11 resultados para AMPEROMETRIC SENSOR

em AMS Tesi di Laurea - Alm@DL - Università di Bologna


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Owing to their capability of merging the properties of metals and conventional polymers, Conducting Polymers (CPs) are a unique class of carbon-based materials capable of conducting electrical current. A conjugated backbone is the hallmark of CPs, which can readily undergo reversible doping to different extents, thus achieving a wide range of electrical conductivities, while maintaining mechanical flexibility, transparency and high thermal stability. Thanks to these inherent versatility and attracting properties, from their discovery CPs have experienced incessant widespread in a great plethora of research fields, ranging from energy storage to healthcare, also encouraging the spring and growth of new scientific areas with highly innovative content. Nowadays, Bioelectronics stands out as one of the most promising research fields, dealing with the mutual interplay between biology and electronics. Among CPs, the polyelectrolyte complex poly (3,4-ethylenedioxythiophene): poly (styrenesulfonate) (PEDOT:PSS), especially in the form of thin films, has been emphasized as ideal platform for bioelectronic applications. Indeed, in the last two decades PEDOT:PSS has played a key role in the sensing of bioanalytes and living cells interfacing and monitoring. In the present work, development and characterization of two kinds of PEDOT:PSS-based devices for applications in Bioelectronics are discussed in detail. In particular, a low-cost amperometric sensor for the selective detection of Dopamine in a ternary mixture was optimized, taking advantage of the electrocatalytic and antifouling properties that render PEDOT:PSS thin films appealing tools for electrochemical sensing of bioanalytes. Moreover, the potentialities of this material to interact with live cells were explored through the fabrication of a microfluidic trapping device for electrical monitoring of 3D spheroids using an impedance-based approach.

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Conductive polymers (CPS) are a class of carbon-based materials, capable of conducting electric current, characterized by metallic properties in combination with the intrinsic properties of conventional polymers. The structural model of the CP consists of a system of double π-conjugated on the backbone (polyene structure) which can easily undergo reversible doping reaching a wide range of conductivity. Thanks to their versatility and peculiar properties (mechanical flexibility, biocompatibility, transparency, ease of chemical functionalization, high thermal stability), CPS have revolutionized the science of materials giving rise to Organic Bioelectronics, the discipline resulting from the convergence between biology and electronics. The Poly (3,4-ethylenedioxythiophene) : poly (styrenesulfonate) (PEDOT: PSS), complex polyelectrolyte, in the form of a thin film, currently represents the reference standard in applications concerning Bioelectronics. In this project, two types of electrochemical sensors ink-jet printed on a flexible polymeric substrate, the polyethylene terephthalate, have been developed and characterized. The Drop on Demand (DOD) inkjet technology has allowed to control the positioning of fluid volumes of the order of picoliters with an accuracy of ± 25μm. This resulted in the creation of amperometric sensors and organic electrochemical transistors (OECT) all-PEDOT: PSS with high reproducibility. The sensors have been used for the determination of Ascorbic Acid (AA) which is currently considered an important benchmark in the field of sensors. In Cyclic Voltammetry, the amperometric sensor has detected AA at potentials less than 0.2 V vs. SCE thanks to the electrocatalytic properties of the PEDOT: PSS. On the other hand, the OECT detected AA concentrations equal to 10 nanomolar in Chronoamperometry. Furthermore, a promising new generation of all-printed OECTS, consisting of silver metal contacts, has been created. Preliminary results are presented.

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Dopamine is a neurotransmitter which has a role in several psychiatric and neurological disorders. In-vivo detection of its concentration at the microscopic scale would benefit the study of these conditions and help in the development of therapies. The ideal sensor would be biocompatible, able to probe concentrations in microscopic volumes and sensitive to the small physiological concentrations of this molecule (10 nM - 1 μM). The ease of oxidation of dopamine makes it possible to detect it by electrochemical methods. An additional requirement in this kind of experiments when run in water, though, is to have a large potential window inside which no redox reactions with water take place. A promising class of materials which are being explored is the one of pyrolyzed photoresists. Photoresists can be lithographically patterned with micrometric resolution and after pyrolysis leave a glassy carbon material which is conductive, biocompatible and has a large electrochemical water window. In this work I developed a fabrication procedure for microelectrode arrays with three dimensional electrodes, making the whole device using just a negative photoresist called SU8. Making 3D electrodes could be a way to enhance the sensitivity of the electrodes without occupying a bigger footprint on the device. I characterized the electrical, morphological, and electrochemical properties of these electrodes, in particular their sensitivity to dopamine. I also fabricated and tested a two dimensional device for comparison. The three dimensional devices fabricated showed inferior properties to their two dimensional counter parts. I found a possible explanation and suggested some ways in which the fabrication could be improved.

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This thesis regards the Wireless Sensor Network (WSN), as one of the most important technologies for the twenty-first century and the implementation of different packet correcting erasure codes to cope with the ”bursty” nature of the transmission channel and the possibility of packet losses during the transmission. The limited battery capacity of each sensor node makes the minimization of the power consumption one of the primary concerns in WSN. Considering also the fact that in each sensor node the communication is considerably more expensive than computation, this motivates the core idea to invest computation within the network whenever possible to safe on communication costs. The goal of the research was to evaluate a parameter, for example the Packet Erasure Ratio (PER), that permit to verify the functionality and the behavior of the created network, validate the theoretical expectations and evaluate the convenience of introducing the recovery packet techniques using different types of packet erasure codes in different types of networks. Thus, considering all the constrains of energy consumption in WSN, the topic of this thesis is to try to minimize it by introducing encoding/decoding algorithms in the transmission chain in order to prevent the retransmission of the erased packets through the Packet Erasure Channel and save the energy used for each retransmitted packet. In this way it is possible extend the lifetime of entire network.

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Wireless sensor networks can transform our buildings in smart environments, improving comfort, energy efficiency and safety. Today however, wireless sensor networks are not considered reliable enough for being deployed on large scale. In this thesis, we study the main failure causes for wireless sensor networks, the existing solutions to improve reliability and investigate the possibility to implement self-diagnosis through power consumption measurements on the sensor nodes. Especially, we focus our interest on faults that generate in-range errors: those are wrong readings but belong to the range of the sensor and can therefore be missed by external observers. Using a wireless sensor network deployed in the R\&D building of NXP at the High Tech Campus of Eindhoven, we performed a power consumption characterization of the Wireless Autonomous Sensor (WAS), and studied through some experiments the effect that faults have in the power consumption of the sensor.

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Un trattato che illustra tutto quello che riguarda il Sensor Data Fusion sin dagli strumenti basilari fino alle applicazioni di specializzate

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Lo scopo del presente lavoro di tesi riguarda la caratterizzazione di un sensore ottico per la lettura di ematocrito e lo sviluppo dell’algoritmo di calibrazione del dispositivo. In altre parole, utilizzando dati ottenuti da una sessione di calibrazione opportunamente pianificata, l’algoritmo sviluppato ha lo scopo di restituire la curva di interpolazione dei dati che caratterizza il trasduttore. I passi principali del lavoro di tesi svolto sono sintetizzati nei punti seguenti: 1) Pianificazione della sessione di calibrazione necessaria per la raccolta dati e conseguente costruzione di un modello black box.  Output: dato proveniente dal sensore ottico (lettura espressa in mV)  Input: valore di ematocrito espresso in punti percentuali ( questa grandezza rappresenta il valore vero di volume ematico ed è stata ottenuta con un dispositivo di centrifugazione sanguigna) 2) Sviluppo dell’algoritmo L’algoritmo sviluppato e utilizzato offline ha lo scopo di restituire la curva di regressione dei dati. Macroscopicamente, il codice possiamo distinguerlo in due parti principali: 1- Acquisizione dei dati provenienti da sensore e stato di funzionamento della pompa bifasica 2- Normalizzazione dei dati ottenuti rispetto al valore di riferimento del sensore e implementazione dell’algoritmo di regressione. Lo step di normalizzazione dei dati è uno strumento statistico fondamentale per poter mettere a confronto grandezze non uniformi tra loro. Studi presenti, dimostrano inoltre un mutazione morfologica del globulo rosso in risposta a sollecitazioni meccaniche. Un ulteriore aspetto trattato nel presente lavoro, riguarda la velocità del flusso sanguigno determinato dalla pompa e come tale grandezza sia in grado di influenzare la lettura di ematocrito.

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Wireless sensor networks (WSNs) consist of a large number of sensor nodes, characterized by low power constraint, limited transmission range and limited computational capabilities [1][2].The cost of these devices is constantly decreasing, making it possible to use a large number of sensor devices in a wide array of commercial, environmental, military, and healthcare fields. Some of these applications involve placing the sensors evenly spaced on a straight line for example in roads, bridges, tunnels, water catchments and water pipelines, city drainages, oil and gas pipelines etc., making a special class of these networks which we define as a Linear Wireless Network (LWN). In LWNs, data transmission happens hop by hop from the source to the destination, through a route composed of multiple relays. The peculiarity of the topology of LWNs, motivates the design of specialized protocols, taking advantage of the linearity of such networks, in order to increase reliability, communication efficiency, energy savings, network lifetime and to minimize the end-to-end delay [3]. In this thesis a novel contention based Medium Access Control (MAC) protocol called L-CSMA, specifically devised for LWNs is presented. The basic idea of L-CSMA is to assign different priorities to nodes based on their position along the line. The priority is assigned in terms of sensing duration, whereby nodes closer to the destination are assigned shorter sensing time compared to the rest of the nodes and hence higher priority. This mechanism speeds up the transmission of packets which are already in the path, making transmission flow more efficient. Using NS-3 simulator, the performance of L-CSMA in terms of packets success rate, that is, the percentage of packets that reach destination, and throughput are compared with that of IEEE 802.15.4 MAC protocol, de-facto standard for wireless sensor networks. In general, L-CSMA outperforms the IEEE 802.15.4 MAC protocol.

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L'obiettivo su cui è stata basata questa Tesi di Laurea è stato quello di integrare la tecnologia delle Wireless Sensor Networks (WSN) al contesto dell'Internet delle cose (IoT). Per poter raggiungere questo obiettivo, il primo passo è stato quello di approfondire il concetto dell'Internet delle cose, in modo tale da comprendere se effettivamente fosse stato possibile applicarlo anche alle WSNs. Quindi è stata analizzata l'architettura delle WSNs e successivamente è stata fatta una ricerca per capire quali fossero stati i vari tipi di sistemi operativi e protocolli di comunicazione supportati da queste reti. Infine sono state studiate alcune IoT software platforms. Il secondo passo è stato quindi di implementare uno stack software che abilitasse la comunicazione tra WSNs e una IoT platform. Come protocollo applicativo da utilizzare per la comunicazione con le WSNs è stato usato CoAP. Lo sviluppo di questo stack ha consentito di estendere la piattaforma SensibleThings e il linguaggio di programmazione utilizzato è stato Java. Come terzo passo è stata effettuata una ricerca per comprendere a quale scenario di applicazione reale, lo stack software progettato potesse essere applicato. Successivamente, al fine di testare il corretto funzionamento dello stack CoAP, è stata sviluppata una proof of concept application che simulasse un sistema per la rilevazione di incendi. Questo scenario era caratterizzato da due WSNs che inviavano la temperatura rilevata da sensori termici ad un terzo nodo che fungeva da control center, il cui compito era quello di capire se i valori ricevuti erano al di sopra di una certa soglia e quindi attivare un allarme. Infine, l'ultimo passo di questo lavoro di tesi è stato quello di valutare le performance del sistema sviluppato. I parametri usati per effettuare queste valutazioni sono stati: tempi di durata delle richieste CoAP, overhead introdotto dallo stack CoAP alla piattaforma Sensible Things e la scalabilità di un particolare componente dello stack. I risultati di questi test hanno mostrato che la soluzione sviluppata in questa tesi ha introdotto un overheadmolto limitato alla piattaforma preesistente e inoltre che non tutte le richieste hanno la stessa durata, in quanto essa dipende dal tipo della richiesta inviata verso una WSN. Tuttavia, le performance del sistema potrebbero essere ulteriormente migliorate, ad esempio sviluppando un algoritmo che consenta la gestione concorrente di richieste CoAP multiple inviate da uno stesso nodo. Inoltre, poichè in questo lavoro di tesi non è stato considerato il problema della sicurezza, una possibile estensione al lavoro svolto potrebbe essere quello di implementare delle politiche per una comunicazione sicura tra Sensible Things e le WSNs.

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[ITA]La demenza consiste nel deterioramento, spesso progressivo, dello stato cognitivo di un individuo. Chi è affetto da demenza, presenta alterazioni a livello cognitivo, comportamentale e motorio, ad esempio compiendo gesti ossessivi, ripetitivi, senza uno scopo preciso. La condizione dei pazienti affetti da demenza è valutata clinicamente tramite apposite scale e le informazioni relative al comportamento vengono raccolte intervistando chi se ne occupa, come familiari, il personale infermieristico o il medico curante. Spesso queste valutazioni si rivelano inaccurate, possono essere fortemente influenzate da considerazioni soggettive, e sono dispendiose in termini di tempo. Si ha quindi l'esigenza di disporre di metodiche oggettive per valutare il comportamento motorio dei pazienti e le sue alterazioni patologiche; i sensori inerziali indossabili potrebbero costituire una valida soluzione, per questo scopo. L'obiettivo principale della presente attività di tesi è stato definire e implementare un software per una valutazione oggettiva, basata su sensori, del pattern motorio circadiano, in pazienti affetti da demenza ricoverati in un'unità di terapia a lungo termine, che potrebbe evidenziare differenze nei sintomi della malattia che interessano il comportamento motorio, come descritto in ambito clinico. Lo scopo secondario è stato quello di verificare i cambiamenti motori pre- e post-intervento in un sottogruppo di pazienti, a seguito della somministrazione di un programma sperimentale di intervento basato su esercizi fisici. --------------- [ENG]Dementia involves deterioration, often progressive, of a person's cognitive status. Those who suffer from dementia, present alterations in cognitive and motor behavior, for example performing obsessive and repetitive gestures, without a purpose. The condition of patients suffering from dementia is clinically assessed by means of specific scales and information relating to the behavior are collected by interviewing caregivers, such as the family, nurses, or the doctor. Often it turns out that these are inaccurate assessments that may be heavily influenced by subjective evaluations and are costly in terms of time. Therefore, there is the need for objective methods to assess the patients' motor behavior and the pathological changes; wearable inertial sensors may represent a viable option, so this aim. The main objective of this thesis project was to define and implement a software for a sensor-based assessment of the circadian motor pattern in patients suffering from dementia, hospitalized in a long-term care unit, which could highlight differences in the disease symptoms affecting the motor behavior, as described in the clinical setting. The secondary objective was to verify pre- and post-intervention changes in the motor patterns of a subgroup of patients, following the administration of an experimental program of intervention based on physical exercises.

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In the last years radar sensor networks for localization and tracking in indoor environment have generated more and more interest, especially for anti-intrusion security systems. These networks often use Ultra Wide Band (UWB) technology, which consists in sending very short (few nanoseconds) impulse signals. This approach guarantees high resolution and accuracy and also other advantages such as low price, low power consumption and narrow-band interference (jamming) robustness. In this thesis the overall data processing (done in MATLAB environment) is discussed, starting from experimental measures from sensor devices, ending with the 2D visualization of targets movements over time and focusing mainly on detection and localization algorithms. Moreover, two different scenarios and both single and multiple target tracking are analyzed.