In-situ FT-IR spectroscopic studies of fuel cell electro-catalysis: From single-crystal to nanoparticle surfaces

The work presented in this article shows the power of the variable temperature, in-situ FT-IR spectroscopy system developed in Newcastle with respect to the investigation of fuel cell electro-catalysis. On the Ru(0001) electrode surface, CO co-adsorbs with the oxygen-containing adlayers to form mixed [CO+(2x2)-O(H)] domains. The electro-oxidation of the Ru(0001) surface leads to the formation of active (1x1)-O(H) domains, and the oxidation of adsorbed CO then takes place at the perimeter of these domains. At 20 degrees C, the adsorbed CO is present as rather compact islands. In contrast, at 60 degrees C, the COads is present as a relatively looser and weaker adlayer. Higher temperature was also found to facilitate the surface diffusion and oxidation of COads. No dissociation or electro-oxidation of methanol was observed at potentials below approximately 950mV; however, the Ru(0001) surface at high anodic potentials was observed to be very active. On both Pt and PtRu nanoparticle surfaces, only one linear bond CO adsorbate was formed from methanol adsorption, and the PtRu surface significantly promoted both methanol dissociative adsorption to CO and its further oxidation to CO2. Increasing temperature from 20 to 60 degrees C significantly facilitates the methanol turnover to CO2.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.