The aim of this thesis was to investigate the question of how to harvest RF energy and if we can harvest enough RF energy for it to be useful in an application. It is aimed towards sensor node applications, commonly used in a typical office environment. The WiFi band was chosen since it is omnipresent in the same environment. With the current development within wireless technology and the IoT domain the demand for low power electronic applications has increased and one of the challenges is to find efficient and sustainable ways of powering these types of devices.The best possible theoretical power content was initially calculated followed by measurements in an office. A circuit was designed containing an impedance matching network and rectifier. A measurement application was constructed using a microcontroller. Measurements were made in an office environment and the maximum harvested energy over 24 hours was 350 mJ. The energy was stored in a supercapacitor and is estimated to be enough to power a low energy sensor for about 30 seconds. A large part of the thesis is devoted to impedance matching involving calculating, simulating and experimenting to get a good result. / Med den nuvarande utvecklingen inom trådlös teknik och IoT-domänen har efterfrågan på elektroniska applikationer med låg effekt ökat och en av utmaningarna är att hitta effektiva och hållbara sätt att driva dessa typer av enheter. Syftet med detta projekt var att undersöka frågan hur vi skördar radiovågsenergi och kan vi skörda tillräckligt mycket med energi för att den ska vara användbar i en applikation. I ett typiskt kontor finns fler källor till radiovågor, däribland WiFi som antas ha en hög nyttjandegrad. Projektet valde att inrikta sig på WiFi bandet och undersöka om det går att utvinna tillräckligt med energi där.Projektet strävade efter att leverera en färdig produkt med alla ingående delar, en antenn, en likriktare, en lagringsenhet och ett matchningsnätverk för att anpassa antenn och likriktare till varandra. För att undersöka hur mycket energi som finns att skörda gjordes först beräkningar och sedan mätningar i bland annat ett typiskt kontor. Det konstaterades att det rör sig om väldigt låga nivåer och betonas att de apparater som använder WiFi klarar av att känna av signaler som är långt mycket lägre än de som krävs för att kunna utvinna energi. Detta innebär alltså att apparaterna kan kommunicera felfritt samtidigt som energiinnehållet är så lågt att det inte går att utvinna någon energi.Projektet ägnar stor del åt att optimera den impedansmatchning som måste ske mellan antenn och likriktare för att största möjliga effektutbyte ska kunna ske. Basen är ett kretskort med ett typiskt impedansnätverk och genom beräkningar, simuleringar och experiment tas en prototyp fram. För att kunna analysera resultaten används en mikrokontroller som tar de analoga värdena, omvandlar dem till digitala och skickar dem till en PC för analys.Mätningar gjordes i en kontorsmiljö och den maximala mängden energi som gick att utvinna var 350 mJ på 24 timmar. Energin lagrades i en superkondensator och bedöms vara tillräcklig för att driva en lågenergisensor i ca 30 sekunder.

Internet of Things (IoT) is becoming a more and more prevailing technology, as it not only makes the routine of our life easier, but it also helps industry and enteprise become more efficient. The high potential of IoT can also help support our own population on Earth, through precision agriculture, smart transportation, smart city and so on. It is therefore important that IoT is made scalable in a sustainable manner, in order to secure our own future as well.The current work is concerning transiently powered systems (TPS), which are embedded systems that use energy harvesting as their only power source. In their basic form, TPS suffer frequent reboots due to unreliable availability of energy from the environment. Initially, the throughput of such systems are therefore lower than their battery-enabled counterparts. To improve this, TPS involve checkpointing of RAM and processor state to non-volatile memory, as to keep computation progress saved throughout power loss intervals.The aim of this project is to lower the number of checkpoints necessary during an application run on a TPS in the generic case, by using approximate computing. The energy need of TPS is lowered using approximations, meaning more results are coming through when the system is working between power loss periods. For this study, the memoization technique is implemented in the form of a hash table. The Kalman filter is taken as the testing application of choice, to run on the Microchip SAM-L11 embedded platform.The memoization technique manages to yield an improvement for the Kalman application considered, versus the initial baseline version of the program. A user is allowed to ”balance” between more energy savings but more inaccurate results or the opposite, by adjusting a ”quality knob” variable epsilon ϵ.For example, for an epsilon ϵ = 0.7, the improvement is of 32% fewer checkpoints needed than for the baseline version, with the output deviating by 42% on average and 71% at its maximum point.The proof of concept has been made, being that approximate computing can indeed improve the throughput of TPS and make them more feasiable. It is pointed out however that only one single application type was tested, with a certain input trace. The hash table method implemented can behave differently depending on what application and/or data it is working with. It is therefore suggested that a pre-analysis of the specific dataset and application can be done at design time, in order to check feasiability of applying approximations for the certain case considered. / Internet of Things (IoT) håller på att bli en mer och mer utbredd teknik, eftersom det inte bara underlättar rutiner i vårt liv, utan det hjälper också industrin och företag att bli effektivare. Den höga potentialen med IoT kan också hjälpa till att ge stöd åt vår egen befolkning på jorden, genom precisionslantbruk, smart transport, smarta städer och mer. Det är därför viktigt att IoT görs skalbart på ett hållbart sätt för att säkra vår egen framtid.Det nuvarande arbetet handlar om transientdrivna system (TPS), vilket är inbäddade system som använder energiskörning som sin enda kraftkälla. I sin grundform har TPS ofta återställningar på grund av opålitlig tillgång till energi från miljön. Ursprungligen är därför sådana systems genomströmning lägre än deras batteriaktiverade motsvarigheter. För att förbättra detta använder TPS kontrollpunkter i RAM och processortillstånd till icke-flyktigt minne, för att hålla beräkningsförloppet sparat under strömförlustintervaller.Syftet med detta projekt är att sänka antalet kontrollpunkter som krävs under en applikationskörning på en TPS i ett generiskt fall, genom att använda ungefärlig datorberäkning. Energibehovet för TPS sänks med ungefärliga belopp, vilket innebär att fler resultat kommer när systemet arbetar mellan strömförlustperioder. För denna studie implementeras memoiseringstekniken i form av en hashtabell. Kalman-filtret tas som testapplikation för att köra på Microchip SAM-L11 inbäddad plattform.Memoization-tekniken lyckas ge en förbättring för Kalman-applikationen som beaktades, jämfört med den ursprungliga baslinjeversionen av programmet. En användare får ”balansera” mellan mer energibesparingar men mer felaktiga resultat eller motsatsen genom att justera en ”kvalitetsrat”-variabel epsilon ϵ. Till exempel, för en epsilon ϵ = 0.7, är förbättringen 32% färre kontrollpunkter som behövs än för baslinjeversionen, med en utdata avvikelse med 42% i genomsnitt och 71% vid sin högsta punkt.Beviset på konceptet har gjorts, att ungefärlig databeräkning verkligen kan förbättra genomströmning av TPS och göra dem mer genomförbara. Det påpekas dock att endast en enda applikationstyp testades, med ett visst inmatningsspår. Den implementerade hashtabellmetoden kan bete sig annorlunda beroende på vilken applikation och/eller data den arbetar med. Det föreslås därför att en föranalys av det specifika datasättet och applikationen kan göras vid designtidpunkten för att kontrollera genomförbarheten av att tillämpa ungefärliga belopp för det aktuella fallet.

No description available.

This thesis presents an active clamp forward converter for use in the Energy Harvesting From Exercise Machines project. Ideally, this converter will find use as the centerpiece in a process that links elliptical trainers to the California grid. This active clamp forward converter boasts a 14V-60V input voltage range and 150W power rating, which closely match the output voltage and power levels from the elliptical trainer. The isolated topology outputs 51V, higher than previous, non-isolated attempts, which allows the elliptical trainers to interact with a central grid-tied inverter instead of many small ones. The final converter operated at greater than 86% efficiency over most of the elliptical trainer’s input range, and produced very little noise, making it a solid choice for this implementation.

This thesis entails the design and fabrication of a smartphone charger that is powered by a bicycle dynamo hub. In addition to the design and validation of the charger prototype, this thesis involves the testing and characterization of the dynamo hub power source, the design and construction of specialized test equipment, and the design and prototyping of a handlebar-mounted case for the smartphone and charging electronics. With the intention of making the device a commercial product, price, aesthetics, and marketability are of importance to the design. An appropriate description of the charger circuit is a microcontroller-based energy management system, tailored to meet strict power demands of current smartphones. The system incorporates a switched-mode power supply, lithium polymer battery, microcontroller, and specialized protection circuitry. Prototype testing confirms that the circuit meets the charging requirements of the smartphone at bicycle speeds ranging from 7 miles per hour to as high as 55 miles per hour.

Energy Harvesting, also known as power harvesting or ambient power, is the process of obtaining small amounts of power from secondary sources, such as vibrations, light, temperature variations and even radio-frequency emissions. These systems have been uncommon in personal and wearable electronics in the past, however they are slowly gaining traction. With the increasing sophistication of prosthetic limbs and implants, devices that in some cases require a consistent and reliable power source, the potential field of application for energy harvesting grows wider. This thesis project evaluates whether energy harvesting methods could be implemented in future prosthetic limb designs without significantly affecting weight, user comfort, complexity of design etc., and whether the gains of such an implementation would be worth the effort and cost put into it. For reference the project used the RHEO KNEE® by Össur Hf., a microcontroller controlled prosthetic knee, as a device that such a system could be integrated with. Energy harvesting is still an emerging field and is a long time away from being a viable primary power source for most electronic devices. However, it still might have potential as a supplementary source for extending charge cycles or making smaller (and therefore more lightweight) power cells viable. This master’s thesis project was broad in scope and included 3D-design; mechanical, electrical and embedded software design; and setting up a miniature kinetic power generator as well as a photovoltaic harvesting system. No amputees were available for testing the designs so the system was tested with a 3D-printed model that was moved by hand to simulate the generation process. Due to some incorrect inital assumptions, the final electronic design was not optimal for this kind of system. However, a kinetic generator that harvested power from a modeled heel striking the ground 50 times a minute produced about 23mW of power. 53cm2 of photovoltaic panels produced 42μW of power in an ambient light setting. For comparison, a low-power microcontroller needed about 119μW of power on average to do some simple processing and send Bluetooth transmissions once every two seconds. / Energiinsamling (e. Energy Harvesting), är processen för att erhålla små mängder kraft från sekundära källor, såsom vibrationer, ljus, temperaturvariationer och utstrålning i radiofrekvens. Dessa system har varit ovanliga i hemelektronik och bärbar teknik, men de vinner sakta dragkraft. Med den ökande förfining av proteser och implantat, som i vissa fall kräver en jämn och pålitlig strömkälla, växer det potentiella användningsområdet för energiinsamling. Detta examensarbete utvärderar huruvida energiinsamlingsmetoder skulle kunna implementeras i framtida proteskonstruktioner utan att nämnvärt påverka vikt, användarkomfort, komplexitet i design etc., och om vinsterna med en sådan implementering skulle vara värd ansträngningen och kostnaden. Som exempel använde detta projekt en datoriserad knäprotes av Össur HF, RHEO KNEE®, som exempel på ett system som energiinsamling skulle kunna integreras med. Energiinsamling är fortfarande ett växande forskningsområde och är långt ifrån att en strömkälla för det mesta elektronik.. Det kan ändå ha potential som en kompletterande strömkälla som kan förlänga laddningscykler eller göra mindre (och därför lättare) batterier möjliga. Detta examensarbete var brett i omfattning och inkluderade 3D-design; mekanisk-, elektrisk- och mjukvara-design; och inrättning av en kinetisk kraftgenerator i miniatyr samt ett ljusdrivet energiinsamlingssystem. Inga amputerade var tillgängliga för att testa designen, därför så testades systemet med en 3D-printad modell som rördes för hand för att simulera strömförsörjelseprocessen. På grund av några felaktiga initiala antaganden var den slutliga elektroniska designen inte optimal för denna typ av system. Ändå lyckades en kinetisk generator som använde energiinsamlingsprinciper producera cirka 23mW ström genom en simulerad häl som träffade marken cirka 50 gånger i minuten. 53cm2 solcellspaneler producerade 42μW energi i en ljussatt miljö. Som jämförelse behövde en strömsnål styrkrets i genomsnitt cirka 119μW effekt för att genomföra enkla programprocesser och skicka Bluetooth-överföringar en gång varannan sekund. / Hliðarorkuöflun (e. energy harvesting), sem einnig bætti kalla umhverfisöflun, er ferlið við að fá lítið magn af orku frá óbeinum aflgjafa, svo sem frá hristingi, ljósi, hitabreytingum og jafnvel útvarpsbylgjum. Þessi kerfi hafa verið sjaldgæf í raftækjum hingað til, þó þau eru hægt og rólega að fá hlutdeild. Með nýrri og fágaðri gervilimum og ígræðslum, tæki sem í sumum tilvikum þurfa samfellda og áreiðanlega orkjugjafa, víkkar mögulegt notkunarsvið hliðarorkuöflunar. Þetta lokaverkefni metur hvort aðferðir við hliðarorkuöflun gætu verið notaðar í hönnun gervilima framtíðarinnar án þess að hafa neikvæð áhrif á þyngd, þægilegheit, flóknun hönnunar o.þ.h., og hvort hagur sé í samræmi við framlag og kostnað. Þetta verkefni notar RHEO KNEE® frá Össuri Hf. sem viðmið, sem er gervihné stjórnað af örtölvu. Viðmiðinu er ætlað að sýna notagildi kerfisins. Hliðarorkuöflun er ennþá svið í þróun og er nokkuð í að það geti orðið frumorkugjafi fyrir flest raftæki. Hins vegar þá gæti það enn átt möguleika á að vera aukaorkugjafi til að auka tímalengd hverrar hleðslu eða gera minni og léttari rafhlöður raunhæfari. Þetta meistaraverkefni var viðamikið að því leiti að það fól í sér þrívíddarhönnun; vél-, raf- og hugbúnaðarhönnun; og uppsetningu á hreyfirafal ásamt ljósorkuöflunarkerfi. Engir einstaklingar sem misst hafa fót voru til staðar til að prófa hannanir þessa verkefnis. Þ.a.l. voru þær prófaðar með þrívíddarprentuðum líkönum sem hreyfð voru með handafli til að líkja eftir orkuframleiðsluferlinu. Vegna rangrar upprunalegrar forsendu þá var endanleg rafhönnunin ekki ákjósanleg fyrir slíkt kerfi. Hreyfirafall tengdur við gervihæl sem sló jörðu 50 sinnum á mínútu framleiddi þó 23mW af orku. 53cm2 af ljósorkueiningum framleiddu 42μW af afli í meðal herbergisbirtu. Til samanburðar þá eyðir skilvirk örtölva u.þ.b. 119μW af afli í einfaldri tölvuvinnslu ásamt því að senda Bluetooth sendingu á tveggja sekúnda fresti.

A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables. / Doctor of Philosophy / A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation first develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems. Afterwards, using tools from optimization theory and reinforcement learning, novel AoI-aware scheduling policies are developed while accounting for the energy constraints at the transmitter nodes for several settings of communication networks, including unmanned aerial vehicles (UAVs)-assisted and radio frequency (RF)-powered communication networks, in the process of decision-making. In the first part of this dissertation, a stochastic hybrid system (SHS)-based general framework is first developed to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. Afterwards, this framework is applied to derive the stationary marginal and joint moment generating functions (MGFs) for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of a UAV as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which RF-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.

The search for renewable energy resources and ways to harvest them has become a global mainstream topic among researchers nowadays, with solar cells and thermoelectric generators among the energy harvesting technologies currently being researched in vast. CZTS (Cu2ZnSnS4), a p-type semiconducting material initially researched to replace copper indium gallium selenide (CIGS) as the light absorbing layer in thin film solar cells, was studied in this doctoral work for alternative uses in energy harvesting. This work aims to systemically investigate the prospects of CZTS to be used as hole transport layers and thermoelectric generators. CZTS thin film was successfully fabricated using a versatile approach involving hot-injection synthesis of CZTS nanoparticles ink followed by spin coating and thermal treatment. Results obtained revealed the possibility to fine control CZTS thin film fabrication based on ink concentration and spin. Besides that, thermal treatment temperature was found to affect the film’s overall properties, where an increase in thermal treatment temperature improved the degree of crystallinity and electrical properties. In addition, a phase change going from less stable cubic and wurtzite structures to a more stable tetragonal structure was also observed. Furthermore, CZTS was found to be a good candidate to replace the commonly used organic hole transport layer in perovskite solar cells, with potentials in improving performance and stability. In addition, CZTS also possessed good transport properties to be a potential p-type material in a thermoelectric generator, with the preliminary performance of fabricated CZTS/AZO thermoelectric generator showing a maximum power output of ~350 nW at ~170 KΔT. These findings provide new perspectives for CZTS in energy harvesting applications, despite the struggle in its development as the absorber layer in thin film solar cells. Besides providing a deeper understanding of CZTS and its vast possibilities in energy harvesting applications, promising future research stemming from this work is also limitless, reinventing ways in material studies, in search of alternative applications which may be of benefit.

No description available.

Dedicated sensors are widely used throughout many industries to monitor everyday operations, maintain safety, and report performance characteristics. In order to adopt a more sustainable solution, much research is being applied to self-powered sensing, implementing solutions which harvest wasted ambient energy sources to power these dedicated sensors. The adoption of not only wireless sensor nodes, but also self-powered capabilities in the nuclear energy process is critical as it can address issues in the overall safety and longevity of nuclear power. The removal of wires for data and power transmission can greatly reduce the cost of both installation and upkeep of power plants, while self-powered capabilities can further reduce effort and money spent in replacing batteries, and importantly may enable sensors to work even in losses to power across the plant, increasing plant safety. This thesis outlines three harvesting opportunities in the nuclear energy process from: thermal, vibration, and radiation sources in the main structure of the power plant, and from thermal and radiation energy from spent fuel in dry cask storage. Thermal energy harvesters for the primary and secondary coolant loops are outlined, and experimental analysis done on their longevity in high-radiation environments is discussed. A vibrational energy harvester for large rotating plant machine vibration is designed, prototyped, and tested, and a model is produced to describe its motion and energy output. Finally, an introduction to the design of a gamma radiation and thermal energy harvester for spent nuclear fuel canisters is discussed, and further research steps are suggested. / Master of Science / In this work multiple energy harvesters are investigated aimed at collecting wasted ambient energy to locally power sensor nodes in nuclear power plants, and in spent nuclear fuel canisters. Locally self-powered, wireless sensors can increase safety and reliability throughout the nuclear process. To address this a thermal energy harvester is tested in a radiation rich environment, and its performance before and after irradiation is analyzed. A vibrational energy harvester designed for use on large rotating machinery is discussed, manufactured, and tested, and a mathematical model describing it is produced. Finally, an introduction to harvesting radiation and heat given off from spent nuclear fuel in dry cask canister storage is investigated. Power capabilities for each design are considered, and the impact of such energy harvesting for wireless sensor nodes on the longevity, safety, and reliability of nuclear power plants is discussed.