The differential capacitance of electric double-layer capacitors is studied by developing a generalized model of the self-consistent Gaussian field theory. This model includes many-body effects of particles near the interface such as ionic sizes, the order of water alignment and electrostatic correlations, and thus can present more accurate predictions of the electric double-layer structure and hence the capacitance than traditional continuum theories. Analytical simplification of the model and efficient numerical method are introduced, in particular, the approximation of the self-Green's function which describes the self energy of a mobile ion. We show that, when the applied voltage on interfaces is small the dielectric effect of the electrode materials plays an important role. For large voltage, this effect is screened, but the dielectric saturation due to the alignment of the nearby water is shown to be essential. For 2:1 electrolytes, abnormal enhancement on the capacitance due to the dielectric electrode is observed, which is due to the interplay of the image charge effect and Born solvation energy in the self energy of ions.

Mechanical property measurement protocols have their origins in metallurgy as well as in mechanical and civil engineering—metals being the first materials used on a broad industrial scale. Recent decades have evidenced growing interest in applying these protocols to biological materials or materials mimicking or replacing biological tissue. However, the mechanical properties of biological materials have been found to be highly variable and seemingly hard to capture by traditional protocols. A slow, emerging thought is that perhaps the mechanical theories underlying the testing protocols emanating from the metals field might not be fully applicable to the highly complex, hierarchically organized biological materials and might need further development. The articles in this issue highlight different and complementary, yet also interdependent, approaches to the challenge of extending theoretical and applied mechanics to the level needed for satisfying and reliably capturing the properties of biological materials. This issue also encompasses corresponding, far-reaching consequences for measurement techniques and evaluation protocols aimed at the determination of mechanical protocols.

Amnesia in its various forms is characterized by defects in one or more components of a complex system. Implantation of short-term memory occurs in the hippocampus, while long-term memory is essentially located in the neocortex; these regions are interconnected through complex synaptic structures. In the hippocampus, physiological data show that, as predicted by Hebb, excitatory synapses between nearby excitatory cells become strengthened by simultaneous activation. In contrast with this local process, the preponderance of clinical and experimental evidence indicates that cortical recall of a “memory” is the reconstruction of fragments stored in different synaptically distant brain regions. A mathematical model of memory must reconcile this apparent contradiction as well as explain how many different memories and “ideas” can be assembled within a given anatomical area. Continuum theory, which treats an ensemble of “cell assemblies” or neural networks, offers a step in this direction. Linear analysis using this approach shows that it is the nature of the neural continuum to generate activity waves of wavelength greater than synaptic connection ranges. These waves grow under certain circumstances, and their wavelength is controlled by the synaptic parameters. Both hippocampal and cortical tissue are subject to such wave growth. In the hippocampus, the local Hebbian strengthening controls the global wave growth, making the difference between wave decay and growth. The cortical wave structure can become very complex, so that reproducible memory recall as well as “creative thought” can be accommodated in the theory. Deficits in the functioning of the system may also be evaluated potentially by means of “goodness-of-fit” of the clinical and spatially resolved data with the model. (JINS, 2000, 6, 593–607.)