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AbstractThe surface oxygen exchange on oxides with high oxygen ion mobility is modelled with a two step reaction with adsorbed mono-atomic oxygen species as intermediate. Interpretation of the Po2 dependence of the exchange rates, following from this model, indicates that these adsorbed oxygen atoms are singly charged.For the stabilized δ-Bi2O3 solid electrolyte a good agreement has been found between the isotope exchange model and the electrochemical study of the oxygen exchange using gold electrodes. For the mixed La-Sr cobaltite perovskite a change in the surface exchange reaction is observed going from room temperature to 600°C. Indications are that above 450°C the bulk exchange is rate limiting with a (Po2)−1 dependence while below this temperature it is assumed that the dissociative adsorption is rate limiting with a (Po2)1/2 dependence.

AbstractThe surface oxygen exchange on oxides with high oxygen ion mobility is modelled with a two step reaction with adsorbed mono-atomic oxygen species as intermediate. Interpretation of the Po2 dependence of the exchange rates, following from this model, indicates that these adsorbed oxygen atoms are singly charged.For the stabilized δ-Bi2O3 solid electrolyte a good agreement has been found between the isotope exchange model and the electrochemical study of the oxygen exchange using gold electrodes. For the mixed La-Sr cobaltite perovskite a change in the surface exchange reaction is observed going from room temperature to 600°C. Indications are that above 450°C the bulk exchange is rate limiting with a (Po2)−1 dependence while below this temperature it is assumed that the dissociative adsorption is rate limiting with a (Po2)1/2 dependence.

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.

Plant-soil feedback (PSF) describes the process whereby plant species modify the soil environment, which subsequently impacts the growth of the same or another plant species. Our aim was to explore PSF by two maize varieties (a landrace and a hybrid variety) and three arbuscular mycorrhizal fungi (AMF) species (Funneliformis mosseae, Claroideoglomus etunicatum, Gigaspora margarita, and the mixture). We carried out a pot experiment with a conditioning and a feedback phase to determine PSF with different species of AMF and with a non-mycorrhizal control. Sterilized soil was conditioned separately by each variety, with or without AMF; in the feedback phase, each soil community was used to grow each in its "home" soil and in the "away" soil. Plant performance was assessed as shoot biomass, phosphorus (P) concentration and P content, and fungal performance was assessed as mycorrhizal colonization and hyphal length density. Both maize varieties were differentially influenced by AMF in the conditioning phase. In the feedback phase, PSF was generally negative for non-mycorrhizal plants or when plants were colonized by G. margarita, whereas PSF was positive in the other three AMF treatments. When plants were grown on home soil, hyphal length density was larger than on away soil. We conclude that different maize varieties can strengthen positive plant-soil feedback for themselves through beneficial mutualists for themselves, but not across the maize varieties.

Plant-soil feedback (PSF) describes the process whereby plant species modify the soil environment, which subsequently impacts the growth of the same or another plant species. Our aim was to explore PSF by two maize varieties (a landrace and a hybrid variety) and three arbuscular mycorrhizal fungi (AMF) species (Funneliformis mosseae, Claroideoglomus etunicatum, Gigaspora margarita, and the mixture). We carried out a pot experiment with a conditioning and a feedback phase to determine PSF with different species of AMF and with a non-mycorrhizal control. Sterilized soil was conditioned separately by each variety, with or without AMF; in the feedback phase, each soil community was used to grow each in its "home" soil and in the "away" soil. Plant performance was assessed as shoot biomass, phosphorus (P) concentration and P content, and fungal performance was assessed as mycorrhizal colonization and hyphal length density. Both maize varieties were differentially influenced by AMF in the conditioning phase. In the feedback phase, PSF was generally negative for non-mycorrhizal plants or when plants were colonized by G. margarita, whereas PSF was positive in the other three AMF treatments. When plants were grown on home soil, hyphal length density was larger than on away soil. We conclude that different maize varieties can strengthen positive plant-soil feedback for themselves through beneficial mutualists for themselves, but not across the maize varieties.