<p>Smoke production in fire represents a threat because fire smoke reduces visibility and because fire smoke is toxic. One way to reduce the risk of persons being overcome by smoke during evacuation is by setting requirements to the building materials’ ability to contribute to the smoke production in a fire. By regulating the use of building products based on their contribution to the optical smoke production in a fire, the toxicity aspects of the smoke will be covered to a high degree as well.</p><p>In this work prediction models for optical smoke production in the Single Burning Item test (SBI) and in the Room Corner test have been developed. The models are of two kinds; classification models based on multivariate statistical analysis of Cone Calorimeter test results, and dynamic calculation models where empirically developed equations are combined with multivariate statistical classification models. The basic idea behind the dynamic smoke prediction models is that the smoke production rate is closely linked to the heat release rate. Prediction models for heat release rate in the two larger-scale methods were therefore a necessary starting point for the modelling of smoke production. Existing models simulating heat release in the SBI test and in the Room Corner test were modified to suit these needs; and were assessed to have high predictability after the modifications.</p><p>My work comprise the following prediction models:</p><p>•A modified version of the Wickström/Göransson model for prediction of heat release rate in the Room Corner test.</p><p>•A statistical model for predicting time to flashover in the Room Corner test using the concept of FO-categories.</p><p>•A model for predicting smoke production rate in the Room Corner test.</p><p>•A statistical model predicting the level of maximum and average smoke production rate in the Room Corner test.</p><p>•A modified version of the model by Messerschmidt et. al. for prediction of heat release rate in the Single Burning Item test.</p><p>•A model for predicting smoke production rate in the Single Burning Item test.</p><p>•A statistical model predicting the level of SMOGRA and the smoke classification in the Single Burning Item test.</p><p>All models, both for prediction of heat release and smoke production, use results from Cone Calorimeter tests at heat flux level 50 kW/m2 as input data. The empirical basis for the models is test data from a total of 65 different products. 32 of the products are tested both in the SBI test and in the Cone Calorimeter test; 56 are tested both in the Room Corner test and in the Cone Calorimeter test. Data from a total of 194 Cone Calorimeter tests have been analysed.</p><p>Both the statistical classification models and the dynamic calculation models can easily be implemented in a PC worksheet, and the prediction results are readily achieved.</p><p>The models’ predictability has been evaluated by comparing the predicted results to results from “real“ larger-scale tests, and by comparing predicted classification to the classification actually obtained. The actual and predicted classifications have been calculated according to the new European system for classification of building products based on reaction to fire test results, and according to the existing classification system based on the EUREFIC-programme.</p><p>The results show that both heat release and smoke production are possible to predict with these models. The predictions of the Single Burning Item test results are more precise than the Room Corner test results, this is probably because the ventilation conditions in the Cone Calorimeter test are more similar to the Single Burning Item test than to the conditions in the Room Corner test. The large-scale fire behaviour is found difficult to predict for some types of products where the fire behaviour depends on certain mechanical or chemical changes during the fire exposure. Such events are obviously not easily predicted from small-scale tests in the Cone Calorimeter, and will need more detailed modelling.</p><p>This thesis presents a generic method of designing prediction models where test results from small-scale methods are used to predict fire behaviour in larger scale. The main feature of this kind of models is the integration of multivariate statistical models in the calculations. Statistical information makes it possible to discriminate between different kinds of products and fire behaviour, and thereby to choose calculation algorithms specially designed for different product groups. Products with high flamespread ability, products with low heat release, products with high smoke production and wood-based products are examples of product types that require special treatment in the modelling of fire behaviour in the Room Corner test and in the SBI test. Modelling of other large-scale test methods may need the option of discriminating between other kinds of groups, based on e.g. product type, geometrical considerations etc.</p>

Smoke production in fire represents a threat because fire smoke reduces visibility and because fire smoke is toxic. One way to reduce the risk of persons being overcome by smoke during evacuation is by setting requirements to the building materials’ ability to contribute to the smoke production in a fire. By regulating the use of building products based on their contribution to the optical smoke production in a fire, the toxicity aspects of the smoke will be covered to a high degree as well. In this work prediction models for optical smoke production in the Single Burning Item test (SBI) and in the Room Corner test have been developed. The models are of two kinds; classification models based on multivariate statistical analysis of Cone Calorimeter test results, and dynamic calculation models where empirically developed equations are combined with multivariate statistical classification models. The basic idea behind the dynamic smoke prediction models is that the smoke production rate is closely linked to the heat release rate. Prediction models for heat release rate in the two larger-scale methods were therefore a necessary starting point for the modelling of smoke production. Existing models simulating heat release in the SBI test and in the Room Corner test were modified to suit these needs; and were assessed to have high predictability after the modifications. My work comprise the following prediction models: •A modified version of the Wickström/Göransson model for prediction of heat release rate in the Room Corner test. •A statistical model for predicting time to flashover in the Room Corner test using the concept of FO-categories. •A model for predicting smoke production rate in the Room Corner test. •A statistical model predicting the level of maximum and average smoke production rate in the Room Corner test. •A modified version of the model by Messerschmidt et. al. for prediction of heat release rate in the Single Burning Item test. •A model for predicting smoke production rate in the Single Burning Item test. •A statistical model predicting the level of SMOGRA and the smoke classification in the Single Burning Item test. All models, both for prediction of heat release and smoke production, use results from Cone Calorimeter tests at heat flux level 50 kW/m2 as input data. The empirical basis for the models is test data from a total of 65 different products. 32 of the products are tested both in the SBI test and in the Cone Calorimeter test; 56 are tested both in the Room Corner test and in the Cone Calorimeter test. Data from a total of 194 Cone Calorimeter tests have been analysed. Both the statistical classification models and the dynamic calculation models can easily be implemented in a PC worksheet, and the prediction results are readily achieved. The models’ predictability has been evaluated by comparing the predicted results to results from “real“ larger-scale tests, and by comparing predicted classification to the classification actually obtained. The actual and predicted classifications have been calculated according to the new European system for classification of building products based on reaction to fire test results, and according to the existing classification system based on the EUREFIC-programme. The results show that both heat release and smoke production are possible to predict with these models. The predictions of the Single Burning Item test results are more precise than the Room Corner test results, this is probably because the ventilation conditions in the Cone Calorimeter test are more similar to the Single Burning Item test than to the conditions in the Room Corner test. The large-scale fire behaviour is found difficult to predict for some types of products where the fire behaviour depends on certain mechanical or chemical changes during the fire exposure. Such events are obviously not easily predicted from small-scale tests in the Cone Calorimeter, and will need more detailed modelling. This thesis presents a generic method of designing prediction models where test results from small-scale methods are used to predict fire behaviour in larger scale. The main feature of this kind of models is the integration of multivariate statistical models in the calculations. Statistical information makes it possible to discriminate between different kinds of products and fire behaviour, and thereby to choose calculation algorithms specially designed for different product groups. Products with high flamespread ability, products with low heat release, products with high smoke production and wood-based products are examples of product types that require special treatment in the modelling of fire behaviour in the Room Corner test and in the SBI test. Modelling of other large-scale test methods may need the option of discriminating between other kinds of groups, based on e.g. product type, geometrical considerations etc.

<p>Micro Electro Mechanical Systems (MEMS) produced to date include IR detectors, accelerometers, pressure sensors, micro lenses, actuators, chemical sensors, gear drives, RF devices, optical processor chips, micro robots and devices for biomedical analysis. The track for tomorrow has already been set and products like 3D TV, physician on a chip, lab on a chip, micro aircraft and food safety sensors will be developed when the technology matures and the market is ready.</p><p>Todays MEMS fabrication is typically based around a silicon substrate and borrow batch fabrication processes from the IC industry. Many of the developed MEMS products have never left a laboratory environment because they are fragile in the macro environment. The way to deal with this is to provide proper packaging so that they can be handled. This poses one of the major challenges in the MEMS industry. Not many packaging techniques have been commercially developed for MEMS and companies that have overcome the packaging problems very seldom reveal their packaging techniques. Functional problems that could be associated with a MEMS structure are often amplified by the package. The reason for this is often associated with packaging stress. Packaging stress related problems is what has kept many promising products from emerging on the market. Even the commercially available pressure sensors and accelerometers have packaging stress problems, but most of them have been overcome. A first step towards solving these challenges is to localise, quantify and understand the critical packaging stresses that act in a packaged MEMS device.</p><p>The goal of this work was to understand how packaging stresses act in a plastic moulded MEMS chip. The work has been threefold; simulation of transfer moulding, static stress analysis of the plastic capsule after moulding and modelling of the piezo-resistive behaviour of a MEMS pressure sensor.</p><p>This dissertation is divided into 9 chapters. Chapter 1 introduces the concept of level-0 and level-1 packaging and looks at different techniques of obtaining the different packaging levels. It introduces the Small Outline Package (SOP) which is the package that has been simulated in this dissertation.</p><p>Chapter 2 gives the background in the theory that has been used to complete this work. It starts by discussing the chemistry and mechanics of thermosetting polymers. Then the rheological behaviour of Epoxy Moulding Compounds (EMC) in a transfer moulding process is discussed. </p><p>The experimental results from the thermomechanical material characterisation of the EMC are presented in Chapter 3. The material was found to have a Tg of 130^oC and coefficient of linear expansion of /oC and /oC below and above Tg respectively. It was further found that the material showed linear viscoelastic behaviour. Stress relaxation tests were run to obtain the relaxation coefficients needed for accurate modelling. The material was found to behave in a thermo rheologically simple manner and the WLF shift function was used to describe the time-temperature superposition principle.</p><p>Chapter 4 addresses the applicability of the plastic processing simulation code, C-Mold, for simulations of MEMS packaging in a SOP. It was found that the 2.5D simulation technique used by the software was inadequate for simulating the polymer filling of the SOP in question. This conclusion was drawn because 3D flow effect were observed in the moulding cavities. The cause for the 3D flow effect was the height of the SOP which was relatively large in order to accommodate for the MEMS device. However, the software proved to be very useful for balancing the runner system.</p><p>Chapter 5 starts with the development of a novel method for calculating the accurate piezoresistance for implanted silicon piezo-resistors. The method let each finite element in a piezoresistor region represent one resistor in a resistor network. The total resistance was then found by simple resistor summation. This method was then utilized on a silicon diaphragm pressure sensor, which had four piezo-resistors implanted into the top surface. The resistors on the diaphragm formed a Wheatstone bridge and the change in piezo-resistance, as a result of applied pressure and hence change in the stress field, was transformed into an electrical signal by proper post processing. The model was built from the design specifications of a commercially manufactured die. The results were compared to the production measurements and matched the data within one standard deviation. It was found that the level-0 package had an effect on the output signal. This work is believed to be the first to report an estimation of the distortion effect that a level-0 package has on a sensor signal with temperature.</p><p>Chapter 6 presents the model of the complete MEMS pressure sensor component encapsulated by EMC in a SOP. The EMC was treated as being elastic and temperature dependent. The method that was developed and calibrated in Chapter 5 was used as an indirect measure of the accuracy of the FEM model. It was evident that the package had a profound effect on the sensor signal. This was consistent with the actual measured data. The match of the signal data was not satisfactory. The signal values for two of the four service temperatures lay outside 3 standard deviations of the experimentally measured results. The estimated sensitivity of the die also fell outside 3 standard deviations for three of the four service temperatures.</p><p>A special vector plot was developed to understand how the pressure, or packaging stress, from the EMC effected the signal and sensitivity of the sensor die. The numerical simulations were done assuming a stress free temperature of 175^oC, the moulding temperature. The packaging stress was found to increase with decreasing temperature. This was the effect of the subsequent increase in ΔT as the service temperature decreased.</p><p>The signal at zero pressure was found to shift as a function of temperature. This was caused by the packaging stress and a corresponding stress-field-shift on the diaphragm. The origin for this shift was an uneven packaging stress between the front and the back side of the sensor die. At -7^oC, the pressure on the front and the back was 30 and 20MPa respectively. This caused an uneven bending moment on the membrane long sides and resulted in a shift in the stress field.</p><p>Chapter 7 elaborated the model one step further by treating the EMC as a viscoelastic material. The result of using the viscoelastic material model showed a reduction in the packaging stress due to stress relaxation. Viscoelastic materials are temperature and strain-history dependent. It was therefore necessary to run the model through the same processes posed by the manufacturing of the MEMS and SOPs. These included a set of thermocycles between -40^oC and 125^oC before the signals as a function of temperature and pressure were taken. The thermocycles were found to have a positive effect on signal shifting. Less signal distortion was seen with more cycles. The estimated and measured signal- vs. temperature-values matched within two standard deviations. The estimated sensitivities did not match the experimental measurements any better than those obtained for the elastic case. It was also found that sensitivity was nearly independent on packaging stress, but significantly dependent on pressure loading conditions.</p><p>The use of the viscoelastic model gave an improvement in simulated signal accuracy over the elastic model. It became clear that the EMC had to be treated as a viscoelastic material.</p><p>Chapter 8 concerned the change in material properties of the EMC and the impact this had on the FEM results. It was found that the behaviour of the MEMS pressure sensor was greatly affected by such changes.</p><p>Chapter 9 present the concluding remarks of this study.</p>

Micro Electro Mechanical Systems (MEMS) produced to date include IR detectors, accelerometers, pressure sensors, micro lenses, actuators, chemical sensors, gear drives, RF devices, optical processor chips, micro robots and devices for biomedical analysis. The track for tomorrow has already been set and products like 3D TV, physician on a chip, lab on a chip, micro aircraft and food safety sensors will be developed when the technology matures and the market is ready. Todays MEMS fabrication is typically based around a silicon substrate and borrow batch fabrication processes from the IC industry. Many of the developed MEMS products have never left a laboratory environment because they are fragile in the macro environment. The way to deal with this is to provide proper packaging so that they can be handled. This poses one of the major challenges in the MEMS industry. Not many packaging techniques have been commercially developed for MEMS and companies that have overcome the packaging problems very seldom reveal their packaging techniques. Functional problems that could be associated with a MEMS structure are often amplified by the package. The reason for this is often associated with packaging stress. Packaging stress related problems is what has kept many promising products from emerging on the market. Even the commercially available pressure sensors and accelerometers have packaging stress problems, but most of them have been overcome. A first step towards solving these challenges is to localise, quantify and understand the critical packaging stresses that act in a packaged MEMS device. The goal of this work was to understand how packaging stresses act in a plastic moulded MEMS chip. The work has been threefold; simulation of transfer moulding, static stress analysis of the plastic capsule after moulding and modelling of the piezo-resistive behaviour of a MEMS pressure sensor. This dissertation is divided into 9 chapters. Chapter 1 introduces the concept of level-0 and level-1 packaging and looks at different techniques of obtaining the different packaging levels. It introduces the Small Outline Package (SOP) which is the package that has been simulated in this dissertation. Chapter 2 gives the background in the theory that has been used to complete this work. It starts by discussing the chemistry and mechanics of thermosetting polymers. Then the rheological behaviour of Epoxy Moulding Compounds (EMC) in a transfer moulding process is discussed. The experimental results from the thermomechanical material characterisation of the EMC are presented in Chapter 3. The material was found to have a Tg of 130oC and coefficient of linear expansion of /oC and /oC below and above Tg respectively. It was further found that the material showed linear viscoelastic behaviour. Stress relaxation tests were run to obtain the relaxation coefficients needed for accurate modelling. The material was found to behave in a thermo rheologically simple manner and the WLF shift function was used to describe the time-temperature superposition principle. Chapter 4 addresses the applicability of the plastic processing simulation code, C-Mold, for simulations of MEMS packaging in a SOP. It was found that the 2.5D simulation technique used by the software was inadequate for simulating the polymer filling of the SOP in question. This conclusion was drawn because 3D flow effect were observed in the moulding cavities. The cause for the 3D flow effect was the height of the SOP which was relatively large in order to accommodate for the MEMS device. However, the software proved to be very useful for balancing the runner system. Chapter 5 starts with the development of a novel method for calculating the accurate piezoresistance for implanted silicon piezo-resistors. The method let each finite element in a piezoresistor region represent one resistor in a resistor network. The total resistance was then found by simple resistor summation. This method was then utilized on a silicon diaphragm pressure sensor, which had four piezo-resistors implanted into the top surface. The resistors on the diaphragm formed a Wheatstone bridge and the change in piezo-resistance, as a result of applied pressure and hence change in the stress field, was transformed into an electrical signal by proper post processing. The model was built from the design specifications of a commercially manufactured die. The results were compared to the production measurements and matched the data within one standard deviation. It was found that the level-0 package had an effect on the output signal. This work is believed to be the first to report an estimation of the distortion effect that a level-0 package has on a sensor signal with temperature. Chapter 6 presents the model of the complete MEMS pressure sensor component encapsulated by EMC in a SOP. The EMC was treated as being elastic and temperature dependent. The method that was developed and calibrated in Chapter 5 was used as an indirect measure of the accuracy of the FEM model. It was evident that the package had a profound effect on the sensor signal. This was consistent with the actual measured data. The match of the signal data was not satisfactory. The signal values for two of the four service temperatures lay outside 3 standard deviations of the experimentally measured results. The estimated sensitivity of the die also fell outside 3 standard deviations for three of the four service temperatures. A special vector plot was developed to understand how the pressure, or packaging stress, from the EMC effected the signal and sensitivity of the sensor die. The numerical simulations were done assuming a stress free temperature of 175oC, the moulding temperature. The packaging stress was found to increase with decreasing temperature. This was the effect of the subsequent increase in ΔT as the service temperature decreased. The signal at zero pressure was found to shift as a function of temperature. This was caused by the packaging stress and a corresponding stress-field-shift on the diaphragm. The origin for this shift was an uneven packaging stress between the front and the back side of the sensor die. At -7oC, the pressure on the front and the back was 30 and 20MPa respectively. This caused an uneven bending moment on the membrane long sides and resulted in a shift in the stress field. Chapter 7 elaborated the model one step further by treating the EMC as a viscoelastic material. The result of using the viscoelastic material model showed a reduction in the packaging stress due to stress relaxation. Viscoelastic materials are temperature and strain-history dependent. It was therefore necessary to run the model through the same processes posed by the manufacturing of the MEMS and SOPs. These included a set of thermocycles between -40oC and 125oC before the signals as a function of temperature and pressure were taken. The thermocycles were found to have a positive effect on signal shifting. Less signal distortion was seen with more cycles. The estimated and measured signal- vs. temperature-values matched within two standard deviations. The estimated sensitivities did not match the experimental measurements any better than those obtained for the elastic case. It was also found that sensitivity was nearly independent on packaging stress, but significantly dependent on pressure loading conditions. The use of the viscoelastic model gave an improvement in simulated signal accuracy over the elastic model. It became clear that the EMC had to be treated as a viscoelastic material. Chapter 8 concerned the change in material properties of the EMC and the impact this had on the FEM results. It was found that the behaviour of the MEMS pressure sensor was greatly affected by such changes. Chapter 9 present the concluding remarks of this study.

No description available.

Affordable price, high abundance of magnesium and high densities of hydrogenin the Mg-based hydrides attract interest to these hydrides tailored for hydrogenand energy storage applications. Ternary La-Mg-Ni hydrogen storage alloys withcomposition La3-xMgxNi9 (x = 0.8-1.2) form a new class of the materials for thenegative electrodes in Ni-Metal Hydride (MH) batteries. The electrochemical dischargecapacity of such alloys reaches 400 mAh/g which is 25 % greater than thatof the commercial AB5-type based electrodes, 315 mAh/g. The La3-xMgxNi9alloys crystallize with trigonal PuNi3 type of crystal structure. Magnesium replaceslanthanum to form the hybrid LaNi5 + Laves phase structures and favorablychanges the thermodynamics of the metal-hydrogen interactions allowingimproved performance of the advanced metal hydride battery electrodes.Differences in melting temperatures of lanthanum, nickel and easily evaporatingmagnesium and a complexity of the phase equilibria in the La-Mg-Ni systemcause difficulties in synthesis of the battery electrode alloys with controlled Mgcontent and a desired phase-structural composition.In present work a La2MgNi9 alloy was in focus. Its successful synthesis hasbeen achieved from the alloy melts containing 0-30 % of overstoichiometric Mgas compared to La2MgNi9 by use of Rapid Solidification performed at variousquenching rates, with a copper wheel rotation speed of 3.1, 10.5 and 20.9 m/s.They were analyzed by synchrotron X-ray diffraction (SR XRD) including in situstudies in hydrogen gas performed at Swiss-Norwegian Beam Lines at ESRF,Grenoble, and by Scanning Electron Microscopy (SEM) with electron probemicroanalysis (EPMA). Pressure-Composition-Temperature isotherms, hydrogenabsorption-desorption cycling and measurements of the electrochemical chargedischargeperformances were employed to characterize hydrogenation behaviorsof the studied alloys. These studies showed that the melt spinning of the alloycontaining 30 % weight excess of Mg quenched using wheel spin speed of 400 RPMallowed obtaining the most homogeneous sample with the optimal microstructureand phase-structural composition corresponding to the formation of La2MgNi9with the highest yield.Variations in magnesium content and selection of optimal conditions for the RapidSolidification process provide complementary possibilities in improving propertiesof the studied La-Mg-Ni alloys as hydrogen storage and battery electrodematerials and provide a possibility to upscale production of the battery alloys.This work was performed at Institute for Energy Technology and at Departmentof Materials Science and Engineering, NTNU.

The most active catalyst for oxygen evolution in PEM water electrolysis is ruthenium oxide. Its major drawback as a commercial catalyst is its poor stability. In a mixed oxide with iridium, ruthenium becomes more stable. However, it would be favorable to find a less expensive substitute to iridium. In this work, the dissolution potential and lifetime of mixed oxides containing ruthenium and tantalum are investigated. In order to effectively determine what effects tantalum and particle size have on stability, only a small amount of tantalum is used, and the catalysts are supported by antimony doped tin oxide, ATO. This leads to a very small particle size, and makes it possible to investigate small amounts of catalyst where little new surface is made available during degradation.Catalysts were prepared with the normal polyol method by reducing RuCl3 and TaCl5 in ethylene glycol, EG, before the metal particles were deposited on the ATO support. The catalysts were investigated electrochemically with cyclic and linear voltammetry. Furthermore, the lifetime of four catalysts were determined by chronoamperometry at 1.455V vs. RHE. The compositions and loading of catalyst on the support were determined by energy dispersive x-ray spectroscopy (EDS) and the particle sizes were measured with transmission electron microscopy (TEM).In one synthesis, the reduction time and temperature were increased from 3 hours at 170&amp;#9702;C to 4 hours at 190&amp;#9702;C in order to increase the reduction rate. While this had no effect on the Ta composition, the catalyst got a fraction of amorphous phase not found in any of the other catalysts. The amorphous Ru0.9Ta0.1O2 particles had the largest particle size and the highest stability of the ones investigated. 10wt% water was added to the synthesis of an ATO-RuO2 catalyst in order to increase the particle size, but no significant effect was observed. Larger RuO2 particles and amorphous Ru0.9Ta0.1O2 particles were obtained by collecting them as unsupported catalysts.The addition of tantalum has a negative effect on the catalytic activity. When Ta is present, the dissolution potential of Ru at around 1.45V is slightly increased, but the degradation rate is increased above 1.49V. A large particle size in RuO2 has a significant positive effect on stability.

Alkaline water electrolysis need a catalyst with low overpotential and high current densitiy for oxygen evolution in order to be a commercial viable hydrogen source in the future. Finding and establishing a correlation between electrocatalytic activity and charge carrier density will help towards finding an optimum catalyst for this purpose. Such comparisons have been made using theoretical values for charge carrier density, but the aim of this work is to use charge carrier data from experimental values.Five powders of La&lt;sub&gt;1-x&lt;/sub&gt;Sr&lt;sub&gt;x&lt;/sub&gt;CoO&lt;sub&gt;3&lt;/sub&gt; (with compositions x = 0, 0.25, 0.5, 0.75, 1) were synthesized by solid-state synthesis and sintered to pellets. The pellet surfaces were investigated in alkaline solution (pH = 13) by cyclic voltammetry, polarization and impedance measurements. Polariza- tion curves with Tafel lines and Mott-Schottky plots were established. The powders and pellet surfaces were investigated by XRD, SEM, EDS, AFM and light microscope.The polarization curves revealed a volcanic behavior with an increase in catalytic activity from x = 0 up to x = 0.75 and then decreasing. The charge carrier density increased with increasing strontium doping. The resulting comparison gave figure 34. Surface investigation revealed much porosity. Because of corrosion, the surface area increased with measuring, and finding the real surface area and the roughness proved to be problematic.A volcanic behavior of the charge carrier density and electrocatalytic activity relationship were observed. Finding roughness factor values by measuring double layer capacitance measured by the cyclic voltammetry method and dividing by the nominal capacitance for a flat surface proved to be unsuccessful. Better synthesis and sintering procedures of pellets are needed to increase the density of the samples in order to decrease the roughness and the effect of corrosion.

Optimalization of PLD deposition of YSZ for micr-SOFC electrolyte applications by varying deposition pressure and target-substrate distance.Substrate used was Si-based chips and wafers (large area PLD), and the substrate temperature was held at 600. Dense films were obtained at 20 mTorr.

The main aim of this work was to analyze an impedance model for porous semiconductor electrodes consisting of spherical particles. The model should make it possible to analyze the flatband potential for this type of electrodes. The analysis was conducted by simulating the model in MATLAB&#174;. Cyclic voltammetry and electrochemical impedance spectroscopy was performed on titanium oxide, TiO2 P25, anodized titanium and some iridium tin oxides, Ir(1-x)SnxO2. The aim was to use the experimental data as a reference and compare the simulated data with the experimental results. This could not be done because the recorded data for the oxides were too strongly influenced by the support material. The supports tested in this work were Au, Ti and ITO. The simulations show that the capacitance of the models spherical particle is only weakly dependent on the particles surface potential. This indicates that this one-dimensional version of the model might not be sufficient to analyze the spherical particles. However, another analysis method for investigation of Mott-Schottky behavior for porous electrodes was confirmed by the result for the anodized titanium.