Time Domain Characterization of the Cole-Cole Dielectric Model

Biological dispersion in the time domain using finite element method software

Biological tissue exhibits a strong dielectric dispersion from DC to GHz. Implementing biological dispersion in the time domain with commercial finite element method software could help improve engineering analysis of electrical transient phenomena. This article describes the steps required to implement time-domain biological dispersion with commercial finite element method software. The study begins with the presentation of a genetic algorithm to fit the experimental dispersion curve of Solanum tuberosum (potato tuber) to multipoles of first-order Debye dispersion. The results show that it is possible to represent the biological dispersion of S. tuberosum from 40 Hz to 10 MHz in a 4-pole Debye dispersion. Then, a set of auxiliary differential equations is used to transform the multipole Debye dispersion from the frequency domain to the time domain. The equations are implemented in the commercial software COMSOL Multiphysics. A comparison between the frequency and time domain simulations was used to validate the method. An analysis of the electric current with square-wave pulsed voltage was performed. We found that the computer implementation proposed in this work can describe the biological dispersion and predict the electric current.

Thermoelectric, Electrochemical, & Dielectric Properties of Four ZnO Nanostructures

In this work, we investigated the thermoelectric, electrochemical, and dielectric properties of four different ZnO morphologies, namely nanoribbons, nanorods, nanoparticles, and nanoshuttles. Temperature-dependent Seebeck coefficients were observed using thermoelectric measurements, which confirmed that all synthesized ZnO nanostructures are n-type semiconductors. The Van der Pauw method was applied to measure electrical conductivity, which was also used to calculate the thermal activation energy. Electrochemical properties were analyzed by cyclic voltammetry techniques under five different optical filters. Electrical conductivity of ZnO morphologies showed an increasing trend with increasing temperature. The highest electrical conductivity (1097.60 Ω−1 m−1) and electronic thermal conductivity (1.16×10−4 W/mK) were obtained for ZnO nanorods at 425 K, whereas ZnO nanoshuttles carried the lowest electrical conductivity (1.10 × 10−4 Ω−1 m−1) and electronic thermal conductivity (8.72 × 10−7 W/mK) at 325 K. ZnO nanorods obtained the maximum Power factor value in all temperature ranges. All nanostructures showed electro-catalytic performance with different optical filters. From impedance spectroscopy analysis, ZnO nanorods showed the highest dielectric constant at high frequencies (>1 MHz) at 2.02 ± 0.06, while ZnO nanoshuttles gave the highest dielectric constant at low frequencies (<100 Hz) at 9.69 ± 0.05. These results indicate that ZnO nanorods have the most favorable thermoelectric, electrochemical, and dielectric properties compared to all other ZnO morphologies.

Exploring the relationship between the dielectric properties and viability of human normal hepatic tissues from 10 Hz to 100 MHz based on grey relational analysis and BP neural network

Liver is an important parenchyma organ, and its tissue viability plays an important role in liver transplantation and liver ischemic injury assessment. Dielectric property is a useful biophysical feature that provides insights into the structure and composition of biological tissues. This work aims to establish the relationship between the dielectric properties and viability of human normal hepatic tissues and explore the possibility of evaluating tissue viability by using dielectric properties. First, data on dielectric properties and tissue viability (including cell morphology and enzyme indicators) were collected from human liver tissues at 0.25-24 h after isolation. Grey relational analysis was conducted to select dielectric property and tissue viability indices that were highly correlated with prolonged ex vivo time as the inputs and outputs, respectively, of back-propagation (BP) neural network analysis. Finally, a BP neural network was developed with the Levenberg-Marquardt algorithm to explore the possibility of using dielectric properties as the basis for tissue viability evaluation. Results showed that the mean relative error for prediction was 2.40%, indicating that the model showed potential in forecasting liver tissue viability by applying dielectric properties.

Simple circuit equivalents for the constant phase element

The constant phase element (CPE) is a capacitive element with a frequency-independent negative phase between current and voltage which interpolates between a capacitor and a resistor. It is used extensively to model the complexity of the physics in e.g. the bioimpedance and electrochemistry fields. There is also a similar element with a positive phase angle, and both the capacitive and inductive CPEs are members of the family of fractional circuit elements or fractance. The physical meaning of the CPE is only partially understood and many consider it an idealized circuit element. The goal here is to provide alternative equivalent circuits, which may give rise to better interpretations of the fractance. Both the capacitive and the inductive CPEs can be interpreted in the time-domain, where the impulse and step responses are temporal power laws. Here we show that the current impulse responses of the capacitive CPE is the same as that of a simple time-varying series RL-circuit where the inductor’s value increases linearly with time. Similarly, the voltage response of the inductive CPE corresponds to that of a simple parallel RC circuit where the capacitor’s value increases linearly with time. We use the Micro-Cap circuit simulation program, which can handle time-varying circuits, for independent verification. The simulation corresponds exactly to the expected response from the proposed equivalents within 0.1% error. The realization with time-varying components correlates with known time-varying properties in applications, and may lead to a better understanding of the link between CPE and applications.