PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

TL;DR

PyOECP is an open-source Python library that accurately estimates and models complex permittivity from OECP measurements using advanced models and MCMC regression, aiding material analysis.

Contribution

The software introduces flexible modeling of dielectric spectra, including continuous relaxation and artifact incorporation, with validation on synthetic and real data.

Findings

Effective modeling of dielectric spectra using MCMC regression.

Inclusion of electrode polarization effects in modeling.

Validated accuracy with synthetic and empirical data.

Abstract

We present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on two different models, including the capacitance model and the antenna model. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are only required so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, the electrode polarization, which is a common measurement artifact for interpreting the dielectric spectra, can be incorporated in the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem) is also included. This open-source software enables users to freely adjust the physical parameters so that they can obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, validates the software functionality based on both synthetic and empirical data included in the software.