance assessment of vehicular connectivity in hybrid electric vehicles for fuel and emissions reduction

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.