Effective water management is one of the key strategies for improving low temperature Proton Exchange Membrane (PEM) fuel cell performance and durability. Phenomena such as membrane dehydration, catalyst layer flooding, mass transport and fluid flow regimes can be affected by the interaction, distribution and movement of water in flow plate channels.In this paper a literature review is completed in relation to PEM fuel cell water flooding. It is clear that droplet formation, movement and interaction with the Gas Diffusion Layer (GDL) have been studied extensively. However slug formation and droplet accumulation in the flow channels has not been analysed in detail. In this study, a Computational Fluid Dynamic (CFD) model and Volume of Fluid (VOF) method is used to simulate water droplet movement and slug formation in PEM fuel cell mini-channels. In addition, water slug visualisation is recorded in ex situ PEM fuel cell mini-channels.Observation and simulation results are discussed with relation to slug formation and the implications to PEM fuel cell performance.

The purpose of the first part of this study was to compare four different temperature measuring methods. The application of these tools for possible temperature monitoring or calibration of monitors of microtubular solid oxide fuel cells (MT-SOFCs) is explored. It was found that a thermographic camera is very useful to visualize the temperature gradient on the outside of a cell, while an electrochemical impedance spectroscopy method was useful for estimating the core temperature of a test cell. A standard thermocouple was also used in combination with the previous two methods. Furthermore, an inexpensive laser guided thermometer was also tested for MT-SOFC temperature measurement. This initial study has opened up a range of questions not only about the effect of the experimental apparatus on the measurement results but also about the radial temperature distribution through a MT-SOFC in a working mode. Both these topics will be further investigated in part II of this study through a computational fluid dynamics study. This should provide additional interesting information about any differences between testing single cells and those within a bundle of cells. The discussed results are expected to be mainly temperature related, which should have direct consequences on power output and optimized gas inlet temperatures.

Micro-tubular solid oxide fuel cells (MT-SOFCs) are a much smaller version of larger tubular SOFCs. They are operational within seconds and allow a higher power density per volume than the larger version. Hence they are a potential technology for automotive, auxiliary and small scale power supply devices. In this study a commercially available computational fluid dynamic (CFD) software program was used to predict a MT-SOFCs performance when located inside a high temperature wind tunnel experimental apparatus. In Part I, experimentally measured temperature profiles were recorded via thermo-graphic analyses and I/V curves. These measurements were used in this study to establish the predictability and validity of the CFD code and furthermore understand the MT-SOFC attributes measured in Part I. A maximum 4% I/V curve deviation and 6 K temperature deviation between the experimentally measured and model predicted results was observed. Thus, the model predicted the MT-SOFCs performance in the experimental environment very accurately. A very critical observation was the current density and temperature profile across the MT-SOFC that was strongly dependent on the distance from the hydrogen/fuel inlet. Not only was the model validated but also a grid and quantitative solution analysis is explicitly shown and discussed. This resulted in the optimum grid density and the indication that a normally undesirable high grid aspect ratio is acceptable for similar MT-SOFC modeling. These initial simulations and grid/solution analysis are the prerequisite before performing a further study including multiple MT-SOFCs within a stack using different fuels is also envisaged.

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

No abstract