Catalyst degradation in membrane electrode assemblies was studied by voltage cycling in hydrogen/air atmosphere. The loss of electrochemically active surface area (ECSA) was quantified by cyclic voltammetry. The influence of cycle duration and dwell time during square wave voltage cycling on catalyst degradation was investigated, as well as the effect of scan rate on ECSA loss during triangular wave voltage cycling. Degradation rates per voltage cycle increased with longer cycle duration and lower scan rate, while degradation rates normalized to operating time were approximately constant over a wide range of cycle lengths and scan rates. The results suggest that the formation of a (sub-)surface oxide layer and cathodic dissolution are important processes in the platinum dissolution mechanism.

Long-term stability of polymer electrolyte membrane fuel cells under dynamic operation still has a high potential for optimization, specifically for use in the automotive industry. This stability is especially affected by the degradation processes taking place in the cathode catalyst layer and hence should be fully understood. In this work, we develop a fast and reliable state-of-health model of the cathode catalyst layer, incorporating the electrochemical degradation processes related to anodic and cathodic platinum dissolution, oxidation, Pt loss due to ion diffusion, carbon corrosion and place exchange mechanisms as well as their interaction. For the purpose of validation, the model is developed alongside a comprehensive experimental data set. A detailed parameter study taking into account temperature, relative humidity and load profile dependency was carried out. A good agreement between model and experiment was found for load ranges between 0.6 and 0.95 V. Further, good approximation of the active surface area loss for cell temperatures between 60°C and 90°C and relative humidity between 30% and 100% were achieved.

Temporal coding is one approach to representing information in spiking neural networks. An example of its application is the location of sounds by barn owls that requires especially precise temporal coding. Dependent upon the azimuthal angle, the arrival times of sound signals are shifted between both ears. In order to determine these interaural time differences, the phase difference of the signals is measured. We implemented this biologically inspired network on a neuromorphic hardware system and demonstrate spike-timing dependent plasticity on an analog, highly accelerated hardware substrate. Our neuromorphic implementation enables the resolution of time differences of less than 50 ns. On-chip Hebbian learning mechanisms select inputs from a pool of neurons which code for the same sound frequency. Hence, noise caused by different synaptic delays across these inputs is reduced. Furthermore, learning compensates for variations on neuronal and synaptic parameters caused by device mismatch intrinsic to the neuromorphic substrate.

We present a model of the cathode catalyst layer morphology before and after loading a porous catalyst support with Pt and ionomer. Support nanopores and catalyst particles within pores and on the support surface are described by size distributions, allowing for qualitative processes during the addition of a material phase to be dependent on the observed pore and particle size. A particular focus is put on the interplay of pore impregnation and blockage due to ionomer loading and the consequences for the Pt/ionomer interface, ionomer film thickness and protonic binding of particles within pores. We used the model to emulate six catalyst/support combinations from literature with different porosity, surface area and pore size distributions of the support as well as varying particle size distributions and ionomer/carbon ratios. Besides providing qualitatively and quantitatively accurate predictions, the model is able to explain why the protonically active catalyst surface area has been reported to not increase monotonically with ionomer addition for some supports, but rather decrease again when the optimum ionomer content is exceeded. The proposed model constitutes a fast translation from manufacturing parameters to catalyst layer morphology which can be incorporated into existing performance and degradation models in a straightforward way.