| Popis: |
In this work we examine the performance of particle-based models with respect to the Ni-YSZ composite anode system. The conductivity and triple-phase boundary (tpb) of particle-based systems is estimated. The systems considered have mono-dispersed particle size distributions, bi-modal particle size distributions with a YSZ:Ni particle size ratio of 1:0.781, and particle size distributions based on experimental measurements. All three types of systems show qualitative behavioral agreement in terms of conductivity, with clear transition from non-conducting behavior to high conducting behavior over a small transition regime which varied from a nickel phase fraction of .22-.28 for the mono- dispersed cases, 0.19-.0.25 for the bimodal cases, and 0.19-0.30 for the experimentally based cases. Mono-dispersed and simple-polydispersed particle size distribution show very low variation from case to case, with σ/μ ≤ 0.04. Cases based on empirical particle size distribution data demonstrated significantly higher variances which varied over a very large range, 0.3 ≤ σ/μ ≤ 1.1. With respect to the calculations of the TPB length, we find that the same pattern of variance in the measure of the triple-phase boundary length. The TPB length for the mono-dispersed and simple poly-dispersed systems was in the range of 3 × 1012 –4 × 1013 m/m3. For empirical particle size distribution data the TPB length density was in the range of 8×109–2×1011 m/m3. The variance of the TPB length density follows the same pattern as the conductivity measurements with very low variance for the mono-dispersed and simple poly-dispersed systems and much larger variance for the empirically-based systems. We also examine the association between the TPB length and the availability of conducting pathways for the participating particles xv of individual TPBs. The probability of a TPB having a conducting pathway in the gas phase is essentially 100% in all cases. The probability of an individual tpb section having conducting pathways in either of the solid phases is directly related to percolation condition of that phase. We also considered a particle-based composite electrode realization based on a three- dimensional reconstruction of an actual Ni-YSZ composite electrode. For this model we used particles which vary in nominal size from 85–465 nm, with size increments of 42.5 nm. We paid particular attention to the coordination numbers between particles and the distribution of particle size interconnections. We found that homogeneous inter-particle connections were far more common than would occur using a random distribution of particles. In particular we found that for a random collection of particles of similar composition the likelihood Ni-Ni particle connections was between 0.18–0.30. For the reconstruction we found the likelihood of Ni-Ni particle connections to be greater than 0.56 in all cases. Similarly, the distribution of connections between particles, with respect to particle size of the participating particles, deviated from what would be expected using a random distribution of particles. Particles in the range of 85–169 nm showed the highest coordination with particles of the same size. Particles in the range of 211–338 nm have the highest coordination with particles of radius 169 nm with very similar distributions. Particles with radius greater than 338 nm represented only 7.2 × 10−3 % of the particles within the reconstruction, and showed the highest coordination with particles of radius of 211 nm, but the distributions vary widely. In the final chapter, we build a model which can account for mass transfer, hetero- geneous chemistry, surface chemistry, and electrochemistry within a porous electrode. The electric potential is calculated on a particle basis using a network model; gas phase concentrations and surface coverages are calculated with a one-dimensional porous me- dia model. Properties of the porous media are calculated via a TPMC method. TPB electrochemistry is calculated at individual triple phase boundaries within the particle xvi model, based on local gas phase concentrations, surface coverages and particle poten- tials, and then added to the porous media model. Using this tool we are able to calculate the spatial distribution of the Faradaic current within the electrode, and variation in gas phase concentrations within the porous media. |
