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Degradation of solid oxide electrolysis cells (SOECs) is probably the biggest concern in the field of high temperature steam electrolysis (HTSE). Anode supported, YSZ-based microtubular solid oxide fuel cells (SOFC) have been tested in fuel cell mode and also at high voltages (up to 2.8 V) under electrolysis mode. At high steam conversion rates the cell voltage tends to saturate. Our hypothesis is that this effect is caused by the electroreduction of the thin YSZ electrolyte which induces electronic conduction losses. YSZ reduction increases the cathode activity and reduces cathode overpotential. Operation of the cell in severe electrolyte reduction conditions induces irreversible damage at the YSZ electrolyte as observed in SEM experiments by the formation of voids at the grain boundaries of the dense YSZ electrolyte. Evidence of this damage was also given by the increase of the ohmic resistance measured by AC impedance. Signs of electrolyte degradation were also found by both EDX analysis and micro-Raman spectroscopy performed along a transverse-cross section of the cell. The observed oxygen electrode delamination is associated to the high oxygen partial pressures gradients that take place at the electrolyte/oxygen electrode interface. © 2011 Elsevier B.V.

Reversible operation of a microtubular solid oxide fuel cell SOFC with high electrochemical efficiency is reported. These devices can ideally produce hydrogen from electricity and steam solid oxide electrolyser SOE and then use the stored hydrogen to generate electricity and heat SOFC, acting as a storage device for the electrical energy. A fuel-electrode-supported Ni–yttriastabilized zirconia YSZ/YSZ/La0.8Sr0.2 0.98MnO3 cell, 2.4 mm in diameter and 20m of electrolyte thickness, was evaluated in an electrolysis mode as a function of the steam concentration supplied to the Ni/YSZ electrode. A good cell performance was obtained at temperatures as high as 950°C for the electrolysis operation. At 850°C, the cell withstood current densities of −1 A/cm2 at 1.3 V with steam utilization of 16.5%. The production of hydrogen in the electrolyzer was tested by mass spectrometry. Their performance, especially in the SOE mode, is very promising for high temperature electrolysis applications. Voltage–current curves present an S-shaped nonlinear behavior in the electrolysis mode with a tendency to saturate at high current density values. The cell could sustain current densities as high as −6A/cm2 at 1.5V, using 70% H2O/15% H2/15% N2 as a fuel with an area-specific resistance of the cell of 0.26cm2. The origin of this effect is discussed. © 2010 The Electrochemical Society.

The suppression of neuronal responses to a repeated event is a ubiquitous phenomenon in neuroscience. However, the underlying mechanisms remain largely unexplored. The aim of this study was to examine the temporal evolution of experience-dependent changes in connectivity induced by repeated stimuli. We recorded event-related potentials (ERPs) during frequency changes of a repeating tone. Bayesian inversion of dynamic causal models (DCM) of ERPs revealed systematic repetition-dependent changes in both intrinsic and extrinsic connections, within a hierarchical cortical network. Critically, these changes occurred very quickly, over inter-stimulus intervals that implicate short-term synaptic plasticity. Furthermore, intrinsic (within-source) connections showed biphasic changes that were much faster than changes in extrinsic (between-source) connections, which decreased monotonically with repetition. This study shows that auditory perceptual learning is associated with repetition-dependent plasticity in the human brain. It is remarkable that distinct changes in intrinsic and extrinsic connections could be quantified so reliably and non-invasively using EEG.

Atomistic computer simulation has been used to predict the most energetically favourable migration pathways for oxygen ion transport in tetragonal La2NiO4. Both interstitial and vacancy mechanisms have been investigated. All of the vacancy mechanisms studied exhibited lower activation energies than the interstitial process. The lowest energy process allowed migration in the a-b plane with an activation energy of 0.35 eV, migration along the c-axis was predicted to have an activation energy of 0.77 eV and interstitial migration in the a-b plane was found to have an energy barrier of 0.86 eV (in agreement with available experimental data). (C) 2008 Elsevier B.V. All rights reserved.

Dynamic causal modelling (DCM) has been applied recently to event-related responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FB-model was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FB-model supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/MEG data and its ability to model ERPs in a mechanistic fashion.