Study of new physiological characteristics as protein receptors (RP55) of light, color, heat, motion, smell, electric, vibration and position located in head, body and abdominal, found in mosquito larvae.

The main objective of the research is to demonstrate new physiological characteristics receptors in the presence of mosquito larvae. 100 larvae of different species were collected and studied for a week in periods of 8-24 hrs. Larvae stages I, II, III and IV have photo-thermo receptors of light and heat housed in the body is divided into head, thorax and abdomen, perceive hot or cold environments, and have fibers in chest or hairs lining your body or abdomen, and a pair of antennae on the head. Stages II and III are more developed than the initial stages. They are attracted by the dark green at the bottom, a pair of eyes that perceive light and color. Have receptors proteins (RP55) that capture motion at a speed the slightest movement of waves in the water. Its nose is not well developed but have chemoreceptors. They adapt to changes in pH in alkaline media, are sensitive to chemical, thermal and mechanical changes nociceptors have electroreceptors or galvanoreceptores sensitive to electrical stimuli, have mechanoreceptors that are sensitive to touch, pain, pressure, gravity, sound. They have a GPS position that seems the guides. It is precisely in the fibers, mushrooms or bristles are recipients along with the micro villi in head, thorax and abdomen.
RESUMEN El objetivo principal de la investigación es demostrar nuevas características fisiológicas como la presencia de receptores en las larvas de mosquitos. Se recolectaron 100 larvas de diferentes especies y se estudiaron por una semana en periodos de 8 a 24 hrs. Las larvas de los estadios I,II,III y IV tienen foto-termo receptores de luz y calor alojados en el cuerpo que se divide en cabeza, tórax y abdomen, perciben ambientes fríos o calientes, así como tienen fibras en tórax o pelos que recubren su cuerpo, y un par de antenas en la cabeza. Los estadios II y III son más desarrollados que las etapas iniciales. Tienen receptores proteicos RP55. Les atrae el color verde oscuro en el fondo, un par de ojos que perciben la luz y color con fotoreceptores. Tienen receptores RP55 de movimiento que captan a una velocidad el más mínimo movimiento de ondas en el agua. Su olfato no está muy desarrollado pero tienen quimioreceptores. Se adaptan a cambios de pH en medios alcalinos, tienen nociceptores sensibles a cambios químicos, térmicos y mecánicos, tienen galvanoreceptores o electroreceptores sensibles a estímulos eléctricos, tienen mecanoreceptores que son sensibles al tacto, dolor, presión gravedad, sonido. Tienen un GPS de posición que pareciera las orienta.

Ultrafast transient absorption spectroscopy: principles and applications

The study of photophysical and photochemical processes crosses the interest of many fields of research in physics, chemistry and biology. In particular, the photophysical and photochemical reactions, after light absorption by a photosynthetic pigment-protein complex, are among the fastest events in biology, taking place on timescales ranging from tens of femtoseconds to a few nanoseconds. Among the experimental approaches developed for this purpose, the advent of ultrafast transient absorption spectroscopy has become a powerful and widely used technique.[1,2] Focusing on the process of photosynthesis, it relies upon the efficient absorption and conversion of the radiant energy from the Sun. Chlorophylls and carotenoids are the main players in the process. Photosynthetic pigments are typically arranged in a highly organized fashion to constitute antennas and reaction centers, supramolecular devices where light harvesting and charge separation take place. The very early steps in the photosynthetic process take place after the absorption of a photon by an antenna system, which harvests light and eventually delivers it to the reaction center. In order to compete with internal conversion, intersystem crossing, and fluorescence, which inevitably lead to energy loss, the energy and electron transfer processes that fix the excited-state energy in photosynthesis must be extremely fast. In order to investigate these events, ultrafast techniques down to a sub-100 fs resolution must be used. In this way, energy migration within the system as well as the formation of new chemical species such as charge-separated states can be tracked in real time. This can be achieved by making use of ultrafast transient absorption spectroscopy. The basic principles of this notable technique, instrumentation, and some recent applications to photosynthetic systems[3] will be described. Acknowledgements M. Moreno Oliva thanks the MINECO for a “Juan de la Cierva-Incorporación” research contract. References [1] U. Megerle, I. Pugliesi, C. Schriever, C.F. Sailer and E. Riedle, Appl. Phys. B, 96, 215 – 231 (2009). [2] R. Berera, R. van Grondelle and J.T.M. Kennis, Photosynth. Res., 101, 105 – 118 (2009). [3] T. Nikkonen, M. Moreno Oliva, A. Kahnt, M. Muuronen, J. Helaja and D.M. Guldi, Chem. Eur. J., 21, 590 – 600 (2015).

Conversão térmica e termocatalítica à baixa temperatura do óleo de girassol para obtenção de bio-óleo

The use of biofuels remotes to the eighteenth century, when Rudolf Diesel made the first trials using peanut oil as fuel in a compression ignition engine. Based on these trials, there was the need for some chemical change to vegetable oil. Among these chemical transformations, we can mention the cracking and transesterification. This work aims at conducting a study using the thermocatalytic and thermal cracking of sunflower oil, using the Al-MCM-41 catalyst. The material type mesoporous Al-MCM-41 was synthesized and characterized by Hydrothermical methods of X-ray diffraction, scanning electron microscopy, nitrogen adsorption, absorption spectroscopy in the infrared and thermal gravimetric analysis (TG / DTG).The study was conducted on the thermogravimetric behavior of sunflower oil on the mesoporous catalyst cited. Activation energy, conversion, and oil degradation as a function of temperature were estimated based on the integral curves of thermogravimetric analysis and the kinetic method of Vyazovkin. The mesoporous material Al-MCM-41 showed one-dimensional hexagonal formation. The study of the kinetic behavior of sunflower oil with the catalyst showed a lower activation energy against the activation energy of pure sunflower oil. Two liquid fractions of sunflower oil were obtained, both in thermal and thermocatalytic pyrolisis. The first fraction obtained was called bio-oil and the second fraction obtained was called acid fraction. The acid fraction collected, in thermal and thermocatalytic pyrolisis, showed very high level of acidity, which is why it was called acid fraction. The first fraction was collected bio-called because it presented results in the range similar to petroleum diesel

Design Optimization of Modern Machine-drive Systems for Maximum Fault Tolerant and Optimal Operation

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Coordinated Scheduling of Wind-Thermal Gencos in Day-Ahead Markets

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modeled by variable costs, start-up costs and technical operating constraints, such as: ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, aiming to maximize the expected profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

Bidding strategy of wind-thermal energy producers

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

Real-Time Engine Diagnostic and Control Based on Acoustic Emissions Analysis

Engine developers are putting more and more emphasis on the research of maximum thermal and mechanical efficiency in the recent years. Research advances have proven the effectiveness of downsized, turbocharged and direct injection concepts, applied to gasoline combustion systems, to reduce the overall fuel consumption while respecting exhaust emissions limits. These new technologies require more complex engine control units. The sound emitted from a mechanical system encloses many information related to its operating condition and it can be used for control and diagnostic purposes. The thesis shows how the functions carried out from different and specific sensors usually present on-board, can be executed, at the same time, using only one multifunction sensor based on low-cost microphone technology. A theoretical background about sound and signal processing is provided in chapter 1. In modern turbocharged downsized GDI engines, the achievement of maximum thermal efficiency is precluded by the occurrence of knock. Knock emits an unmistakable sound perceived by the human ear like a clink. In chapter 2, the possibility of using this characteristic sound for knock control propose, starting from first experimental assessment tests, to the implementation in a real, production-type engine control unit will be shown. Chapter 3 focus is on misfire detection. Putting emphasis on the low frequency domain of the engine sound spectrum, features related to each combustion cycle of each cylinder can be identified and isolated. An innovative approach to misfire detection, which presents the advantage of not being affected by the road and driveline conditions is introduced. A preliminary study of air path leak detection techniques based on acoustic emissions analysis has been developed, and the first experimental results are shown in chapter 4. Finally, in chapter 5, an innovative detection methodology, based on engine vibration analysis, that can provide useful information about combustion phase is reported.

Synthesis, Characterization, and Applications of a Novel Cu(II)-MOF Based on a Propargylcarbamate-Functionalized Isophthalate Ligand

This work describes the synthesis of a propargylcarbamate-functionalized isophthalate ligand and its use in the solvothermal preparation of a new copper(II)-based metal organic framework named [Cu(1,3-YBDC)]ˑxH2O (also abbreviated as Cu-MOF. The characterization of this compound was performed using several complementary techniques such as infrared (ATR-FTIR) and Raman spectroscopy, X-ray powder diffraction spectroscopy (PXRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), atomic absorption spectroscopy (AAS) as well as thermal and surface area measurements. Synchrotron X-ray diffraction analysis revealed that this MOF contains a complex network of 5-substituted isophthalate anions bound to Cu(II) centers, arranged in pairs within paddlewheel (or “Chinese lantern”) structure with a short Cu…Cu distance of 2.633 Å. Quite unexpectedly, the apical atom in the paddlewheel structure belongs to the carbamate carbonyl oxygen atom. Such extra coordination by the propargylcarbamate groups drastically reduces the MOF porosity, a feature that was also confirmed by BET measurements. Indeed, its surface area was determined to be low (14.5 ± 0.8 m2/g) as its total pore volume (46 mm3/g). Successively the Cu-MOF was treated with HAuCl4 with the aim of studying the ability of the propargylcarbamate functionality to capture the Au(III) ion and reduce it to Au(0) to give gold nanoparticles (AuNPs). The overall amount of gold retained by the Cu-MOF/Au was determined by AAS while the amount of gold and its oxidation state on the surface of the MOF was studied by XPS. A glassy carbon (GC) electrode was drop-casted with a Cu-MOF suspension to electrochemically characterize the material through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The performance of the modified electrodes towards nitrite oxidation was tested by CV and chronoamperometry.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Development and characterization of innovative graphene-based composites

Over the last decade, graphene and related materials (GRM) have drawn significant interest and resources for their development into the next generation of composite materials. This is because these nanoparticles have the ability to operate as reinforcing additives capable of imparting considerable mechanical property increases while also embedding multi-functional advantages on the host matrix. Because graphene and 2D materials are still in their early stages, the relative maturity of different types of composite systems varies. As a result, certain nanocomposite systems are currently commercially accessible, while others are not yet sufficiently developed to enter the market. A substantial emphasis has been placed on developing thermoplastic and thermosetting materials that combine a variety of mechanical and functional qualities. These include higher strength and stiffness, increased thermal and electrical conductivity, improved barrier properties, fire retardancy, and others, with the ultimate goal of providing multifunctionality to already employed composites. The work presented in this thesis investigates the use and benefits that GRM could bring to composites for a variety of applications, with the goal of realizing multifunctional components with improved properties that leads to lightweight and, as a result, energy and cost savings and pollution reduction in the environment. In particular, we worked on the following topics: • Benchmarking of commercial GRM-based master batches; • GRM-coatings for water uptake reduction; • GRM as thermo-electrical anti-icing /de-icing system; • GRM for Out of Oven curing of composites.

Synthesis, characterization and potential applications of novel metal-organic frameworks

​The research work described in this thesis concerns the synthesis, characterization, and applications of two kinds of metal-organic frameworks (MOFs), Copper based MOF (Cu-MOF) and zirconium based MOF (Zr-MOF) functionalized with new linkers. ​The common thread of this research project can be summarized in three work phases: ​first, the synthesis and characterization of new organic linkers is described, followed by the presentation of the different optimization conditions for the MOFs synthesis. ​Second, the new materials were fully characterized using several complementary techniques, such as infrared (ATR-FTIR) and Raman spectroscopy, X-ray powder diffraction spectroscopy (PXRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), atomic absorption spectroscopy (AAS) as well as thermal and surface area measurements. ​Final, to obtain a complete work the possible environmental applications of the new materials were explored.

Diazene photoswitches in artificial molecular machines and light-effected materials

The research developed in this thesis focused on the spectroscopic and photochemical characterization of molecular diazene photoswitches, both as individual species and as functional components of mechanically interlocked molecules, molecular-based materials and artificial molecular machines and motors. Among the plethora of photochromes reported so far, azobenzene is the most versatile photoswitch due to its reproducible and well-established photochemical properties. Part I of this thesis work focuses on the characterization of light-responsive supramolecular systems based on azobenzene: a photochemically-driven rotary motor, a light-responsive supramolecular polymeric material and a supramolecular system capable of photoinduced entantiodiscrimination. Despite the wide success of azobenzene photoswitches, the tunability of their photochemical properties as a function of the diversified substitution pattern on its aryl ring presents intrinsic limitations. To overcome this issue, in the last decade heteroaryl azoswitches (i.e., azobenzene having heterocyclic rings in place of one or both phenyl groups) have attracted a great deal of attention. Hence, Part II of this thesis work treats the photochemical characterization of two different families of azoheteroarenes embedding imidazolium and thienyl functionalities in their structures. Their potential implementation in water-soluble artificial molecular machines and light-effected semiconductor materials is also assessed.

Design and implementation of a synchronous interleaved boost converter for fuel cell-powered catamaran

The present work describes the different stages of design, implementation, and validation procedures for an interleaved DC-DC boost converter intended for the 2022 Futura, a fuel cell-powered racing catamaran developed by the UniBoAT team. The main goal of the entire design has been the significant reduction of the weight of the converter by removing heat sinks and reducing component size while increasing its efficiency by adopting high-end power switches and the interleaved architecture operated with a synchronous control strategy. The obtained converter has been integrated into the structure containing the fuel cell stack obtaining a fully integrated system. The realized device has been based on an interleaved architecture with six phases controlled digitally through the average current mode control. The design has been validated through simulations carried out using the software LT-Spice, whereas experimental validations have been performed by means of laboratory bench tests and on-field tests. Detailed thermal and efficiency analyses are provided with the bench tests under the two synchronous and non-synchronous operating modes and with the adoption of the phase shedding technique. The prototype implementation and its performance in real operating conditions are also discussed. Eventually, it is underlined as the designed converter can be used in other applications requiring a voltage-controlled boost converter.