The study of platinum alloy films of thickness on the order of 1 nanometre is an important topic. For the development of viable fuel cell catalysts, there is a necessity to reduce the overall platinum content while maintaining high catalytic performance. Such nanoscale structures are also convenient model systems to gain a fundamental understanding of the correlation between surface structure and reactivity. The work presented within this thesis focusses on the development of a method to electrochemically form Pt1-xPbx nanoscale overlayers of controlled thickness, composition and morphology. By utilising partial surface limited redox replacement (SLRR) , Pt1-xPbx alloys of variable thickness and surface composition have been formed. The electrochemical adsorption of hydrogen and carbon monoxide has been studied with respect to these two variables. A pronounced sensitivity of adsorption to small (on the order of 1 atomic percent) modulations of Pb surface composition has been determined. Electrocatalytic reactions of relevance to fuel cells, carbon monoxide oxidation and formic acid oxidation, have also been examined on Pt1-x Pbx overlayers. Changes in activity with respect to surface Pb content have been assessed by a combination of electrochemical and x-ray photo emission spectroscopy (XPS) depth profiling measurements. It has been established that Pb is concentrated towards the near surface of these films, and that electrochemical treatment results in the dissolution of Pb and a concomitant loss of activity. The study extends knowledge by developing a new methodology to form Pt1-xPbx alloys and by systematically studying the effect of film thickness, surface composition and electrochemical treatment on adsorption and reactivity. However, the described methods and analysis could be applied to other bimetallic systems on the nanoscale

Suction caissons have recently been considered as an alternative to monopile foundations for offshore wind turbines and met masts. By their nature, such structures have stringent limit states imposed on their design dictating the first modal frequency and the allowable structural rotations. The aim of this thesis is to assess how cyclic loading will affect the long-term serviceability behaviour of such an offshore structure. The behaviour of such a representative caisson system was assessed through the use of a series of scale model tests conducted in dry sand, replicating a fully drained prototype condition. These tests were designed to record the foundation stiffness, its evolution under cyclic loading, how the system accumulates rotation with loading cycles and the dynamic properties of a caisson system. This was conducted at a number of scales under single-g and multi-g conditions. Considering all of the experimentally obtained data it was possible to analyse and provide a prediction as to the long-term behaviour of such an offshore structure. It was discovered that the foundation stiffness was highly dependent on the strain level and under the application of cyclic loading the stiffness would tend to increase in a logarithmic manner. In addition it was found that when subjected to a cyclic load a caisson system will accumulate rotation in accordance with a power relationship. Finally the dynamic properties were found to closely match pre-existing formulations describing a simple dynamic system. Considering these results it was possible to produce an analytical model to describe the evolution of serviceability of a caisson founded offshore structure. Latterly this model was applied to a series of representative cyclic loading test to examine the validity of the complete model.

No description available.

No description available.

Electrochemical impedance spectroscopy (EIS) is recognized as a powerful tool for characterizing the charge transfer reaction on heterogeneous interfaces, thus has been widely applied in various fields of research and in many applications, such as corrosion, development of various types of batteries and fuel cells. Due to the limitation of linearization inherent to the EIS technique the nonlinear information of an investigated system is automatically abandoned. This work focuses on extending the traditional linear EIS technique into the nonlinear domain, nonlinear EIS (NLEIS), and has demonstrated that the nonlinear information can be utilized for gaining a more comprehensive understanding of a system. In this work, the NLEIS technique is discussed from a basic theoretical point of view, including the fundamental definition of nonlinear “impedance” and experimental issues on which a consensus has yet to be reached within the community. A LabView programmed experimental setup was developed during the project to perform NLEIS measurements and preliminary data analysis, which appeared superior to commercial systems (higher frequency range) and systems developed at other institutes (lower noise level). A qualitative approach for data analysis in NLEIS is discussed and prompted in this work, which is more intuitive and suitable for fast first stage analysis compared to an in-depth quantitative modelling approach. To test the NLEIS methodology developed, a classic redox couple- ferri-ferrocyanide- was examined. Information on nonlinearity and symmetry of the system was directly investigated by measuring harmonic responses up to 5th order and it was demonstrated that in contrast to EIS a full set of kinetic parameters could be directly calculated from a single measurement. In addition, features generic to the methodology were discovered, which are independent of systems under investigation. Finally, the NLEIS technique was used in investigating the oxygen reduction reaction on strontium and iron doped lanthanum cobaltite (LSCF) cathode materials of intermediate-temperature solid oxide fuel cells (IT-SOFCs). The decrease of the level of nonlinearity and the increase of asymmetry indicates a potential mechanism change, with increased operating temperature.

In this thesis, we explore the use of graphene incorporated onto indium tin oxide (G/ITO) as a structural template to modify the orientation of copper phthalocyanine (CuPc) molecules for organic photovoltaic (OPV) device applications. We also investigate the effectiveness of 2,3,5,65 tetrafluoro57,7,8,85tetracyanoquinodimethane (F45TCNQ) as a work function modifier for G/ITO without compromising the templating properties of graphene. Photoemission spectroscopy (PES) is employed to assess the electronic properties at the anode5CuPc interface, while X5ray diffraction (XRD) and near5edge X5ray absorption fine structure (NEXAFS) are used to determine the molecular orientation of CuPc. OPV devices are fabricated to attempt to correlate the observations at the microscopic level with the macroscopic device performance. First, we investigate the electronic properties of CuPc deposited on G/ITO and ITO using PES. While the interaction between CuPc molecules and ITO and G/ITO is similar, the hole injection barrier (HIB) is ~0.9 eV for CuPc/G/ITO as compared to 0.5 eV for CuPc/ITO. Therefore, further modification of G/ITO to reduce the HIB is required. The XRD spectrum of CuPc molecules deposited onto graphene grown on copper foil (G/Cu) verifies that graphene is an effective structural template, causing CuPc molecules to 'lie' on the substrate. NEXAFS data shows that the orientation of CuPc molecules changes from 'standing' on ITO to 'tilted' on G/ITO. Next, the effectiveness of F45TCNQ deposited on ITO and G/ITO as a work function modifier is assessed. A thin layer of F45TCNQ is able to increase the substrate work function to ~5 eV, which is close to the ionization potential of CuPc molecules. This suggests that barrierless extraction of holes from CuPc into F45TCNQ modified ITO or G/ITO may be possible. F45TCNQ molecules are found to be predominantly tilted on G/ITO, suggesting that the templating property of graphene may be propagated through F45TCNQ molecules. CuPc molecules deposited onto F45 TCNQ/G/ITO attain a 'lying' configuration, confirming that the templating property of graphene is preserved despite the inclusion of a layer of F45TCNQ. The HIB is dramatically reduced to ~0.2 eV for CuPc/F45TCNQ/G/ITO, and ~0.1 eV for CuPc/F45TCNQ/ITO. Optical absorption of templated CuPc molecules over the visible range is enhanced by over 40% as compared to the non5templated molecules. Therefore, the structure of F45TCNQ/G/ITO appears to be a potential anode design to improve OPV device performance. Our test cells however do not show an improvement in OPV parameters due to the poor quality of transferred graphene, and the high series resistance in our unoptimized OPV device. Finally, the diffusion of F45TCNQ through a CuPc film is studied using time5of5flight secondary ion mass spectrometry (TOF5SIMS). The F5 depth profiles establish that a higher quantity of F45 TCNQ molecules diffuse into CuPc on the G/ITO sample. This is attributed to the weaker interfacial adhesion between F45TCNQ and graphene, and the crystallinity of the templated CuPc film. The quantity of diffused F45TCNQ in the G/ITO sample is only about 0.2 mol%. At this dopant concentration, the conductivity of the film should increase; thus doping of the whole organic film may be favourable for OPV devices.

Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration.

No description available.

Wind power forecasting is essential for the integration of large amounts of wind power into the electric grid, especially during large rapid changes of wind generation. These changes, known as ramp events, may cause instability in the power grid. Therefore, detailed information of future ramp events could potentially improve the backup allocation process during the Day Ahead (DA) market (12 to 36 hours before the actual operation), allowing the reduction of resources needed, costs and environmental impact. It is well established in the literature that meteorological models are necessary when forecasting more than six hours into the future. Most state-of-the-art forecasting tools use a combination of Numerical Weather Prediction (NWP) forecasts and observations to estimate the power output of a single wind turbine or a whole wind farm. Although NWP systems can model meteorological processes that are related to large changes in wind power, these might be misplaced i.e. in the wrong physical position. A standard way to quantify such errors is by the use of NWP ensembles. However, these are computationally expensive. Here, an alternative is to use spatial fields, which are used to explore different numerical grid points to quantify variability. This strategy can achieve comparable results to typical numerical ensembles, which makes it a potential candidate for ramp characterisation. A major disadvantage of most ramp events studies is that they are based on a binary classification, which specifies a percentage of change in power within a defined time window. This may produce artifacts, as ramp detection tools might miss potential changes due to errors in the forecasts. Moreover, a change just below the threshold could be equally damaging as a change that meets the definition. The novel contribution of this project is the application of computational intelligence techniques for wind power forecasting and ramp event characterisation. To achieve this, two stages are required. In the first stage, Genetic Programming (GP) is used to generate an ensemble of wind power forecasts based on the idea of spatial fields. This in its own is an important contribution as the approach will allow the development of computationally cheap wind speed-to-power conversion models, without making any assumptions of their shape or properties. In the second stage, wind power forecasts are converted into a set of filtered signals in order to study ramp events at different time scales. These signals, when applied to a set of Fuzzy Logic rules, indicate the probabilities of a ramp event happening, avoiding the binary classification, which is another important contribution of this work. The observation data used for this investigation was obtained from a real wind park in Galicia, Spain and some observation points in Illinois, USA. The numerical data was obtained by running locally a Mesoscale model. Experiments showed that the accuracy of wind power forecasts obtained using GP as a downscaling/conversion method are comparable to traditional forecasting tools as it is able to achieve an 87% of accuracy. At the same time the computational effort was significantly reduced. The novel ramp detection approach that is introduced here, is able to outperform a basic binary-based detection algorithm. In addition, the fuzzy rules can provide a probability of other events happening; events that might not meet the crisp definition. Using colour maps, which are easier to interpret by human non-experts, it is possible to show how an event is developing in different time windows. Finally, it is shown how neighbouring points can help modelling events that might not be detected using only the closest point of the grid. Having a detailed characterisation of future ramp events can help grid operators to make more informed decisions on the scheduling of back-up units needed and hence to potentially reduce costs and the environmental impact.

The main objective of this thesis work is to synthesis quantum-confined structures, tailor their properties and investigate their applicability to photovoltaics. In this context, quantum-confined silicon nanocrystals (SiNes) are synthesized and surface engineered to tailor and understand their properties. Also a synthesis method for copper (Cu) oxide nanomaterials is developed with control over band energy diagram and optical properties. Finally these engineered quantum-confined nanostructures are successfully implemented in all-inorganic third generation photovoltaic devices with various device architectures. One of the important finding of this work is the dopant-dependant surface chemistry of doped SiNes and found that their optoelectronic properties and Fermi level are influenced by the different surface chemistries of the SiNCs. Secondly, the functionality of tailored SiNCs and Cu-oxide nanomaterials is demonstrated by fabricating all-inorganic solar cells. Some of these devices result in the highest open circuit voltage all-inorganic solar cells devices based on SiNCs. Devices that utilize SiNCs and CuO NPs were therefore presented for the first time in all-inorganic third generation architectures, which also made used of highly novel atmospheric pressure plasma processes.