The electrochemical behavior of arrays of Au nanoparticles assembled on Au electrodes modified by 11-mercaptoundecanoic acid (MUA) and poly-L-lysine (PLYS) was investigated as a function of the particle number density. The self-assembled MUA and PLYS layers formed compact ultrathin films with a low density of defects as examined by scanning tunneling microscopy. The electrostatic adsorption of Au particles of 19 +/- 3 nm on the PLYS layer resulted in randomly distributed arrays in which the particle number density is controlled by the adsorption time. In the absence of the nanoparticles, the dynamics of electron transfer involving the hexacynoferrate redox couple is strongly hindered by the self-assembled film. This effect is primarily associated with a decrease in the electron tunneling probability as the redox couple cannot permeate through the MUA monolayer at the electrode surface. Adsorption of the Au nanoparticles dramatically affects the electron-transfer dynamics even at low particle number density. Cyclic voltammetry and impedance spectroscopy were interpreted in terms of classical models developed for partially blocked surfaces. The analysis shows that the electron transfer across a single particle exhibits the same phenomenological rate constant of electron transfer as for a clean Au surface. The apparent unhindered electron exchange between the nanoparticles and the electrode surface is discussed in terms of established models for electron tunneling across metal-insulator-metal junctions.

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Human skin consists of several layers with distinct dielectric properties. Physiological processes leading to changes in dielectric properties of the specific layers can potentially be non-invasively monitored employing dielectric spectroscopy. So far no comprehensive skin and underlying tissue model is available for this purpose in the frequency range between 1 and 100 MHz. Focusing on this dispersion-dominated frequency region, different multilayer skin models are investigated. First, with sublayers obtained from two-phase mixtures, second, three-phase mixtures of shelled cell-like ellipsoids and finally, multiphase mixtures obtained from numerical models of single cells generated using a flexible surface parametrization method. All models are numerically evaluated using the finite-element method and a fringing field sensor on the top of the multilayer system serving as a probe. Furthermore, measurements with the sensor probing skin in vivo were performed. The validity of the models was tested by removing the uppermost skin layer, the stratum corneum (SC). It was found that only a three-phase mixture (extracellular medium, cell membrane and cytoplasm) at least can qualitatively reproduce the measured dispersion still occurring without the SC if the model is set up without a priori knowledge of the dispersive behaviour as e.g. a Cole–Cole fit to measured data. Consequently, microstructural features of tissue have to be part of any accurate skin model in the MHz region.

An efficient and versatile numerical method for the generation of different realistically shaped biological cells is developed. This framework is used to calculate the dielectric spectra of materials containing specific types of biological cells. For the generation of the numerical models of the cells a flexible parametrization method based on the so-called superformula is applied including the option of obtaining non-axisymmetric shapes such as box-shaped cells and even shapes corresponding to echinocytes. The dielectric spectra of effective media containing various cell morphologies are calculated focusing on the dependence of the spectral features on the cell shape. The numerical method is validated by comparing a model of spherical inclusions at a low volume fraction with the analytical solution obtained by the Maxwell–Garnett mixing formula, resulting in good agreement. Our simulation data for different cell shapes suggest that around 1MHz the effective dielectric properties of different cell shapes at different volume fractions significantly deviate from the spherical case. The most pronounced change exhibits εeff between 0.1 and 1 MHz with a deviation of up to 35% for a box-shaped cell and 15% for an echinocyte compared with the sphere at a volume fraction of 0.4. This hampers the unique interpretation of changes in cellular features measured by dielectric spectroscopy when simplified material models are used.

The sensitivity and specificity of dielectric spectroscopy for the detection of dielectric changes inside a multi-layered structure is investigated. We focus on providing a base for sensing physiological changes in the human skin, i.e. in the epidermal and dermal layers. The correlation between changes of the human skin's effective permittivity and changes of dielectric parameters and layer thickness of the epidermal and dermal layers is assessed using numerical simulations. Numerical models include fringing-field probes placed directly on a multi-layer model of the skin. The resulting dielectric spectra in the range from 100 kHz up to 100 MHz for different layer parameters and sensor geometries are used for a sensitivity and specificity analysis of this multi-layer system. First, employing a coaxial probe, a sensitivity analysis is performed for specific variations of the parameters of the epidermal and dermal layers. Second, the specificity of this system is analysed based on the roots and corresponding sign changes of the computed dielectric spectra and their first and second derivatives. The transferability of the derived results is shown by a comparison of the dielectric spectra of a coplanar probe and a scaled coaxial probe. Additionally, a comparison of the sensitivity of a coaxial probe and an interdigitated probe as a function of electrode distance is performed. It is found that the sensitivity for detecting changes of dielectric properties in the epidermal and dermal layers strongly depends on frequency. Based on an analysis of the dielectric spectra, changes in the effective dielectric parameters can theoretically be uniquely assigned to specific changes in permittivity and conductivity. However, in practice, measurement uncertainties may degrade the performance of the system.