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  • Disputas: 2019-08-19 10:00 F3, Stockholm
    Maros, Marie
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Teknisk informationsvetenskap.
    Distributed Optimization in Time-Varying Environments2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Solving optimization problems in a distributed manner is critical in many systems. Many relevant systems are distributed in nature in the sense that they consist of autonomous agents that are to come to a joint decision based on a certain metric. In many cases, these agents may collect information independently and would therefore have to centralize all the data. In applications were this is not a viable approach distributed solutions are desirable.

    In this thesis, we study distributed optimization methods in time-varying environments. In the first part of the thesis, we consider optimization problems that evolve over time in a controlled manner. We propose the use of the Alternating Direction Method of Multipliers (ADMM) due to its flexibility in step-size selection. We establish ADMM's ability to follow an optimal point as it moves over time. In our set-up, a distributed variant of ADMM is allowed to perform a single iteration per problem change. Under smoothness assumptions on the objective and constraint functions we establishthat there exists a sufficiently small variation of the problem data for which we can guarantee that ADMM will be able to follow the optimal point in a decentralized manner. These conditions are less stringent than the conditions found in the literature. Later on, we introduce a stochastic model for the variation of the problem's data. Under some assumptions, we establish that decentralized ADMM is capable of remaining in a bounded mean square neighbourhood of a primal-dual optimal point. Introducing a stochastic model allows to us relax many of the requirements found in the literature, while still providing some guarantees. We provide with application examples and simulations for both scenarios.

    In the second part of the thesis we consider distributed optimization methods that converge over time-varying networks. We propose the first dual method to converge linearly on time-varying networks, in which we allow the networks to become disconnected. We establish that the method converges R-linearly and illustrate that under some circumstances it performs better than other state of the art methods, while, at the same time, cutting the required information exchanges in half. Since the proposed method is computationally quite expensive we propose a linearized and therefore computationally cheaper version of our method. Finally, we establish that the linearized version will also converge R-linearly on time-varying graphs and quantify the loss in convergence rate due to the approximation.

  • Disputas: 2019-08-20 10:00 F3, Stockholm
    Birru, Eyerusalem Deresse
    KTH, Skolan för industriell teknik och management (ITM), Energiteknik, Kraft- och värmeteknologi.
    Process Utility Performance Evaluation and Enhancements in the Traditional Sugar Cane Industry2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The need to achieve sustainable development has led to devising various approaches for the efficient utilization of natural resources. Renewable energy technology and energy efficiency measures feature prominently in this regard, and in particular for industries such as sugar production:  the sugar cane industry’s eponymous feedstock is a renewable resource, and mills have potential for increased energy savings via improvements to cogeneration units, electric drive retrofitting, and other measures.  The overall objective of this research work is to investigate different approaches of efficiency improvements for enhancing sugar cane conversion, thereby increasing the services obtained including surplus electric power delivery. Traditional sugar cane mills, i.e. those that lack modern components such as high-performance boilers and electric drives, are the focus of this investigation. 

    System simulations show that modern mills generate more electrical power as compared to traditional mills, with power-to-heat ratios nearly one order of magnitude higher (i.e. 0.3-0.5).  Comparison of the thermodynamic performance of three retrofits showed that such modifications could raise the performance of traditional mills to approach those for their modern counterparts. Results for a base case traditional plant show that losses related to mechanical prime movers are high, since the mills and shredder are driven by steam and generate excess mechanical power. When considering press mill stoppages, steam is wasted during the ensuing fuel oil-fired start-up period. CO2emission for such transient conditions can be decreased owing via bagasse drying and storage. 


    In studying both energy and water impacts, a comparison of four technological improvements demonstrates benefits outside the crushing season for three scenarios: recovery of excess wastewater for enhanced imbibition; recovery of waste heat for thermally-driven cooling; and pelletization of excess bagasse. The fourth option, involving upgrading of the mill’s cogeneration unit, is advantageous for continuous surplus power supply.

  • Disputas: 2019-08-23 09:00 Sal B, Kista
    Yao, Yuan
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Power and Performance Optimization for Network-on-Chip based Many-Core Processors2019Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    Network-on-Chip (NoC) is emerging as a critical shared architecture for CMPs (Chip Multi-/Many-Core Processors) running parallel and concurrent applications. As the core count scales up and the transistor size shrinks, how to optimize power and performance for NoC open new research challenges.

    As it can potentially consume 20--40\% of the entire chip power, NoC power efficiency has emerged as one of the main design constraints in today's and future high performance CMPs. For NoC power management, we propose a novel on-chip DVFS technique that is able to adjust per-region NoC V/F according to voted V/F levels from communicating threads. A thread periodically votes for a preferred NoC V/F level that best suits its individual performance interests. The final DVFS decision of each region is adjusted by a region DVFS controller democratically based on the majority of votes it receives.

    Mutually exclusive locks are pervasive shared memory synchronization primitives. In advanced locks such as the Linux queue spinlock comprising a low-overhead spinning phase and a high-overhead sleeping phase, we show that the lock primitive may create very high competition overhead (COH), which is the time threads compete with each other for the next critical section grant. For performance enhancement, we propose a software-hardware cooperative mechanism that can opportunistically maximize the chance of a thread winning critical section in the low-overhead spinning phase and minimize the chance of winning critical section in the high-overhead sleeping phase, so that COH is significantly reduced. Besides, we further observe that the cache invalidation-acknowledgement round-trip delay between the home node storing the critical section lock and the cores running competing locks can heavily downgrade application performance. To reduce such high lock coherence overhead (LCO), we propose in-network packet generation (iNPG) to turn passive ``normal'' NoC routers into active ``big'' ones that can not only transmit but also generate packets to perform early invalidation and collect inv-acks. iNPG effectively shortens the protocol round-trip delay and thus largely reduces LCO in various locking primitives.

    To enhance performance fairness when running multiple multi-threaded programs on a single CMP, we develop the concept of aggregate flow which refers to a sequence of associated data and cache coherence flows issued from the same thread. Based on the aggregate flow concept, we propose three coherent mechanisms to efficiently achieve performance isolation: rate profiling, rate inheritance and flow arbitration. Rate profiling dynamically characterizes thread performance and communication needs. Rate inheritance allows a data or coherence reply flow to inherit the characteristics of its associated data or coherency request flow, so that consistent bandwidth allocation policy is applied to all sub-flows of the same aggregate flow. Flow arbitration uses a proven scheduling policy, self-clocked fair queueing (SCFQ), to achieve rate-proportional arbitration for different aggregate flows. Our approach successfully achieves balanced performance isolations with different mixtures of applications.

  • Disputas: 2019-08-23 10:00 F3, Stockholm
    Töpfer, Fritzi
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Mikro- och nanosystemteknik.
    Micromachined Microwave Sensors for Non-Invasive Skin Cancer Diagnostics2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Malignant melanoma is one of the cancers with the highest incident rates. It is also the most dangerous skin cancer type and an early diagnosis is crucial for the successful treatment of malignant melanoma patients. If it is diagnosed and treated at an early stage, the survival rate for patients is 99%, however, this is reduced to only 25% if diagnosed at a later stage. The work in this thesis combines microsystem technology, microwave engineering and biomedical engineering to develop a sensing tool for early-stage malignant melanoma diagnostics. Such a tool could not only increase the clinical accuracy of malignant melanoma diagnosis, but also reduce the time needed for examination, and lower the number of unnecessary biopsies. Furthermore, a reliable and easy-to-use tool can enable non-specialist healthcare personnel, including primary care physicians or nurses, to perform a prescreening for malignant melanoma with a high sensitivity. Consequently, a large number of patients could receive a timely examination despite the shortage of dermatologists, which exists in many healthcare systems. The dielectric properties of tumor tissue differ from healthy tissue, which is mainly accounted to a difference in the water content. This difference can be measured by a microwave-based sensing technique called microwave reflectometry. Previously reported microwave-based skin measurements largely relied on standard open-ended waveguide probes that are not suitable for early-stage skin tumor diagnosis. Thus, alternative near-field probe designs based on micromachined dielectric-rod waveguides are presented here. The thesis focuses on a broadband microwave probe that operates in the W-band (75 to 110 GHz), with a sensing depth and resolution tailored to small and shallow skin tumors, allowing a high sensitivity to early-stage malignant melanoma. Prototypes of the probe were fabricated by micromachining and characterized. For the characterization, a novel type of silicon-based heterogeneous sample with tailor-made permittivity was introduced. Furthermore, the performance of the probe was evaluated in vivo. First, through measurements on human volunteers, it was shown that the probe is sensitive to artificially induced changes of the skin hydration. Then, measurements on murine skin melanoma models were performed and small early-stage skin tumors were successfully distinguished from healthy skin. Additionally, a resonant probe for microwave skin sensing was designed and micromachined protoypes were tested on phantom materials. However, the resonant probe was found less suitable than the broadband probe for the measurements on skin. The broadband probe presented in this thesis is the first microwave nearfield probe specifically designed for early-stage malignant melanoma diagnostics and successfully evaluated in vivo.

  • Disputas: 2019-08-29 13:15 F3, Stockholm
    Vu, Minh Thành
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Teknisk informationsvetenskap.
    Perspectives on Identification Systems2019Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    Identification systems such as biometric identification systems have been becoming ubiquitous. Fundamental bounds on the performance of the systems have been established in literature. In this thesis we further relax several assumptions in the identification problem and derive the corresponding fundamental regions for these settings.

    The generic identification architecture is first extended so that users’ information is stored in two layers. Additionally, the processing is separated in two steps where the observation sequence in the first step is a noisy, pre-processed version of the original one. This setting generalizes several known settings in the literature. Given fixed pre-processing schemes, we study optimal trade-offs in the discrete and Gaussian cases. As corollaries we also provide characterizations for related problems.

    In a second aspect, the joint distribution in the identification problem is relaxed in several ways. We first assume that all users’ sequences are drawn from a common distribution, which depends on a state of the system. The observation sequence is induced by a channel which has its own state. Another variant, in which the channel is fixed, however the distributions of users’ sequences are not necessarily identical, is considered next. We then study the case that users’ data sequence are generated independently from a mixture distribution. Optimal performance regions of these settings are provided. We further give an inner bound and an outer bound on the region when the observation channel varies arbitrarily. Additionally, we strengthen the relation between the Wyner-Ahlswede-Körner problem and the identification problem and show the equivalence of these two.

    Finally, we study a binary hypothesis testing problem which decides whether or not the observation sequence is related to one user in the database. The optimal exponent of the second type of error is studied. Furthermore, we show that the single-user testing against independence problem studied by Ahlswede and Csiszár is equivalent to the identification problem as well as the Wyner-Ahlswede-Körner problem.

  • Disputas: 2019-08-30 10:00 F3, Stockholm
    Henschen, Jonatan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Fiber- och polymerteknologi.
    Bio-based preparation of nanocellulose and functionalization using polyelectrolytes2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [sv]

    Nanocellulosa, som kan utvinnas från skogsråvara, har de senaste åren fått mycket uppmärksamhet för sina intressanta egenskaper och breda användningsområde. Studierna i denna avhandling syftar till att vidga möjligheterna att använda nanocellulosa i olika applikationer. Detta har skett genom att utveckla en ny metod för att tillverka nanocellulosa och genom att studera möjligheten att adsorbera polyelektrolyter på material av nanocellulosa för att ändra hur bakterier interagerar med dessa.

    Nanocellulosan tillverkades genom att förbehandla pappersmassa med smält oxalsyra dihydrat. Reaktionsblandningen tvättades med etanol, aceton eller tetrahydrofuran innan den torkades och fibrillerades. Den resulterande nanocellulosan erhölls med högt utbyte, hade hög ytladdning (upp till 1,4 mmol g-1) och innehöll partiklar som både liknande nanofibriller och nanokristaller. Materialet visades kunna användas både för att tillverka Pickering emulsioner och tunna filmer med en styrka upp till 197 MPa, töjning upp till 5 %, E-modul upp till 10,6 GPa och syrepermeabilitet ner till 0.31 cm3 µm m‑2 dag‑kPa‑1.

    Genom att adsorbera polyvinylamin och polyakrylsyra på material av nanocellulosa visades det vara möjligt att påverka mängden bakterier som fäster till materialet. Substraten bestod både av kompakta filmer och porösa aerogeler. Genom att variera ytladdningen på materialen, ytans struktur och antalet adsorberade lager av polymererna var det möjligt att tillverka material med både hög och låg bakterieadhesion. Detta gör det möjligt att anpassa material för användning antingen som kontaktaktivt- eller icke-adhesivt antibakteriellt material. Båda dessa kan vara miljövänliga alternativ till dagens antibakteriella material.

    Nanocellulosa är ett material som inom snar framtid sannolikt kommer användas inom en mängd olika applikationer. För att öka mängden applikationer där nanocellulosa tillför ett stort värde är det nödvändigt att utveckla alternativa tillverkningsmetoder till dagens välkända, exempelvis, genom att använda den beskrivna oxaleringen som förbehandling. Förmågan att styra bakterieadhesionen på material av nanocellulosa ger därtill möjlighet att hitta nya användningsområden inom t.ex. hälso- och sjukvårdsbranschen.

  • Disputas: 2019-08-30 13:00 Sal C, Kungl Tekniska högskolan, Stockholm
    Chaourani, Panagiotis
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektronik, Integrerade komponenter och kretsar.
    Sequential 3D Integration - Design Methodologies and Circuit Techniques2019Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    Sequential 3D (S3D) integration has been identified as a potential candidate for area efficient ICs. It entails the sequential processing of tiers of devices, one on top the other. The sequential nature of this processing allows the inter-tier vias to be processed like any other inter-metal vias, resulting in an unprecedented increase in the density of vertical interconnects. A lot of scientific attention has been directed towards the processing aspects of this 3-D integration approach, and in particular producing high-performance top-tier transistors without damaging the bottom tier devices and interconnects.As far as the applications of S3D integration are concerned, a lot of focus has been placed on digital circuits. However, the advent of Internet-of-Things applications has motivated the investigation of other circuits as well.

    As a first step, two S3D design platforms for custom ICs have been developed, one to facilitate the development of the in-house S3D process and the other to enable the exploration of S3D applications. Both contain device models and physical verification scripts. A novel parasitic extraction flow for S3D ICs has been also developed for the study of tier-to-tier parasitic coupling.

    The potential of S3D RF/AMS circuits has been explored and identified using these design platforms. A frequency-based partition scheme has been proposed, with high frequency blocks placed in the top-tier and low-frequency ones in the bottom. As a proof of concept, a receiver front-end for the ZigBee standard has been designed and a 35% area reduction with no performance trade-offs has been demonstrated.

    To highlight the prospects of S3D RF/AMS circuits, a study of S3D inductors has been carried out. Planar coils have been identified as the most optimal configuration for S3D inductors and ways to improve their quality factors have been explored. Furthermore, a set of guidelines has been proposed to allow the placement of bottom tier blocks under top-tier inductors towards very compact S3D integration. These guidelines take into consideration the operating frequencies and type of components placed in the bottom tier.

    Lastly, the prospects of S3D heterogeneous integration for circuit design have been analyzed with the focus lying on a Ge-over-Si approach. Based on the results of this analysis, track-and-hold circuits and digital cells have been identified as potential circuits that could benefit the most from a Ge-over-Si S3D integration scheme, thanks to the low on-resistance of Ge transistors in the triode region. To improve the performance of top-tier Ge transistors, a processing flow that enables the control of their back-gates has been also proposed, which allows controlling the threshold voltage of top-tier transistors a truntime.

  • Disputas: 2019-09-06 10:00 Kollegiesalen, Stockholm
    Aljure, Mauricio
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Pre-breakdown Phenomena in Mineral Oil Based Nanofluids2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Mineral oil is a dielectric liquid commonly used in high voltage equipment such as power transformers. Interestingly, it has been experimentally observed that the dielectric strength of the mineral oil is improved when nanoparticles are added. However, the mechanisms behind these improvements are not well understood, hindering the further innovation process of these so-called nanofluids. This thesis aims to contribute to the understanding of the mechanisms explaining the dielectric strength improvement of the base oil when nanoparticles are added.For this, several experiments and numerical simulations are performed in this thesis. The initiation voltage of electric discharges infive different kind of nanofluids was measured. The large data set obtained allowed to cast experimental evidence on the existing hypotheses that are used to explain the effect of nanoparticles. It is found that hydrophilic nanoparticles hinder the electric discharge initiation from anode electrodes. On the other hand, electric discharge initiation from cathode electrodes was hindered by nanoparticles with low charge relaxation time.The electric currents in mineral oil and nanofluids were also measured under intense electric fields (up to 2GV/m). It is found that the addition of certain nanoparticles increases the measured currents. The possible physical mechanisms explaining the measured currents inmineral oil with and without nanoparticles were thoroughly discussed based on results of numerical simulations. Preliminary parameters used in this thesis to model these mechanisms led to a good agreement between the measured and simulated electric currents.

  • Disputas: 2019-09-06 10:00 F3, Stockholm
    Abdalmoaty, Mohamed
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH Royal Institute of Technology.
    Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions2019Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. During the last decade, several numerical methods have been developed to approximately solve the maximum likelihood problem. A class of algorithms that attracted considerable attention is based on sequential Monte Carlo algorithms (also known as particle filters/smoothers) and particle Markov chain Monte Carlo algorithms. These algorithms were able to obtain impressive results on several challenging benchmark problems; however, their application is so far limited to cases where fundamental limitations, such as the sample impoverishment and path degeneracy problems, can be avoided.

    This thesis introduces relatively simple alternative parameter estimation methods that may be used for fairly general stochastic nonlinear dynamical models. They are based on one-step-ahead predictors that are linear in the observed outputs and do not require the computations of the likelihood function. Therefore, the resulting estimators are relatively easy to compute and may be highly competitive in this regard: they are in fact defined by analytically tractable objective functions in several relevant cases. In cases where the predictors are analytically intractable due to the complexity of the model, it is possible to resort to {plain} Monte Carlo approximations. Under certain assumptions on the data and some conditions on the model, the convergence and consistency of the estimators can be established. Several numerical simulation examples and a recent real-data benchmark problem demonstrate a good performance of the proposed method, in several cases that are considered challenging, with a considerable reduction in computational time in comparison with state-of-the-art sequential Monte Carlo implementations of the ML estimator.

    Moreover, we provide some insight into the asymptotic properties of the proposed methods. We show that the accuracy of the estimators depends on the model parameterization and the shape of the unknown distribution of the outputs (via the third and fourth moments). In particular, it is shown that when the model is non-Gaussian, a prediction error method based on the Gaussian assumption is not necessarily more accurate than one based on an optimally weighted parameter-independent quadratic norm. Therefore, it is generally not obvious which method should be used. This result comes in contrast to a current belief in some of the literature on the subject. 

    Furthermore, we introduce the estimating functions approach, which was mainly developed in the statistics literature, as a generalization of the maximum likelihood and prediction error methods. We show how it may be used to systematically define optimal estimators, within a predefined class, using only a partial specification of the probabilistic model. Unless the model is Gaussian, this leads to estimators that are asymptotically uniformly more accurate than linear prediction error methods when quadratic criteria are used. Convergence and consistency are established under standard regularity and identifiability assumptions akin to those of prediction error methods.

    Finally, we consider the problem of closed-loop identification when the system is stochastic and nonlinear. A couple of scenarios given by the assumptions on the disturbances, the measurement noise and the knowledge of the feedback mechanism are considered. They include a challenging case where the feedback mechanism is completely unknown to the user. Our methods can be regarded as generalizations of some classical closed-loop identification approaches for the linear time-invariant case. We provide an asymptotic analysis of the methods, and demonstrate their properties in a simulation example.

  • Disputas: 2019-09-12 13:00 F3, Stockholm
    Bütepage, Judith
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Generative models for action generation and action understanding2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The question of how to build intelligent machines raises the question of how to rep-resent the world to enable intelligent behavior. In nature, this representation relies onthe interplay between an organism’s sensory input and motor input. Action-perceptionloops allow many complex behaviors to arise naturally. In this work, we take these sen-sorimotor contingencies as an inspiration to build robot systems that can autonomouslyinteract with their environment and with humans. The goal is to pave the way for robotsystems that can learn motor control in an unsupervised fashion and relate their ownsensorimotor experience to observed human actions. By combining action generationand action understanding we hope to facilitate smooth and intuitive interaction betweenrobots and humans in shared work spaces.To model robot sensorimotor contingencies and human behavior we employ gen-erative models. Since generative models represent a joint distribution over relevantvariables, they are flexible enough to cover the range of tasks that we are tacklinghere. Generative models can represent variables that originate from multiple modali-ties, model temporal dynamics, incorporate latent variables and represent uncertaintyover any variable - all of which are features required to model sensorimotor contin-gencies. By using generative models, we can predict the temporal development of thevariables in the future, which is important for intelligent action selection.We present two lines of work. Firstly, we will focus on unsupervised learning ofmotor control with help of sensorimotor contingencies. Based on Gaussian Processforward models we demonstrate how the robot can execute goal-directed actions withthe help of planning techniques or reinforcement learning. Secondly, we present anumber of approaches to model human activity, ranging from pure unsupervised mo-tion prediction to including semantic action and affordance labels. Here we employdeep generative models, namely Variational Autoencoders, to model the 3D skeletalpose of humans over time and, if required, include semantic information. These twolines of work are then combined to implement physical human-robot interaction tasks.Our experiments focus on real-time applications, both when it comes to robot ex-periments and human activity modeling. Since many real-world scenarios do not haveaccess to high-end sensors, we require our models to cope with uncertainty. Additionalrequirements are data-efficient learning, because of the wear and tear of the robot andhuman involvement, online employability and operation under safety and complianceconstraints. We demonstrate how generative models of sensorimotor contingencies canhandle these requirements in our experiments satisfyingly.

  • Disputas: 2019-09-13 13:00 Sal B, Kista
    Sollami Delekta, Szymon
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektronik.
    Inkjet Printing of Graphene-based Microsupercapacitors for Miniaturized Energy Storage Applications2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Printing technologies are becoming increasingly popular because they enable the large-scale and low-cost production of functional devices with various designs, functions, mechanical properties and materials. Among these technologies, inkjet printing is promising thanks to its direct (mask-free) patterning, non-contact nature, low material waste, resolution down to 10 µm, and compatibility with a broad range of materials and substrates. As a result, inkjet printing has applications in several fields like wearables, opto-electronics, thin-film transistors, displays, photovoltaic devices, and in energy storage. It's in energy storage that the technique shows its full potential by allowing the production of miniaturized devices with a compact form factor, high power density and long cycle life, called microsupercapacitors (MSCs). To this end, graphene has a number of remarkable properties like high electrical conductivity, large surface area, elasticity and transparency, making it a top candidate as an electrode material for MSCs.

    Some key drawbacks limit the use of inkjet printing for the production of graphene-based MSCs. This thesis aims at improving its scalability by producing fully inkjet printed devices, and extending its applications through the integration of inkjet printing with other fabrication techniques.

    MSCs typically rely on the deposition by hand of gel electrolyte that is not printable or by submerging the whole structure into liquid electrolyte. Because of this, so far large-scale production of more than 10 interconnected devices has not been attempted. In this thesis, a printable gel electrolyte ink based on poly(4-styrene sulfonic acid) was developed, allowing the production of large arrays of more than 100 fully inkjet printed devices connected in series and parallel that can be reliably charged up to 12 V. Also, a second electrolyte ink based on nano-graphene oxide, a solid-state material with high ionic conductivity, was formulated to optimize the volumetric performance of these devices. The resulting MSCs were also fully inkjet printed and exhibited an overall device thickness of around 1 µm, yielding a power density of 80 mW cm-3.

    Next, the use of inkjet printing of graphene was explored for the fabrication of transparent MSCs. This application is typically hindered by the so-called coffee-ring effect, which creates dark deposits on the edges of the drying patterns and depletes material from the inside area. In light of this issue, inkjet printing was combined with etching to remove the dark deposits thus leaving uniform and thin films of graphene with vertical sidewalls. The resulting devices showed a transmittance of up to 90%.

    Finally, the issue of the substrate compatibility of inkjet printed graphene was addressed. Although inkjet printing is considered to have broad substrate versatility, it is unreliable on hydrophilic or porous substrates and most inks (including graphene inks) require thermal annealing that damages substrates that are not resistant to heat. Accordingly, a technique based on inkjet printing and wet transfer was developed to reliably deposit graphene-based MSCs on a number of substrates, including flat, 3D, porous, plastics and biological (plants and fruits) with adverse surfaces.

    The contributions of this thesis have the potential to boost the use of inkjet printed MSCs in applications requiring scalability and resolution (e.g. on-chip integration) as well as applications requiring conformability and versatility (e.g. wearable electronics).